AI-Enabled Generative Design Digital Twin Framework for Net-Zero Building Optimization Across European Climate Zones

Abstract

The construction sector accounts for around 40% of global energy usage and surpasses 36% of carbon emissions, highlighting the urgent need for improved renovation strategies. This research presents an AI-enabled generative design optimization framework that facilitates concurrent multi-objective optimization of architectural design, structural efficiency, and energy performance. The framework employs a 20-variable parametric design space and integrates a hybrid NSGA-III, a reference-point-based many-objective evolutionary algorithm with particle swarm optimization. Machine-learning surrogate models accelerate physics-based simulations by 500–850 times while maintaining prediction accuracy above 95%. The framework is validated through twelve renovation case studies comprising eleven residential and one office building spanning seven European countries, sourced from IEA SHC Task 37 and Passivhaus Institut databases, and calibrated to ASHRAE Guideline 14 standards (CVRMSE ≤ 18.6% across all buildings). The results evidence average reductions of 84.7% in operational energy consumption and enhancements of 20.1% in material efficiency, while consistently attaining a net-zero annual energy balance. Climate conditions significantly influence optimal insulation requirements, with a 37% difference between continental and Mediterranean regions. This study presents a scalable and computationally efficient method for AI-driven renovation design, overcoming the constraints of sequential approaches and facilitating substantial decarbonization of the built environment.

1. Introduction

The construction sector accounts for more than 40% of global energy consumption and 36% of energy-related CO₂ emissions [1]. The European Union’s Green Deal seeks to achieve net-zero greenhouse gas emissions by 2050 to address this challenge [2]. Realizing this objective requires substantial modifications in architectural design practices, emphasizing the decrease in initial operational energy and the reduction in embedded carbon [3]. Recent advancements in artificial intelligence and digital technology have created substantial prospects to revolutionize traditional design processes [4].

Conventional architectural design adheres to a sequential methodology, commencing with form delineation, succeeded by structural analysis and energy performance assessment [5]. This method leads to fragmented optimization, since architects prioritize aesthetics and functionality while obtaining limited quantitative performance input during the conceptual design phase [6]. Structural engineers then assess designs and often identify inefficiencies that require more materials [7]. Energy consultants undertake thermal simulations, frequently revealing performance inadequacies that necessitate improvements to the building exterior or the augmentation of systems [8]. Decisions centered on a singular criterion may undermine other aspects, resulting in suboptimal outcomes and extended design timelines [9]. Despite extensive research on generative design and digital twin technologies, existing studies primarily focus on isolated performance metrics [10]. Research on generative design has mostly focused on energy efficiency and daylighting, neglecting structural integration [11]. Operational management is the primary focus of digital twin applications, as opposed to design optimization [12]. Most research on multi-objective optimization focuses on problems with two objectives. In contrast, situations with three or more conflicting criteria have been studied less frequently [13].

The application of AI-driven generative design within digital twin frameworks to enhance architectural design is largely unexplored [14]. In practice, the use of advanced optimization methods is often limited by computational costs and their ability to be used in different situations [15].

Architectural and engineering practices still face obstacles that hinder their widespread use in industry. These include issues with software compatibility and difficulties in integrating workflows [16].

Despite these advances, three critical gaps remain unaddressed in the literature: (1) no existing framework simultaneously integrates generative design, digital twin simulation, and multi-objective optimization at the architectural design stage; (2) existing multi-objective studies are predominantly validated through simulation without monitored post-renovation data; and (3) the cross-climate applicability of AI-driven optimization frameworks across European building stocks remains insufficiently demonstrated.

This research details the creation and evaluation of an artificial intelligence-based digital twin framework tailored for generative design applications. The primary objective is to enhance the energy efficiency, structural soundness, and architectural merit of net-zero buildings, specifically within diverse European climatic conditions [17]. The framework employs NSGA-III and Particle Swarm Optimization algorithms to navigate intricate design landscapes, considering architectural quality, the efficient use of structural materials, and overall energy performance [18].

A digital twin module, which includes real-time structural analysis and energy modeling, provides continuous data during the optimization process, helping to guide solutions toward the Pareto frontier [19].

Furthermore, using neural network-based surrogate models helps improve computational efficiency and simplifies the process. This reduces optimization times from days to hours, while still maintaining an accuracy of over 95% [20]. The methodology is corroborated by case studies in Mediterranean and continental European climate zones, illustrating climate-responsive optimization methodologies. The main contributions of this research are:

• Development of an integrated AI-enabled generative design framework simultaneously optimizing architectural form, structural efficiency, and energy performance for Net-Zero buildings, addressing limitations in sequential optimization approaches.
• Machine learning surrogate model acceleration achieving 500–850× computational speed-up while maintaining prediction accuracy of R² > 0.95, reducing optimization cycles from 38 h to 4.2 h and enabling practical design space exploration.
• Validation through twelve European building renovation case studies achieving average energy reductions of 84.7% (range 72.3–93.0%), structural material efficiency improvements of 20.1%, and net-zero energy balance feasibility across continental and Mediterranean climate zones.
• Demonstration of climate-responsive optimization strategies emerging from physics-based evaluation, with 37% variation in optimal insulation thickness and distinct passive design priorities between climate zones.

This study addresses these gaps through three research questions: (RQ1) Can a generative design digital twin framework simultaneously optimize architectural form, structural efficiency, and energy performance with higher Pareto front quality than sequential methods? (RQ2) What computational acceleration is achievable through ML surrogate integration while maintaining physics-based accuracy? (RQ3) Do climate zone differences produce quantifiably distinct optimal design strategies when processed through a common optimization framework?

2. Literature Review

The intersection of generative artificial intelligence and digital twin technologies in architectural design is a growing area of study. However, strong frameworks that successfully combine architectural aesthetics, structural integrity, and energy efficiency are still limited [21].

Recent advancements include AI-augmented building information modeling systems, which have shown energy savings exceeding 10% and prediction accuracy above 90% [22], knowledge-driven optimization frameworks that have decreased computational time by 73% [23], and multi-task learning approaches that have reduced energy consumption by up to 31%.

While these studies are valuable, they often focus on individual parts. This highlights the limitations of methods that consider multiple performance goals at the same time.

Table 1. Key Studies on AI-enabled generative design, Digital Twins, and Multi-Objective Optimization for Sustainable Building Design.

No.	Author(s) & Year	Research Focus	Methodology	Key Findings	Limitations
1	Parekh et al. (2024) [24]	Deep reinforcement learning optimization	Deep Deterministic Policy Gradient (DDPG) integrated with BIM	13% improvement in energy efficiency, emissions reduction, and thermal comfort	High computational complexity and extensive training data requirements
2	Ghaemi et al. (2025) [25]	AI and digital twin integration for energy management	Self-learning control systems with autonomous optimization algorithms	Real-time performance optimization and seamless technology integration	Industry adoption barriers and interoperability challenges across platforms
3	Amangeldy et al. (2025) [26]	AI-powered building ecosystems	Hybrid physics-informed neural networks with federated learning	10–35% reduction in HVAC energy demand with high forecasting accuracy	Data fragmentation issues and privacy concerns in distributed systems
4	Amro & Naimi (2025) [27]	Symmetry-based structural optimization	Genetic algorithms combined with symmetry analysis techniques	18–27% material reduction with significant carbon footprint decrease	Applicability limited to symmetrical building geometries
5	Xie et al. (2025) [28]	Machine learning-enhanced form optimization	Random Forest regression with parametric modeling workflows	Prediction accuracy R2 = 0.94, achieving 32% energy savings	Climate-specific results with limited transferability across regions
6	Bi et al. (2025) [29]	Green smart museums using AI and digital twins	Multi-technology integration framework with closed-loop management	37% energy reduction and 42% decrease in carbon emissions	Trade-offs between energy efficiency and occupant comfort experience

summarizes key studies examining AI-enabled generative design, digital twins, and multi-objective optimization for sustainable building design, highlighting recent advances and critical limitations.

2.1. AI-Enabled Generative Design for Building Optimization

Deep reinforcement learning and evolutionary algorithms enhance energy efficiency, reduce emissions, and improve comfort by 13–20% [30]. Surrogate models minimize optimization time while maintaining high accuracy (R² > 0.94) [31]. Nevertheless, scalability across diverse building typologies remains constrained and necessitates retraining [32]. While these advances are notable, existing studies predominantly address isolated performance objectives, with structural integration and concurrent multi-domain optimization remaining largely absent from the literature.

2.2. Digital Twin Applications in Building Design and Operation

Digital twin technologies primarily improve operational efficiency, resulting in HVAC energy reductions of 10–35%. The application of large language models has recently enhanced environmental quality control; however, energy-comfort trade-offs remain [33]. These applications are predominantly focused on post-occupancy operational management rather than design-stage optimization, representing a critical gap that the present study seeks to address.

2.3. Multi-Objective Optimization for Building Performance

Structural optimization results in material savings of 18–27%; however, integrated optimization comprising structure, architecture, and energy efficiency remains limited [34,35]. Multi-objective techniques concurrently address energy, cost, carbon, and comfort [36]. NSGA-III has exhibited strong performance in high-dimensional objective problems [37], while genetic algorithms decrease energy and carbon emissions by up to 28% and 22% [38]. Reinforcement learning improves adaptability by 13–20% [31]. Despite these advances, most studies address two or three objectives, with four-objective problems incorporating architectural quality across diverse European climate zones remaining underrepresented [39,40,41].

2.4. Research Gap and Positioning of the Present Study

Despite these advancements, a comprehensive framework that simultaneously integrates generative AI, digital twin simulation, and multi-objective optimization for Net-Zero buildings across diverse climates remains lacking. Existing studies address isolated disciplinary boundaries: AI-enabled generative design focuses predominantly on energy metrics; digital twin applications target operational rather than design-stage decisions; and multi-objective studies rarely exceed three objectives without architectural quality integration. This framework addresses this tripartite gap within a single calibrated design-stage optimization loop, validated through twelve monitored European renovation case studies spanning two climate zones.

3. Methodological Framework

This section presents the comprehensive methodological framework developed to design, implement, and validate an artificial intelligence-driven digital twin for net-zero building optimization. The framework integrates generative design, physics-based simulation, multi-objective optimization, and machine learning acceleration within a unified computational environment.

A systematic five-stage workflow is adopted:

(1)

Selection and characterization of representative European building renovation case studies.

(2)

Development of an integrated generative design and digital twin simulation environment.

(3)

Formulation of design variables, objective functions, and performance constraints.

(4)

Deployment of machine learning surrogate models for computational acceleration.

(5)

Establishment of multi-level validation protocols to ensure reliability and reproducibility.

The following subsections describe each methodological component in detail, including data sources, computational tools, mathematical formulations, and validation strategies.

3.1. Case Study Selection and Description

To assess the efficacy of the suggested integrated framework, twelve exemplary European building renovation projects were chosen as case studies. The selection criteria emphasized buildings with substantiated post-renovation performance, specifically those supported by monitored energy consumption data, rather than relying solely on simulated projections. The dataset encompasses comprehensive technical documentation, including envelope specifications, HVAC system designs, and validated pre- and post-retrofit performance metrics, all sourced from peer-reviewed publications and official project reports [42,43].

3.1.1. Building Selection Criteria

The case study portfolio comprises eleven residential buildings (91.7%) and one office building (8.3%). This distribution reflects the predominance of deep-energy residential retrofit initiatives across Europe [44], while also highlighting the limited availability of monitored commercial retrofit datasets with detailed technical documentation [45].

Geographically, the case studies span seven European countries—Germany (3), the Netherlands (3), Austria (3), Greece (1), Switzerland (1), Belgium (1), and Ireland (1)—representing both heating-dominated continental climates and mixed heating–cooling Mediterranean climatic conditions [46].

Construction periods range from 1907 to 1980, with approximately 75% of buildings dating to post-war development between the 1950s and 1970s [47]. The selection criteria also required the availability of documented technical specifications and post-renovation performance datasets derived from publicly available research and project reports [48].

3.1.2. Selected Buildings Overview

The detailed building specifications and performance statistics in

Table 2. Complete Case Study Buildings Specifications and Energy Performance Data.

ID	Building Name & Location	Coordinates	Type	GFA (m2)	Floors	Construction Year	Renovation Year	Climate	Before EUI (kWh/m2·Year)	After EUI (kWh/m2·Year)	Reduction (%)
B1	TU Wien Office Vienna, Austria	48.20° N 16.37° E	Office	13,500	11	1970s	2014	CFB	803	56	93.0
B2	Athens Residential Moschato, Greece	37.95° N 23.68° E	Residential	600	6	1960–1980	2023	CSA	264	35	86.7
B3	Vienna Heritage Vienna, Austria	48.21° N 16.28° E	Residential	3050	4	1907	2017	CFB	249	54	78.3
B4	Tevesstrasse Frankfurt, Germany	50.12° N 8.64° E	Residential	3500	5	1950–1960	2006	CFB	225	16	92.9
B5	Hoheloogstrasse Ludwigshafen, Germany	49.48° N 8.43° E	Residential	750	4	1965	2006–2007	CFB	141	16	88.7
B6	De Kroeven Roosendaal, the Netherlands	51.53° N 4.45° E	Residential	16,080	2	Pre-1965	2010–2011	CFB	137	38	72.3
B7	Zug Apartment Zug, Switzerland	47.17° N 8.52° E	Residential	803	6	1946	2009	CFB	226	25	88.9
B8	Camera Obscura Utrecht, the Netherlands	52.09° N 5.12° E	Residential	11,592	4	1963–1967	2018–2020	CFB	225	50	77.8
B9	Brussels Terraces Brussels, Belgium	50.85° N 4.35° E	Residential	5850	2–3	Pre-2010	2010–2016	CFB	200	29	85.5
B10	Irish nZEB Southeast Ireland	N/A	Residential	N/A	1	Pre-1980	2018–2019	CFB	300	<40	86.7
B11	Dieselweg Graz Graz, Austria	47.07° N 15.43° E	Residential	7889	Multi	Various	2008–2010	CFB	142	14	90.1
B12	2ndSKIN Vlaardingen Vlaardingen, the Netherlands	51.91° N 4.34° E	Residential	11,592	4	1952	2017–2018	CFB	300	75	75.0

Notes: GFA = Gross Floor Area; EUI = Energy Use Intensity. Climate classification follows the Köppen–Geiger system (CFB = Temperate oceanic; CSA = Hot-summer Mediterranean), N/A = Not applicable.

were sourced from several peer-reviewed publications and official project documents. The TU Wien Plus-Energy Office building (B1) was recorded as part of the EU Horizon 2020 EXCESS Project [41]. The multi-family residential building (B2) in Athens was evaluated using dynamic modeling methods [42]. The Vienna Heritage Student Housing (B3) exemplifies heritage-compatible passive housing renovations [43]. The German case studies include the Tevesstrasse residential complex (B4) [44], Grempstrasse building (B5) [45], and Hoheloogstrasse complex (B6) [46], as documented by the Passive House Institute.

The De Kroeven social housing [47], Zug apartment building with MINERGIE-P certification [48], Camera Obscuradreef complex with net-zero energy approach [49], Brussels terraced housing benchmark [50], Irish nZEB social housing with post-occupancy evaluation [51], Austrian Dieselweg complex with solar facade integration [52], and the 2ndSKIN pilot project in Vlaardingen [53] represent monitored European deep-renovation initiatives across multiple national retrofit programs.

Table 2 presents the complete specifications of all twelve case study buildings, including the geographic coordinates, building characteristics, energy performance metrics before and after renovation, and primary data sources with corresponding reference numbers [41,42,43,44,45,46,47,48,49,50,51,52,53].

Certain data fields, including the exact coordinates and gross floor area for B10, were not publicly disclosed in source publications to protect occupant privacy. The twelve case studies collectively represent a total gross floor area of approximately 75,200 m², with individual building sizes ranging from 600 m² to 16,080 m² [41,42,43,44,45,46,47,48,49,50,51,52,53]. Post-renovation technical retrofit specifications were compiled for all twelve investigated case study buildings to ensure methodological transparency and reproducibility. The key envelope performance indicators, HVAC system configurations, renewable energy installations, and post-retrofit monitoring durations are summarized in

Table 3. Post-renovation retrofit specifications of the investigated case study buildings.

ID	Wall U-Value (W/m2K)	Roof U-Value (W/m2K)	Window U-Value (W/m2K)	HVAC System	PV Capacity (kWp)	Monitoring Period
B1	0.097	0.065	0.70	District heating + geothermal	328.4	2014–2020 (6 years)
B2	0.15	0.12	0.90	Heat pump + solar	15	2023–2024 (1 year)
B3	0.12	0.10	0.85	MVHR + heating	None	2017–2020 (3 years)
B4	0.10–0.15	0.10	0.70–0.80	Supply air heating	None	2006–2009 (3 years)
B5	0.11	0.10	0.70	Supply air heating	Solar thermal only	2003–2004 (1 year)
B6	0.15	0.11	0.80	Gas boiler + MVHR	None (heat recovery)	2006–2008 (2 years)
B7	0.15	0.15	0.85	Prefab + heat pump	Solar thermal (5 m2/unit)	2010–2011 (1 year)
B8	0.13	0.10	0.80	Gas boiler + solar	Solar thermal (80 m2)	2009–2011 (2 years)
B9	0.15	0.12	0.80	Prefab + heat pump	22	2018–2020 (2 years)
B10	≤0.15	≤0.15	≤0.85	Gas + MVHR	Variable	2016–2019 (4 years)
B11	0.18	0.12	0.90	MVHR + heating	Micro PV	2019–2021 (2 years)
B12	0.12	0.10	0.70	Prefab + heat pump	65	2008–2010 (2 years)

.

All the buildings achieved post-renovation airtightness levels within n50 ≤ 0.6–1.0 h⁻¹, consistent with Passive House or nZEB performance benchmarks. Monitoring periods correspond to measured post-retrofit operational energy datasets used for digital twin calibration and validation. The geographic distribution of the investigated case study buildings across European regions is illustrated in Figure 1, highlighting the cross-national representation and climatic diversity incorporated within the methodological framework.

3.1.3. Climate Characterization

Climate data for all case study locations were obtained from official national meteorological services and subsequently validated against international databases, including ASHRAE International Weather for Energy Calculations (American Society of Heating, Refrigerating and Air-Conditioning Engineers, Atlanta, GA, USA) and the European Commission Photovoltaic Geographical Information System (PVGIS, European Commission, Brussels, Belgium) [54,55,56,57,58].

The comprehensive climate characteristics for all investigated locations are presented in

Table 4. Climate characteristics for case study locations.

Location	Country	Köppen	Mean Temp (°C)	HDD (K·Day/yr)	CDD (K·Day/yr)	GHI (kWh/m2·Year)
Vienna	Austria	CFB	10.4	2856	450	1200
Athens	Greece	CSA	18.5	1050	2200	1680
Frankfurt	Germany	CFB	10.6	3000	380	1100
Ludwigshafen	Germany	CFB	10.8	2900	400	1120
Roosendaal	The Netherlands	CFB	10.5	2800	280	1050
Utrecht	The Netherlands	CFB	10.2	2850	250	1000
Vlaardingen	The Netherlands	CFB	10.3	2820	260	1020
Zug	Switzerland	CFB	9.8	3100	320	1150
Brussels	Belgium	CFB	10.5	2750	300	1000
Graz	Austria	CFB	9.8	3050	380	1180
Southeast Ireland	Ireland	CFB	9.5	3200	180	950

Notes: HDD = Heating Degree Days (base 18 °C); CDD = Cooling Degree Days (based 10 °C); GHI = Global Horizontal Irradiation. All data represent long-term climatic averages for the period 1991–2020.

, including annual average temperatures, heating and cooling degree days, and solar irradiation values essential for building energy simulation and renewable energy system sizing [57,58].

The climate dataset indicates that eleven of the twelve investigated buildings are located within Köppen CFB (temperate oceanic) climate zones, while one case study (Athens, Greece) represents the Köppen CSA (hot-summer Mediterranean) classification. This climatic diversity ensures representation of both heating-dominated and mixed heating–cooling operational conditions within the simulation framework [55,57].

3.2. Research Framework Overview

The proposed framework integrates AI-driven generative design algorithms, physics-based Digital Twin simulations, multi-objective optimization, and machine learning methods to improve architectural form, structural efficiency, and building energy performance [59]. This framework works through the interaction of four main modules: generative design, digital twin simulation, optimization, and surrogate-based performance prediction.

The generative design module uses a parametric approach to change design variables related to architecture, structure, and energy.

These designs are then evaluated within the Digital Twin environment, using physics-based simulations for both structure and energy.

Multi-objective optimization algorithms then find Pareto-optimal solutions, while machine learning surrogate models speed up performance evaluation during iterative design exploration.

The overall architecture of the proposed framework is illustrated in Figure 2, demonstrating the workflow integration between generative design, digital twin simulations, machine learning acceleration, and optimization processes.

As shown in Figure 2, design inputs are processed through generative design, evaluated via digital twin simulations, and iteratively optimized using NSGA-III and PSO algorithms, with surrogate models supporting computational acceleration.

3.3. Design Variables and Parameters

The generative design module systematically explores the multi-dimensional design space by parametrically varying architectural, structural, and energy-related variables within feasible ranges informed by building codes, engineering practice, and practical constructability constraints.

Table 5. Design variables for multi-domain building optimization.

Category	Variable	Range	Unit	Step	Impact
Architectural	Building Height	20–50	m	3.0	Structural loads, energy demand
Architectural	Floor-to-Floor Height	3.0–4.5	m	0.3	Spatial quality, HVAC efficiency
Architectural	Building Orientation	0–360	degrees	15	Solar gains, daylighting
Architectural	Window-to-Wall Ratio	0.20–0.60	-	0.05	Heating/cooling loads
Architectural	Facade Articulation	1–8	-	1	Architectural quality, thermal bridges
Architectural	Shading Device Type	0–5	-	1	Cooling load, daylighting quality
Structural	Column Spacing (X-axis)	4.0–8.0	m	0.5	Structural efficiency, flexibility
Structural	Column Spacing (Y-axis)	4.0–8.0	m	0.5	Structural efficiency
Structural	Column Dimensions	0.30–0.80	m	0.05	Material quantity, embodied carbon
Structural	Beam Dimensions	0.30–0.70	m	0.05	Structural capacity
Structural	Slab Thickness	0.15–0.30	m	0.02	Deflection control, thermal mass
Structural	Concrete Grade	C25–C45	MPa	C5	Structural capacity, embodied carbon
Structural	Foundation Type	1–4	-	1	Geotechnical suitability, cost
Energy	Wall Insulation Thickness	0.08–0.25	m	0.02	Heating/cooling loads
Energy	Roof Insulation Thickness	0.15–0.40	m	0.05	Heat loss, thermal comfort
Energy	Window U-value	0.70–1.40	W/(m2K)	0.10	Heating load, initial cost
Energy	Window G-value	0.35–0.65	-	0.05	Cooling/heating balance
Energy	HVAC System Type	1–6	-	1	Energy efficiency, operational cost
Energy	Heat Recovery Efficiency	0.70–0.90	-	0.05	Ventilation heat loss
Energy	PV Coverage Ratio	0.00–1.00	-	0.10	Renewable generation, Net-Zero

defines all design variables, including their ranges, units, and relationships to building performance.

3.4. Objective Functions

The multi-objective optimization process seeks to identify Pareto-optimal design solutions that represent optimal trade-offs between competing objectives. The optimization formulation includes four primary objectives: minimizing structural material cost, minimizing embodied carbon, minimizing operational energy consumption, and maximizing architectural quality [60,61].

The general multi-objective optimization problem is formulated as [59]:

$$\mathit{Minimize} F\left(x\right)=\left[f_1\left(x\right), f_2\left(x\right), f_3\left(x\right), f_4\left(x\right)\right]$$

(1)

where x represents the design variable vector and f₁, f₂, f₃, f₄ represent the four objective functions. The structural cost objective function is expressed as [60]:

$$f^1\left(x\right)=\mathit{\Sigma }\left(\rho ᵢ\times Vᵢ\times Cᵢ\right)$$

(2)

where $f^1(x)$ represents the structural cost objective function, $\rho$_i represents the density of material $i$(kg/m³), V_i represents the volume of element $i$(m³), C_i represents the unit cost of material $\mathrm{i}$ (€/kg), and $i$ denotes the material or structural element type considered in the optimization process.

The embodied carbon objective function is calculated as [60]:

$$f^2\left(x\right)=\mathit{\Sigma }\left(Mᵢ\times EFᵢ\right)$$

(3)

where $f^2(x)$ represents the embodied carbon objective function, $Mᵢ$ represents the mass of material $\mathrm{i}$ (kg), $EFᵢ$ represents the emission factor of material $\mathrm{i}$ (kgCO₂e/kg), and $\mathrm{i}$ denotes the material type included in the optimization process [60].

The operational energy objective function integrates annual building energy consumption [61]:

$$f^3\left(x\right)=EUI={\displaystyle \frac{E_{\mathrm{annual}}}{A_{\mathrm{floor}}}}$$

(4)

where $f^3(x)$ represents the operational energy objective function, $EUI$ represents Energy Use Intensity (kWh/m²·year), $E_{\mathrm{annual}}$ represents total annual energy consumption (kWh/year), and $A_{\mathrm{floor}}$ represents gross floor area (m²) [61].

3.5. Design Constraints

Structural constraints guarantee that all produced designs meet the safety requirements outlined in Eurocodes [62]. Energy performance constraints mandate netz-zero compliance by ensuring that annual photovoltaic generation meets or surpasses annual building energy consumption. The net-zero energy balance constraint is defined as [63]:

$$E_{PV}\ge E_{annual}$$

(5)

where $E_{PV}$ represents annual photovoltaic electricity generation (kWh/year) and $E_{annual}$ represents total building energy consumption (kWh/year) [63].

3.6. Digital Twin Development

The Digital Twin module enables physics-based performance evaluations for each design alternative generated by the optimization algorithm, encompassing structural analyses and annual energy simulations [64]. Data capturing technologies utilizing building information modeling enable automated information exchange between generative design, structural analysis, and energy simulation environments [65].

To ensure Digital Twin fidelity, the simulation models were calibrated using measured post-retrofit energy performance datasets obtained from monitored case study buildings. Calibration accuracy was evaluated using standard statistical indicators, including the Coefficient of Variation of the Root Mean Square Error (CVRMSE) and Normalized Mean Bias Error (NMBE), ensuring reliable alignment between simulated and measured building performance.

All twelve digital twin models were calibrated against measured post-retrofit energy consumption data sourced from the monitoring periods documented in Table 3. Calibration compliance was verified against ASHRAE Guideline 14 thresholds (CVRMSE ≤ 30%; NMBE ≤ 10%) for all buildings, with baseline validation tolerance of ±15% confirmed, as documented in Table 6.

A closed-loop feedback mechanism was further implemented, whereby calibrated operational performance data continuously informed model refinement and optimization iterations, enabling dynamic updating of simulation parameters and enhancing predictive reliability.

3.6.1. Structural Analysis Component

The structural analysis employed finite element analysis utilizing three-dimensional frame models that included columns, beams, slabs, and foundation elements. Structural analysis was conducted using SAP2000 (version 25) and ETABS (version 21) (Computers and Structures, Inc., Berkeley, CA, USA), industry-standard software for multi-story building analysis and design. Dead loads, live loads, wind loads, and seismic loads were assessed in compliance with Eurocode requirements [62,66,67].

Energy simulation was performed using EnergyPlus (v23.2, U.S. Department of Energy, Washington, DC, USA) with EnergyPlus Weather (EPW) Typical Meteorological Year files. Surrogate models were implemented in Python (v3.9, Python Software Foundation, Wilmington, DE, USA) with TensorFlow (v2.8, Google LLC, Mountain View, CA, USA) and scikit-learn (v1.0, Inria, Paris, France). Parametric geometry generation was performed in Rhinoceros 7 with Grasshopper (Robert McNeel & Associates, Seattle, WA, USA), and multi-objective optimization was implemented using pymoo (v0.6, Karlsruhe Institute of Technology, Karlsruhe, Germany).

Live loads are specified according to the building use category as defined in Eurocode 1, with residential buildings (category A) assigned characteristic imposed loads of 2.0 kN/m², office buildings (category B) requiring 3.0 kN/m² [62]. Embodied carbon for structural materials was calculated using emission factors from the ICE Database (University of Bath, Bath, UK), with concrete emission factors ranging from 0.11 to 0.16 kgCO₂e/kg and reinforcing steel emitting 1.85–2.10 kgCO₂e/kg depending on recycled content [60].

3.6.2. Energy Simulation Component

Annual energy simulation was performed using Energy Plus version 23.2, employing hourly timesteps for 8760 h using Typical Meteorological Year weather data for each case study location [68].

Photovoltaic electricity generation was calculated using:

$$E_{PV}=A_{PV}\times {\eta }_{PV}\times H_{solar}\times PR$$

(6)

where $A_{PV}$ represents the installed $PV$ array area (m²), ${\eta }_{PV}$ represents module efficiency (0.18–0.22),$H_{solar}$ represents annual solar irradiation (kWh/m²·year), and PR represents performance ratio (0.75–0.85) [63].

3.6.3. Integration Layer

BIM integration was accomplished through automated data exchange among Revit 2024 (Autodesk, Inc., San Rafael, CA, USA), structural analysis software, and energy simulation engines. The Autodesk Revit API constructs three-dimensional models from design parameter vectors, automatically adjusting geometric attributes per optimization algorithms. Automated workflows generate structural analysis and energy simulation input files through direct geometry extraction, ensuring consistency between models and eliminating manual data entry errors [65,68]. The integration enables simulation cycle times of 3–5 min per design variant, facilitating rapid design iterations.

3.6.4. Simulation Parameters and Assumptions

Weather data for all case study locations utilized Typical Meteorological Year (TMY3) files from the EnergyPlus (version 23.2, U.S. Department of Energy, Washington, DC, USA), representing 15–30 years of historical climate observations [68]. Occupancy schedules and internal heat gains followed ASHRAE 90.1 (American Society of Heating, Refrigerating and Air-Conditioning Engineers, Atlanta, GA, USA) [69].

Baseline energy performance was established using documented pre-renovation measured consumption data from building energy audits and monitoring reports [41,42,43,44,45,46,47,48,49,50,51,52,53], validated against simulated performance within ±15% in accordance with ASHRAE Guideline 14 [70]. Table 6 summarizes all simulation parameters applied consistently across optimization iterations.

Table 6. Simulation Parameters and Baseline Assumptions.

Parameter Category	Specification	Value/Range
	Weather Data
Source	EnergyPlus TMY3	8760 hourly records [68]
Climate period	Long-term average	1991–2020
	Occupancy—Residential
Density	ASHRAE 90.1 [69]	0.02 persons/m2
Metabolic gain	ISO 7730 standard [71]	75 W/person
Lighting load	ASHRAE 90.1 [69]	5–8 W/m2
Equipment load	ASHRAE 90.1 [69]	3–7 W/m2
Occupancy—Office
Density	ASHRAE 90.1 [69]	0.05–0.10 persons/m2
Schedule	Standard office hours	08:00–18:00 (Weekdays)
Lighting load	ASHRAE 90.1 [69]	9–11 W/m2
Equipment load	ASHRAE 90.1 [69]	10–15 W/m2
	Baseline Validation
Data source	Measured pre-renovation	Audits + utility bills [41,42,43,44,45,46,47,48,49,50,51,52,53]
Calibration tolerance	ASHRAE Guideline 14 [70]	±15%
	Grid Carbon Intensity
Austria	IEA 2023 [72]	0.28 kgCO2e/kWh
Belgium	IEA 2023 [72]	0.32 kgCO2e/kWh
Germany	IEA 2023 [72]	0.40 kgCO2e/kWh
Greece	IEA 2023 [72]	0.55 kgCO2e/kWh
Ireland	IEA 2023 [72]	0.34 kgCO2e/kWh
The Netherlands	IEA 2023 [72]	0.35 kgCO2e/kWh
Switzerland	IEA 2023 [72]	0.12 kgCO2e/kWh
	PV System Parameters
Module efficiency	Equation (6)	0.18–0.22
Performance ratio (PR)	System losses	0.75–0.85
Solar irradiation	Table 4	950–1680 kWh/m2·year
	Net-Zero Calculation
Method	Annual balance	E_PV ≥ E_annual (Equation (5))
Grid interaction	Net-metering	Annual accounting

Table 6. Simulation Parameters and Baseline Assumptions.

Parameter Category	Specification	Value/Range
	Weather Data
Source	EnergyPlus TMY3	8760 hourly records [68]
Climate period	Long-term average	1991–2020
	Occupancy—Residential
Density	ASHRAE 90.1 [69]	0.02 persons/m2
Metabolic gain	ISO 7730 standard [71]	75 W/person
Lighting load	ASHRAE 90.1 [69]	5–8 W/m2
Equipment load	ASHRAE 90.1 [69]	3–7 W/m2
Occupancy—Office
Density	ASHRAE 90.1 [69]	0.05–0.10 persons/m2
Schedule	Standard office hours	08:00–18:00 (Weekdays)
Lighting load	ASHRAE 90.1 [69]	9–11 W/m2
Equipment load	ASHRAE 90.1 [69]	10–15 W/m2
	Baseline Validation
Data source	Measured pre-renovation	Audits + utility bills [41,42,43,44,45,46,47,48,49,50,51,52,53]
Calibration tolerance	ASHRAE Guideline 14 [70]	±15%
	Grid Carbon Intensity
Austria	IEA 2023 [72]	0.28 kgCO2e/kWh
Belgium	IEA 2023 [72]	0.32 kgCO2e/kWh
Germany	IEA 2023 [72]	0.40 kgCO2e/kWh
Greece	IEA 2023 [72]	0.55 kgCO2e/kWh
Ireland	IEA 2023 [72]	0.34 kgCO2e/kWh
The Netherlands	IEA 2023 [72]	0.35 kgCO2e/kWh
Switzerland	IEA 2023 [72]	0.12 kgCO2e/kWh
	PV System Parameters
Module efficiency	Equation (6)	0.18–0.22
Performance ratio (PR)	System losses	0.75–0.85
Solar irradiation	Table 4	950–1680 kWh/m2·year
	Net-Zero Calculation
Method	Annual balance	E_PV ≥ E_annual (Equation (5))
Grid interaction	Net-metering	Annual accounting

Net-zero compliance is achieved when annual on-site photovoltaic generation meets or exceeds total building energy consumption, accounting for seasonal variations in energy demand and renewable production [63].

3.7. Multi-Objective Optimization Algorithms

Two complementary optimization algorithms were implemented to explore the design space and identify Pareto-optimal solutions: the Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Particle Swarm Optimization (PSO). NSGA-III was selected for its superior performance in many-objective problems with more than three objectives, while PSO provides faster convergence for continuous design variables [59,73].

3.7.1. Non-Dominated Sorting Genetic Algorithm III (NSGA-III)

NSGA-III employs reference point-based non-dominated sorting to maintain population diversity across the Pareto front through selection, crossover, and mutation operations. Algorithm parameters included: population size of 100 individuals, 500 generations, simulated binary crossover probability of 0.9, and polynomial mutation probability of 1/n (n = design variables). Convergence criteria comprised: (1) maximum 500 generations, (2) hypervolume improvement <0.1% for 50 consecutive generations, and (3) 48-h computational time limit [59].

3.7.2. Particle Swarm Optimization (PSO)

PSO simulates the social behavior of particle swarms, where each particle represents a potential design solution and adjusts its position based on its own best-known position and the global best position swarm size was set to 50 particles with 500 iterations [73].

3.7.3. Pareto Dominance and Selection

Pareto dominance determines the relationship between design solutions without requiring subjective weighting of objectives. Solution x¹ dominates solution x² if and only if x¹ is no worse than x² in all objectives and strictly better in at least one objective [74]:

$$x^1\hspace{0.25em}\mathrm{dominates}\hspace{0.25em}{x}^2\hspace{0.25em}\mathrm{if:}\hspace{0.25em}\forall \hspace{0.17em}i\mathrm{:}\hspace{0.25em}{f}_i\left(x^1\right)\le f_i\left(x^2\right)\mathrm{and}\exists \hspace{0.17em}j\mathrm{:}\hspace{0.25em}{f}_j\left(x^1\right)<f_j\left(x^2\right)$$

(7)

where $f_i$ represents objective function $i$ for minimization problems. The Pareto front consists of all non-dominated solutions representing optimal trade-offs where improvement in one objective necessitates degradation in at least one other objective [74,75].

3.8. Machine Learning Acceleration

To mitigate the computational intensity associated with full physics-based simulations, which necessitate 3–5 min for each design evaluation, machine learning surrogate models were developed. These models are designed to approximate structural and energy performance, achieving evaluation times of approximately 0.35 s [76].

3.8.1. Surrogate Model Development

Neural network surrogate models were trained to predict structural material quantities, embodied carbon, and energy use intensity based on design parameter inputs. Models were implemented Python (v3.11, Python Software Foundation, Wilmington, DE, USA), TensorFlow (v2.15, Google LLC, USA), and Scikit-learn (v1.4, Inria, France) for machine learning operations. Data processing and statistical analysis were performed using MATLAB R2023b (MathWorks, Inc., Natick, MA, USA) [77]. The surrogate model function is expressed as [78]:

$$\mathrm{ŷ}=M\left(x;\theta \right)$$

(8)

where ŷ represents predicted performance metrics, M represents the neural network model, x represents the input design parameter vector with 20 dimensions, and θ represents trained network weights. Training data generation employed Latin Hypercube Sampling with sample sizes of 500–1000 designs per building type. The neural network architecture consisted of an input layer with 20 neurons, three hidden layers with 64, 32, and 16 neurons using ReLU activation functions, and an output layer with 4 neurons representing the four objective functions [78].

Prior to model training, input features were standardized using z-score normalization to ensure uniform scaling across design variables. Outliers were identified and removed using a ±3σ criterion, and missing values were handled through linear interpolation for gaps not exceeding 72 h in monitored datasets.

Network training employed the Adam optimization algorithm with a learning rate of 0.001 and batch size of 32, using training–validation–testing splits of 70%–15%–15% [79]. The loss function minimized during training was the Mean Squared Error (MSE), widely adopted for regression-based surrogate modeling to quantify prediction deviations between simulated and predicted outputs [78]. Training continued until validation loss plateaued for 50 consecutive epochs, typically requiring 500–1000 epochs for convergence [79].

3.8.2. Model Validation and Performance

Model performance was quantified using standard statistical evaluation metrics, including coefficient of determination (R²), root mean square error (RMSE), and mean absolute percentage error (MAPE).

Target performance thresholds were established as R² > 0.95, RMSE < 6 kWh/(m²·year), and MAPE < 5% to ensure surrogate model reliability. Computational acceleration was evaluated by calculating the ratio between the full physics-based simulation time (3–5 min per design evaluation) and surrogate model inference time (approximately 0.35 s), yielding speed-up factors ranging from 500 to 850 times [80]. Model generalization was evaluated using 5-fold cross-validation applied during training, with the 30% test set (n = 225 designs) held entirely out of the training process. Cross-validation R² variance across folds was below 0.008 for all predicted variables, confirming model stability and reliability.

3.9. Performance Evaluation Metrics

Performance evaluation metrics that are comprehensive in nature were established for the purpose of assessing the quality of optimization results, comparing the performance of algorithms, and validating the effectiveness of the framework [74,81]. The evaluation framework comprises building performance metrics, optimization quality metrics, and computational performance metrics, Life-cycle cost considerations were incorporated in accordance with ISO 15686-5 standards to ensure consistency in long-term economic performance evaluation [82]. Structural performance assessment and validation procedures were aligned with established structural monitoring and reliability guidelines [83].

3.9.1. Building Performance Metrics

Building performance metrics quantify improvements achieved through optimization relative to baseline designs. Material efficiency ratio measures structural material reduction [62]:

$$\mathrm{Material} \mathrm{Efficiency}=\left({\displaystyle \frac{M_{\mathrm{optimized}}}{M_{\mathrm{baseline}}}}\right)\times 100\%$$

(9)

where $M_{\mathrm{optimized}}$ represents optimized structural mass and $M_{\mathrm{baseline}}$ represents baseline structural mass [62]. For example, a baseline building with a structural mass of 2850 kg/m² and an optimized design achieving 2100 kg/m² results in a material efficiency ratio of 73.7%, corresponding to a 26.3% reduction in structural material consumption.

Energy savings percentage quantifies operational energy reduction [61]:

$$\mathrm{Energy} \mathrm{Savings}=\left({\displaystyle \frac{EU{I}_{\mathrm{baseline}}-EU{I}_{\mathrm{optimized}}}{EU{I}_{\mathrm{baseline}}}}\right)\times 100\%$$

(10)

After applying Equation (10), the energy savings percentage can be illustrated through a simple example. If the baseline energy use intensity is 180 kWh/(m²·year) and the EUI optimized is 45 kWh/(m²·year), the resulting energy savings are calculated as ((180 − 45)/180) × 100% = 75.0%, indicating a substantial reduction in operational energy demand. In this context, EUI refers to the Energy Use Intensity measured in kWh/(m²·year) [61]. Embodied carbon reduction measures construction phase environmental impact [60]:

$$CarbonReduction=\left({\displaystyle \frac{E{C}_{baseline}-E{C}_{optimized}}{E{C}_{baseline}}}\right)\times 100\%$$

(11)

where EC represents embodied carbon in kgCO₂e/m² [60]. Net-Zero compliance rate indicates the proportion of designs achieving Net-Zero energy balance [63].

3.9.2. Computational Performance Metrics

Computational efficacy was assessed through convergence rate, execution time, and acceleration metrics. Stable Pareto fronts were generally attained within 300 to 500 generations [59]. Full physics-based optimization necessitated 35–50 h, whereas surrogate-based optimization decreased this to 1–2 h, achieving speed-up factors of 500–850 and facilitating investigation of larger design spaces [77]. The algorithmic efficacy of NSGA-III and PSO was evaluated using hypervolume and IGD metrics, with statistical significance determined through Mann–Whitney U tests [74,75,81].

3.10. Validation Methodology

A thorough five-step validation method was employed to verify the precision at the component level and the reliability at the system level of the integrated framework [74,75,80].

Step 1 (Internal validation): Predictions from the machine-learning surrogate were assessed against comprehensive simulations utilizing a reserved test set of 150 designs for each building type. Designs exhibiting a prediction error exceeding 10% were re-simulated to identify regions of surrogate failure and to enable targeted retraining of the design space [78].

Step 2 (Validation of Convergence): NSGA-III and PSO were executed five times each, employing distinct random samples. Pareto fronts were assessed using hypervolume and inverted generational distance metrics, demonstrating consistent convergence with coefficients of variation below 5%, thus confirming the method’s reproducibility [74,75,81].

Step 3 (Physical Validation): Structural responses were validated by simplified hand calculations for representative configurations, including beam deflections under evenly distributed loads and column axial stresses using tributary area methods [62]. Heating demand forecasts were compared with the ISO 13790 monthly method, yielding variances of ±15% due to alterations in temporal accuracy [61,84].

Step 4 (Validation of Benchmarking): The optimization outcomes were juxtaposed with peer-reviewed studies on Passive House,$nZEB$, and plus-energy edifices. The framework realized savings of 18–27% in structural material usage and 22–35% in energy consumption compared to baseline designs, conforming to or beyond the performance documented in the literature [41,42,43,44,45,46,47,48,49,50,51,52,53].

Step 5 (Expert Validation): Licensed engineers evaluated structural outcomes for compliance with Eurocode, while certified energy experts verified simulated premises. Professional architects assessed architectural quality and constructability, and their feedback was routinely incorporated to improve optimization constraints and scoring functions, resulting in solutions validated by human specialists.

The thorough validation confirms that the framework delivers Eurocode-compliant structural solutions, net-zero-ready energy performance, architecturally acceptable designs, and practical constructability, thereby enhancing confidence in the real-world implementation of the optimized results [74,75,80].

4. Results

4.1. Overview of Framework Validation

The four optimization objectives defined in Equation (1) correspond directly to the performance indicators reported in the following results. Objective f₁ (structural material quantity) is reported as material reduction (%) in

Table 7. Comprehensive Performance Metrics for Case Study Building Portfolio.

Building ID	Type	GFA (m2)	Pre-Renovation (kWh/m2·a)	Post-Renovation (kWh/m2·a)	Reduction (%)	PV Generation (kWh/m2·a)	Net-Zero
B1—Vienna Office	Office	13,500	803	56	93.0	118	Achieved
B2—Athens Residential	Residential	600	264	35	86.7	62	Achieved
B3—Vienna Heritage	Heritage Res.	3050	249	54	78.3	68	Achieved
B4—Frankfurt Tevesstr.	Residential	3500	225	16	92.9	48	Achieved
B5—Ludwigshafen	Residential	750	141	16	88.7	40	Achieved
B6—Roosendaal	Residential	16,080	137	38	72.3	78	Achieved
B7—Zug	Residential	803	226	25	88.9	50	Achieved
B8—Utrecht	Residential	11,592	225	50	77.8	80	Achieved
B9—Brussels Terraced	Terraced	5850	200	29	85.5	49	Achieved
B10—Ireland nZEB	Rural Res.	N/A	300	<40	86.7	75	Achieved
B11—Graz	Residential	7889	142	14	90.1	65	Achieved
B12—Vlaardingen	Residential	11,592	300	75	75.0	95	Achieved

N/A = Not available.

. Objective f₂ (embodied carbon) is presented in kgCO₂e/m² in

Table 8. Surrogate Model Accuracy and Computational Performance Metrics.

Predicted Variable	R2 Score	RMSE	MAPE (%)	Training Duration (h)	Inference Time (s)	Speed-Up Factor
Structural Material Quantity	0.974	62 kg/m2	3.8	2.1	0.38	600×
Embodied Carbon	0.968	12 kgCO2e/m2	4.1	1.9	0.35	651×
Energy Use Intensity	0.982	3.2 kWh/(m2·yr)	2.7	2.3	0.37	616×
Lifecycle Cost	0.965	45 €/m2	4.3	2.0	0.43	530×

. Objective f₃ (operational energy use intensity) corresponds to pre- and post-renovation energy values in Table 7. Objective f₄ (architectural quality) was evaluated through the structured expert assessment conducted in Step 5 of the validation methodology (Section 3.10), whereby licensed architects and engineers verified spatial quality, constructability, and design merit for each optimized solution. This qualitative evaluation complements the three quantitative objectives and confirms the framework’s capacity to produce architecturally acceptable outcomes alongside measurable energy and structural improvements.

The AI-driven digital twin infrastructure was validated in 12 refurbishment projects across seven European nations, encompassing 75,200 m² of residential and office space. Case examples varied from a 750 m² residential building in Ludwigshafen to a 16,080 m² social housing complex in the Netherlands, illustrating scalability across different building dimensions. Pre-renovation energy consumption exhibited characteristics of mid-20th-century European architecture, with baseline intensities ranging from 137 to 803 kWh/(m²·year) and a portfolio mean of 267.7 kWh/(m²·year). Following optimization, energy consumption dropped to 14–75 kWh/(m²·year), yielding an average reduction of 84.7% (with a range of 72.3% to 93.0% across different structures). All projects met or exceeded EnerPHit/Passive House standards, with space-heating demands below 25 kWh/(m²·year).

Portfolio-level performance exhibited low variability (coefficient of variation = 8.0%), with a standard deviation of ±6.8 percentage points across the twelve case studies. The full range (72.3–93.0%) is attributable to identifiable factors: heritage preservation constraints (B3, 78.3%), exceptionally high pre-renovation baseline (B1, 93.0%), and Mediterranean climate conditions (B2, 86.7%).

Comparison with conventional deep-renovation benchmarks from IEA SHC Task 37 [17] reporting mean energy reductions of 52–65% confirms that the AI-enabled optimization framework achieved statistically higher performance across all twelve case studies, with a minimum improvement of 7.3 percentage points over the upper conventional benchmark.

Table 7 presents comprehensive performance metrics for all twelve case study buildings, documenting the systematic achievement of net-zero energy balance through integrated photovoltaic systems across diverse building typologies, construction vintages, and climatic contexts.

Multi-objective optimization per Equation (1) was applied across all case studies.

4.2. Machine Learning Surrogate Model Performance

Surrogate models based on neural networks were utilized to expedite the review process while maintaining the precision of physics-based simulations. A total of 750 building design variants, generated through Latin Hypercube Sampling, were employed to assure comprehensive coverage of the design space, with 70% of the data designated for training and 30% for validation and testing. The chosen network architecture comprised three hidden layers with 64, 32, and 16 neurons, respectively, effectively modeling nonlinear relationships while avoiding overfitting, and converged within 200–300 epochs utilizing the Adam optimizer. Model performance, as summarized in Table 8, was evaluated on an independent test set utilizing the coefficient of determination (R²) and mean absolute percentage error (MAPE).

Surrogate model validation is illustrated in Figure 3, demonstrating strong prediction accuracy across all objective functions.

The validation demonstrates strong agreement between predictions and simulations, with data clustering along the 1:1 line. Energy use intensity achieved the highest accuracy (R² = 0.982), while all models maintained errors within acceptable thresholds for optimization.

The surrogate models demonstrated robust predictive capabilities, with coefficients of determination (R²) of 0.974, root mean square errors (RMSE) of 62 kg/m² for structural material quantities, R² of 0.968 and RMSE of 12 kgCO₂e/m² for embodied carbon, and R² of 0.982 with RMSE of 3.2 kWh/(m²·year) for energy use intensity. Figure 4 demonstrates that significant reductions in energy consumption were consistently observed across all case studies, with reductions ranging from 72.3% to 93.0% (from 803 to 56 kWh/(m²·year)), thereby confirming the framework’s robustness across various building typologies and climatic conditions. The implementation of surrogate models led to considerable improvements in computational efficiency, decreasing the evaluation time from 3.8 min per design alternative to 0.35–0.43 s and reducing the overall multi-objective optimization runtime from 38 h to 4.2 h.

Ablation Study: Surrogate Model Contribution

To quantify the computational benefits of machine learning-based acceleration, the optimization performance was systematically evaluated under two configurations: (1) full physics-based simulation; and (2) machine learning surrogate modeling. The comparison focused on computational time per design evaluation and total runtime across 500 optimization generations.

Table 9. Computational performance comparison between full physics simulation and ML surrogate modeling.

Configuration	Time per Design Evaluation	Total Optimization Time (500 Generations)
Full Physics Simulation	3.8 min	38 h
ML Surrogate Model	0.37 s	4.2 h

presents a detailed performance comparison.

The results demonstrate a substantial computational advantage when surrogate models are integrated into the optimization workflow. Specifically, the evaluation time per design was reduced from 3.8 min to 0.37 s, corresponding to a 617× acceleration. At the full optimization scale, total runtime decreased from 38 h to 4.2 h, yielding an overall 9× speed-up.

To ensure that computational gains did not compromise solution fidelity, the final Pareto-optimal solutions identified via surrogate-assisted optimization were re-evaluated using full physics-based simulations. The re-evaluation revealed deviations of less than 3% across all objective functions, confirming that the surrogate models preserved optimization accuracy while significantly reducing computational cost.

It is noted that surrogate model accuracy was validated within the Latin Hypercube-sampled design space; predictions for designs outside this space may exhibit higher uncertainty. The ±3% deviation reported represents performance within the trained design space boundaries and should be interpreted accordingly.

4.3. Multi-Objective Optimization Performance and Trade-Off Analysis

Multi-objective optimization produced Pareto-optimal solutions that harmonized four primary objectives: material utilization, embodied carbon, operational energy demands, and architectural excellence. A hybrid methodology combining NSGA-III and PSO was utilized to facilitate comprehensive exploration and swift convergence. Each algorithm assessed 50,000 design variants (500 generations multiplied by 100 individuals). The hypervolume indicator was utilized to monitor convergence, which consistently rose during the search process. NSGA-III attained 90% of its ultimate hypervolume by generation 350, whereas PSO hit this benchmark by generation 200. Optimization adhered to the formulas presented in Equations (2) and (5), with Pareto dominance assessed via Equation (7).

Table 10. Comparative Performance of Multi-Objective Optimization Algorithms.

Algorithm Configuration	Convergence Generation	Pareto Solutions	Hypervolume	Spacing Metric	Runtime (h)
NSGA-III (500 gen.)	350	87	0.852	0.028	4.2
PSO (500 gen.)	380	64	0.831	0.041	3.8
Hybrid PSO → NSGA-III	320	95	0.869	0.024	4.8
NSGA-II (baseline)	420	71	0.825	0.036	4.1

presents comparative performance metrics for the two optimization algorithms evaluated independently, alongside results from a hybrid approach sequentially applying PSO for 200 generations followed by NSGA-III for an additional 300 generations.

The hybrid NSGA-III–PSO approach yielded 95 well-distributed Pareto-optimal solutions (hypervolume = 0.869), while correlation analysis showed a strong negative relationship between material cost and embodied carbon (r = −0.78) and no significant trade-offs with energy or architectural quality.

Ablation Study: Algorithm Comparison

To justify algorithm selection for four-objective optimization, NSGA-III performance was compared against the NSGA-II baseline, as shown in

Table 11. Performance comparison between NSGA-III and NSGA-II.

Metric	NSGA-III	NSGA-II (Baseline)	Improvement
Pareto Solutions Generated	87	71	+22.5%
Hypervolume Indicator	0.852	0.825	+3.3%
Spacing Metric	0.028	0.036	Better (lower)
Convergence Generation	350	420	Faster

.

NSGA-III demonstrated superior performance in maintaining solution diversity across the four-objective space. The algorithm generated 22.5% more non-dominated solutions with more uniform distribution (spacing: 0.028 vs. 0.036) and achieved faster convergence (350 vs. 420 generations). These results validate the selection of NSGA-III for many-objective optimization problems (n ≥ 3), where reference point-based diversity preservation improves Pareto front quality and distribution.

4.4. Building-Specific Performance Analysis and Design Strategies

A detailed examination of the individual case study outcomes revealed how the optimization framework automatically adapted design strategies to building-specific constraints, opportunities, and contextual factors.

Table 12. Building-Specific Optimization Strategies and Performance Outcomes.

Building	Primary Strategy	Material Reduction	Insulation (mm)	Window U-Value	Cost (€/m2)	Key Innovation
B1 Vienna Office	Envelope + BIPV integration	23%	300	0.70	950	Facade-integrated PV panels
B2 Athens	Solar control priority	20%	180	0.80	720	External shading systems
B3 Vienna Heritage	Interior insulation	15%	250	0.75	1050	Heritage-compatible retrofit
B4 Frankfurt	Passive House standard	24%	275	0.75	880	Supply air heating system
B5 Ludwigshafen	Cost-optimal balance	22%	260	0.80	740	Economic optimization focus
B6 Roosendaal	Standardized modules	19%	240	0.80	650	Prefabrication economies
B7 Zug	District heating link	21%	290	0.70	920	Renewable energy access
B8 Utrecht	Prefab facade system	20%	240	0.75	780	Occupied building retrofit
B9 Brussels	Facade-integrated PV	18%	255	0.75	830	Urban BIPV solution
B10 Ireland	Compact building form	19%	270	0.75	710	Rural nZEB approach
B11 Graz	Timber-concrete hybrid	25%	285	0.70	860	Bio-based structure
B12 Vlaardingen	Rapid installation	17%	220	0.80	690	Second-skin system

summarizes the primary optimization strategies, achieved material efficiency improvements, and key technical innovations identified for each building through the integrated material efficiency improvements per Equation (9), energy savings per Equation (10), and carbon reduction per Equation (11) calculated for all buildings.

Averaging 20.1% less than traditional structural design methods, material efficiency improvements across the portfolio ranged from 17% to 25%. Post-war buildings with over-designed structural systems saved 25% and 24% of materials, respectively, in the Graz residential complex and Frankfurt Teves Strasse project. These conservatively designed buildings with little computational analysis can be optimized using modern structural engineering methods. Recent analysis showed ways to optimize column spacing, reduce floor slab thickness with composite systems, and improve concrete strength grades based on load distributions.

Figure 5 illustrates the documentation quality for Building B4 (Tevesstrasse, Frankfurt), serving as a sample example of all twelve instances necessitating comprehensive architectural and performance records [44,45]. Material savings fluctuated based on constraints: the Vienna heritage building (B3) realized a 15% reduction due to stringent preservation regulations, while prefabrication limitations in the Vlaardingen second-skin project (B12) restricted savings to 17%.

Insulation improvement demonstrated significant climate adaptability. Continental constructions required 270–290 mm of external insulation, while the Athens Mediterranean case (B2) necessitated only 180 mm, demonstrating a 37% reduction and signifying climate-specific adaptability.

Rehabilitation costs ranged from €650/m² to €1050/m², averaging €815/m². The Roosendaal social housing project (B6, 16,080 m²) achieved the lowest cost (€650/m²) using common modules, whereas the Vienna legacy project incurred the greatest cost (€1050/m²) due to the employment of specialized materials and expertise, As illustrated in Figure 6, the Dieselweg renovation project demonstrates the transformation in building envelope and spatial configuration before and after retrofit.

4.5. Climate-Responsive Design Strategy Emergence

Given that 11 of the 12 case studies are in $CFb$ climates and only one is in $Csa$ (Athens, B2), the climate-responsive findings reported in this section should be considered preliminary observations rather than definitive cross-climate conclusions, and further validation across additional climate zones is recommended.

Analysis of optimization results in Mediterranean and continental European climate zones showed distinct design strategies, even with the same optimization algorithms and objective functions. These climate-responsive adaptations developed naturally through physics-based performance evaluation instead of explicit climate-specific programming or rule-based systems, showcasing the framework’s ability to automatically identify context-appropriate solutions. Heating demands were computed using ISO 13790 dynamic heat balance methods according to Eurocode requirements [61,66].

Table 13. Climate-Responsive Design Strategy Differentiation.

Design Parameter	Continental (CFB)	Mediterranean (CSA)	Difference	Physical Basis
Wall insulation thickness	270–290 mm	180 mm	−38%	Lower heating degree days
Window U-value target	0.70–0.80	0.75–0.85	+7%	Reduced conductive losses
South facade WWR	0.45–0.55	0.35–0.45	−20%	Solar gain control priority
External shading priority	Moderate	Critical	Essential	Cooling load dominance
SHGC optimal range	0.45–0.55	0.25–0.35	−40%	Summer overheating prevention
Night ventilation strategy	Limited use	Extensive	Fundamental	Passive cooling potential
PV capacity per 1000 m2	24 kWp	18 kWp	−25%	Higher solar irradiation
Thermal mass utilization	Moderate	Maximized	Critical	Diurnal temperature buffering
Heating system priority	Primary	Secondary	Reversed	Cooling demand emergence

summarizes the systematic differences between optimal design strategies for the two climate classifications.

Continental climates require strong thermal envelopes, incorporating insulation with an average thickness of 285 mm (U = 0.10–0.12 W/(m²·K)) and triple-glazed low-emissivity windows to reduce heating demands.

Mediterranean techniques employed reduced insulation (180 mm; U = 0.18 W/(m²·K)), prioritizing external shading (SHGC 0.25–0.35) and nocturnal ventilation (8–12 ACH) to mitigate cooling demands. Increased sun irradiation facilitated climate-adaptive photovoltaic scaling, decreasing the necessary capacity per unit area by 25% while preserving net-zero performance.

4.6. Net-Zero Energy Balance Achievement and Self-Sufficiency Analysis

All twelve case study buildings attained a net-zero energy balance, with annual on-site renewable electricity generation meeting or surpassing total energy use for the year. This success demonstrated the optimization framework’s capacity to identify integrated design solutions that eliminate net operational carbon emissions across various building types, construction ages, and climate conditions. PV sizing followed Equation (6), with Net-Zero compliance verified through Equation (5).

Table 14. Net-Zero Energy Balance Verification and Self-Sufficiency Metrics.

Building	Annual Consumption	PV Generation	Balance Ratio	Self-Sufficiency	Grid Export	Grid Import
B1 Vienna Office	56 kWh/m2·a	118 kWh/m2·a	2.1	78%	62 kWh/m2·a	12 kWh/m2·a
B2 Athens	35 kWh/m2·a	62 kWh/m2·a	1.8	82%	27 kWh/m2·a	6 kWh/m2·a
B3 Vienna Heritage	54 kWh/m2·a	68 kWh/m2·a	1.3	65%	14 kWh/m2·a	19 kWh/m2·a
B4 Frankfurt	16 kWh/m2·a	48 kWh/m2·a	3.0	85%	32 kWh/m2·a	2 kWh/m2·a
B5 Ludwigshafen	16 kWh/m2·a	40 kWh/m2·a	2.5	88%	24 kWh/m2·a	2 kWh/m2·a
B6 Roosendaal	55 kWh/m2·a	78 kWh/m2·a	1.4	68%	23 kWh/m2·a	18 kWh/m2·a
B7 Zug	25 kWh/m2·a	50 kWh/m2·a	2.0	76%	25 kWh/m2·a	6 kWh/m2·a
B8 Utrecht	50 kWh/m2·a	80 kWh/m2·a	1.6	72%	30 kWh/m2·a	14 kWh/m2·a
B9 Brussels	29 kWh/m2·a	49 kWh/m2·a	1.7	75%	20 kWh/m2·a	7 kWh/m2·a
B10 Ireland	40 kWh/m2·a	75 kWh/m2·a	1.9	79%	35 kWh/m2·a	8 kWh/m2·a
B11 Graz	14 kWh/m2·a	65 kWh/m2·a	2.6	88%	27 kWh/m2·a	2 kWh/m2·a
B12 Vlaardingen	75 kWh/m2·a	95 kWh/m2·a	1.3	65%	20 kWh/m2·a	27 kWh/m2·a

presents the energy balance calculations for each case study, comparing photovoltaic generation with final energy consumption.

Photovoltaic generation throughout the portfolio varied from 48 to 118 kWh/m²·year), indicating disparities in solar irradiation and the available roof and facade area. The energy balance ratios ranged from 1.3 to 3.0, with a mean of 1.9, thereby affirming net-zero performance for all structures. Heritage and rapid-installation projects attained ratios close to 1.3, whereas German Passive House instances obtained ratios between 2.5 and 3.0, despite constrained photovoltaic capacity. The average self-sufficiency rate was 75.5 percent, necessitating winter grid imports of 20 to 35 percent, which were counterbalanced by summer exports. Net-metering regulations facilitated steady grid-interactive net-zero functionality without the need for battery storage.

4.7. Comparative Performance Benchmarking Against Published Case Studies

This research evaluates an AI-based optimization framework for high-performance building retrofits, utilizing literature, technical reports, and Passive House refurbishment data from 2010 to 2023. Performance metrics are defined in Equations (11)–(13).

Table 15. Framework Performance Benchmarking Against Alternative Renovation Approaches.

Performance Metric	This Study (Integrated Framework)	Passive House Retrofits	Standard nZEB Renovations	Conventional Energy Renovation	Performance Advantage
Energy reduction (%)	72.3–93.0 (avg. 84.7)	65–80	50–70	30–45	+4.7% to +39.7%
Material efficiency gain (%)	17–25 (avg. 20.1)	8–12	5–8	0–3	+8.1% to +20.1%
Embodied carbon reduction (%)	15–24 (avg. 19.5)	10–15	5–10	0–5	+4.5% to +19.5%
Renovation cost (€/m2)	650–1050 (avg. 815)	850–1100	600–800	400–600	Competitive within nZEB range
Net-Zero achievement rate (%)	100 (12/12 buildings)	60–75	40–60	0–10	+25% to +100%
Design iteration time	4.2 h (ML-accelerated)	Not systematically reported	Not systematically reported	Not systematically reported	Substantial acceleration
Multi-objective integration	Simultaneous (4 objectives)	Sequential (2–3 objectives)	Sequential (2 objectives)	Energy-only focus	Holistic optimization

assesses energy reduction, material efficiency, embodied carbon, retrofit costs, and net-zero compliance in comparison to established renovation practices.

Framework-optimized designs attained an average energy reduction of 84.7%, surpassing Passive House, nZEB, and traditional retrofit solutions, while enhancing material efficiency by 20.1% and decreasing embodied carbon by 15–24%, all at a competitive cost of €815/m². All case studies achieved net-zero performance, and machine-learning acceleration decreased the optimization duration from 38 h to 4.2 h. Simultaneous multi-objective optimization facilitates synergistic, climate-adaptive solutions that are not achievable through sequential approaches, As illustrated in Figure 7, normalized performance comparison between CFB and CSA climates highlights higher cost-effectiveness in CSA conditions (92%).

5. Discussion

The AI-powered digital twin framework, validated by twelve European building renovation case studies, shows significant performance improvements across various domains concurrently. This discussion analyzes key findings, situates results within current literature, recognizes limitations, and outlines future research directions. The optimization framework addresses four competing design objectives at once, producing a Pareto in front of non-dominated solutions that illustrate optimal trade-offs as specified by Equation (7). Figure 8 illustrates a parallel coordinates plot that visualizes the complex interdependence among these objectives, showcasing the complete solution space explored by the NSGA-III algorithm.

5.1. Multi-Domain Optimization Performance

The framework achieved 84.7% energy reduction, 20.1% material efficiency gains, and full net-zero compliance, clearly outperforming sequential design [5,9]. The weak correlation between embodied carbon and operational energy (r = 0.11) supports the ability of integrated optimization to balance both objectives, consistent with Li et al. [23]. Strong negative correlation with material cost (r = −0.78) and material savings of 17–25% exceeding typical 8–12% highlight the value of simultaneously optimizing structural, energy, and architectural performance [30,31,65].

While recent studies have addressed multi-parameter optimization in building energy systems including attention-based fault diagnosis with joint hyperparameter tuning in CO₂ heat pump systems [85] and seasonal demand-side flexibility quantification in residential air conditioning [86] these works operate at the post-occupancy operational level rather than the design stage. This framework advances beyond these approaches by integrating architectural form, structural efficiency, and energy performance simultaneously within a generative design optimization loop, addressing a domain that neither operational fault diagnosis nor single-system flexibility modeling can resolve.

Addressing RQ1: The hybrid NSGA-III–PSO approach generated 95 Pareto-optimal solutions with a hypervolume of 0.869, compared to 71 solutions and hypervolume of 0.825 for the NSGA-II baseline (Table 11), confirming that the integrated framework achieves superior Pareto front quality over sequential methods. Direct side-by-side comparison with a practitioner sequential workflow was not conducted and represents a future research direction.

5.2. Climate-Responsive Optimization

A 37% difference in optimal insulation thickness between continental and Mediterranean climates demonstrates strong climate adaptability without rule-based programming [28,57]. Mediterranean designs prioritized shading, night ventilation, and thermal mass due to lower heating demand, consistent with physics-based performance findings [42,66]. The 25% reduction in required PV capacity, despite higher irradiation, confirms that integrated optimization reveals regional effects overlooked in sequential methods [56,63].

Addressing RQ3: Qualitative differences in optimal design strategies were observed between continental (CFB, n = 11) and Mediterranean (CSA, n = 1) climates, including a 37% variation in insulation thickness and distinct passive cooling priorities. However, the significant sample imbalance limits definitive conclusions, and these findings should be interpreted as preliminary observations pending broader climate-zone validation.

5.3. Computational Acceleration and Practical Feasibility

Surrogate models accelerated optimization by 500–850 × and reduced runtime from 38 h to 4.2 h while preserving >95% accuracy [22,73,79]. Energy surrogates performed slightly better (R² = 0.982) than structural models (R² = 0.974) due to smoother thermal response surfaces [66,73]. Complementary knowledge-informed acceleration methods reported by Wu et al. further support the feasibility of advanced generative design in real project timelines [22].

Addressing RQ2: Surrogate model integration reduced optimization runtime from 38 h to 4.2 h (617× per-evaluation speedup, Table 9), while maintaining prediction accuracy above 95% (R² > 0.97). This confirms the practical feasibility of ML acceleration within standard design project timelines.

5.4. Performance Benchmarking and Industry Implications

The framework exceeded Passive House retrofit performance by 5–20 percentage points (84.7% vs. 65–80%) while maintaining an average cost of €815/m² within nZEB ranges [41,42,43,44,45,46,47,48,49,50,51,52,53]. All buildings achieved net-zero, compared with 60–75% success rates in Passive House renovations, underscoring the value of integrated renewable sizing [43,49,56,63]. Material efficiency, standardized components, and precise system sizing enabled cost competitiveness and challenge assumptions about computational design complexity [22,40,47,50].

5.5. Limitations and Future Directions

This study has several limitations that should be acknowledged.

First, the case study portfolio shows climate zone imbalance (eleven continental vs. one Mediterranean) and building type imbalance (eleven residential vs. one office), reflecting data availability constraints [41,42,43,44,45,46,47,48,49,50,51,52,53]. Future validation should encompass diverse climate zones and additional building typologies.

Second, computational requirements include machine learning training (750–5000 simulation samples) and climate-specific model retraining when transferring across regions. While 500–850× surrogate acceleration enables practical application, the initial framework setup remains resource-intensive [73,79].

Third, the framework relies on simulation-based validation. While surrogate models achieved R² > 0.95 and case studies showed 0–5% validation gaps, typical performance gaps documented in the literature (10–30%) suggest that real-world implementation may achieve lower improvements due to construction quality variability and occupant behavior deviations from design assumptions [43,44,84].

Additionally, the climate zone sample is heavily skewed toward continental $CSB$ climates (n = 11) with only one Mediterranean $CSA$ case, which limits the generalizability of the climate-responsive findings reported in Section 4.5.

Future research directions include comprehensive lifecycle assessment, construction sequencing optimization, advanced occupant behavior modeling, real-time operational monitoring through continuous digital twin feedback, and extension to urban district-scale applications.

6. SWOT Analysis for AI-Enabled Digital Twin Optimization in Renovation Construction Projects

To complement the computational and empirical results, a strategic SWOT analysis was developed to assess the practical feasibility and strategic implications of adopting AI-enabled digital twin optimization in building renovation projects. The SWOT draws primarily on quantitative outcomes and qualitative observations derived from the twelve validated case studies presented in this study (energy reductions, structural efficiency improvements, embodied carbon trends, climate-responsive behaviors, surrogate model performance, and cost ranges) and no additional empirical fieldwork (interviews or surveys) was required because the substantial case-study evidence provides direct inferential support for managerial conclusions. Each SWOT entry below is therefore traceable to specific outcomes of this study.

6.1. Strengths

This section describes the key advantages of the proposed AI-enabled digital twin framework for renovation construction projects. As summarized in

Table 16. Strengths of AI-enabled digital twin optimization for renovation construction projects.

No.	Strength	Quantitative/Technical Evidence	Strategic/Managerial Implication
1	Transformative performance gains	Average 84.7% reduction in operational energy (range 72.3–93.0%) with 100% net-zero attainment across cases.	Demonstrates clear technical superiority over conventional retrofit approaches and supports net-zero policy targets.
2	Material efficiency	Approx. 20.1% improvement in structural material use while upgrading thermal performance.	Enables simultaneous structural consolidation and energy upgrading, supporting circular economy and resource efficiency.
3	Cost-competitive solutions	Average renovation cost €815/m2 (range €650–€1050/m2), comparable or lower than Passive House benchmarks.	Confirms financial viability and supports arguments for large-scale, performance-based investment in deep renovation.
4	Climate-aware optimization	Autonomous, climate-responsive decisions (e.g., 37% variance in insulation thickness across climates).	Reduces design uncertainty and tailors retrofit solutions to regional climate conditions, improving long-term robustness.
5	Operational feasibility through computational scaling	Surrogate models provide 500–850 × speed-up with >95% accuracy (R2 > 0.97); optimization time cut from ~38 h to ~4.2 h.	Makes industrial adoption feasible within typical design timelines and supports iterative scenario testing at portfolio scale.
6	Integrated multi-domain optimization	Concurrent optimization of architecture, structure, and energy rather than sequential discipline-specific runs.	Eliminates traditional cross-domain trade-offs, leading to more balanced solutions in performance, cost, and constructability.

, the strategy significantly improves energy performance, material efficiency, and cost-effectiveness while also improving climate responsiveness, computational feasibility, and cross-disciplinary integration.

6.2. Weaknesses

While the suggested AI-enabled digital twin structure is impressive, it has several drawbacks that may prevent wider use in real-world renovation construction projects. The main observed weaknesses are listed below.

I.

Strong data dependency

Relies on accurate as-built information (geometry, materials, systems); missing or outdated records in existing buildings can reduce reliability of the results. This limitation is evidenced by the monitoring data requirements documented in Table 3, where monitoring periods ranged from 1 to 6 years across the twelve case studies.

II.

High upfront setup and expertise

Requires specialist skills to build the digital twin, calibrate surrogate models, and configure multi-objective optimization; this may be prohibitive for smaller firms or low-fee projects.

The computational requirements are reflected in Table 9, where full physics-based optimization required 38 h prior to surrogate acceleration.

III.

Limited transferability to atypical buildings

Models are trained on specific typologies and renovation scenarios; performance and recommendations may not generalize well to unconventional structures or novel materials without retraining.

Surrogate model accuracy (R² > 0.95) was validated within the trained design space only, as reported in Table 8.

IV.

Integration challenges with current workflows

Interoperability with existing BIM tools, procurement procedures, and contractor practices is not seamless, potentially causing coordination overhead and resistance to adoption. The multi-software integration workflow described in Section 3.6.3 illustrates the complexity involved.

V.

Sensitivity to key assumptions

Outputs depend on assumptions about energy prices, occupancy patterns, discount rates, and embodied carbon factors; different choices can significantly change the “optimal” renovation strategies. The simulation parameters and assumptions applied in this study are detailed in Table 6.

VI.

Perceived complexity and black-box behavior

Stakeholders may find AI-based optimization opaque, which can reduce trust in the outcomes and complicate communication with clients, regulators, and contractors. The multi-algorithm comparison in Table 10 and Table 11 illustrates the complexity of the optimization process.

6.3. Opportunities

The use of artificial intelligence-enabled digital twin technologies in building rehabilitation improves technical performance while also creating an enormous number of strategic prospects for industry, governments, and practitioners. The study’s findings, which show large reductions in operational energy, embodied carbon, and renovation costs, as well as significant computing acceleration, provide the groundwork for widespread implementation in European and worldwide contexts.

Table 17. Opportunities of AI-enabled digital twin optimization for renovation construction projects.

No.	Opportunities	Quantitative/Technical Evidence	Strategic/Managerial Implication
1	Policy alignment and programmatic scaling	Average 84.7% reduction in operational energy demand (72.3–93%) 100% net-zero annual energy balance attainment Renovation cost ≈ €815/m2 (within EU high-efficiency renovation benchmarks)	Strong net-zero and energy savings outcomes create clear pathways to support EU renovation initiatives, national subsidy schemes, and municipal retrofit programs.
2	Green finance and performance-based contracting	20.1% improvement in structural material efficiency (17–25%) 15–24% reduction in embodied carbon Energy performance exceeding Passive House (65–80%) and nZEB (50–70%)	Quantified energy/carbon outcomes enable eligibility for green loans, EPC-linked finance, and value-based procurement that reward measured performance.
3	Urban and portfolio scaling	500–850 × computational acceleration from surrogate models Optimization cycle reduced 38 h → 4.2 h Validated across 12 buildings in 7 European countries	Fast surrogate-assisted optimization enables batch evaluation across building portfolios or neighborhood-scale interventions, amplifying decarbonization impact.
4	Reduced lifecycle cost and embodied carbon	15–24% embodied carbon reduction 20.1% structural material efficiency improvement Renovation costs €650–1050/m2, comparable or lower than Passive House (€850–1100/m2)	joint optimization of structural efficiency and energy performance supports circular economy objectives and potential reductions in embodied carbon.
5	Competitive differentiation for early adopters	>95% prediction accuracy (R2 > 0.97) 37% climate-driven variation in optimal insulation thickness Superior multi-objective optimization versus conventional sequential design	Contractors and consultants that internalize these methods can offer demonstrably superior retrofit packages and tender advantages.

summarizes the significant opportunities identified by the verified framework, each relating to quantitative technical data and highlighting the relevant strategic or managerial consequences. These opportunities demonstrate how the suggested technique can create policy alignment, promote green financing methods, enable large-scale portfolio optimization, advance circular economy goals, and provide competitive benefits to early adopters in the construction industry.

6.4. Threats

While the proposed AI-enabled digital twin framework offers substantial technical and strategic advantages, several external threats may hinder its widespread adoption and long-term impact. These risks originate from regulatory uncertainties, market dynamics, technological disruptions, and the inherent conservatism of the construction industry. A clear understanding of these threats is crucial to anticipate implementation barriers, ensure resilience of the proposed methodology, and support decision-makers in managing the risks associated with scaling advanced computational technologies across diverse regions and building stocks.

I.

Regulatory and code-related barriers

Existing national building regulations may not yet recognize or approve AI-driven multi-objective optimization workflows or surrogate-assisted design solutions. The five-step validation methodology described in Section 3.10 was developed specifically to address regulatory compliance requirements.

II.

Industry resistance to technological change

Contractors, engineers, and public authorities rely on conventional sequential design processes and may distrust algorithm-driven decision-making. Table 15 demonstrates the performance gap between the proposed framework and conventional retrofit approaches, which may be required to justify adoption.

III.

Potential misalignment with local energy markets

Net-zero strategies may be undermined by unstable electricity pricing, limited grid capacity, or insufficient renewable energy infrastructure. The grid import/export data presented in Table 14 illustrates the dependency on net-metering policies for net-zero balance achievement.

IV.

Cybersecurity and data privacy risks

Since digital twins depend on detailed building information, sensor data, and cloud-based computation, they are sensitive to unauthorized access or breaches. The data integration requirements described in Section 3.6 highlight the extent of building information processed by the framework.

V.

High upfront training and implementation costs

These costs may deter small and medium-sized construction firms that lack technical expertise or capital to adopt advanced AI-based tools. Training durations of 1.9–2.3 h per surrogate model (Table 8) and total optimization cycles of 4.2 h (Table 9) reflect the minimum computational investment required.

VI.

Uncertainty in long-term performance

Climate change, evolving weather patterns, and unpredictable occupant behavior could reduce the accuracy of optimization strategies derived from historical data. As noted in Section 5.5, typical performance gaps of 10–30% documented in the literature suggest real-world outcomes may differ from simulation predictions.

VII.

Supply chain and material market volatility

This may compromise the feasibility of optimal retrofit solutions by causing price instability in insulation, renewable energy systems, or structural materials. Renovation costs ranging from €650 to €1050/m² across the portfolio (Table 12) demonstrate the sensitivity of outcomes to material cost variations.

VIII.

Dependence on digital infrastructure

This creates vulnerabilities in regions where reliable high-performance computing, broadband connectivity, or digitalization of the building sector is limited. The multi-software integration workflow detailed in Section 3.6.3 requires reliable computational infrastructure for implementation.

7. Conclusions

This research established and validated a digital twin framework employing artificial intelligence, achieving remarkable simultaneous optimization of architectural design, structural efficiency, and energy performance in net-zero building restorations. Validation across twelve European case studies in seven countries demonstrated transformative results: average energy consumption reductions of 84.7% (ranging from 72.3% to 93.0%), enhancements in structural material efficiency of 20.1% (ranging from 17% to 25%), reductions in embodied carbon of 15% to 24%, and consistent achievement of net-zero energy balance across all twelve case studies under the studied conditions, performance metrics that significantly exceed conventional Passive House retrofits (65% to 80%) and standard near-Zero Energy Building implementations (50% to 70%).

Climate-responsive optimization capabilities developed organically through physics-based assessment; preliminary observations suggest a 37% variation in insulation thickness between continental and Mediterranean climates, pending broader validation across additional climate zones, context-specific window specifications, and regionally tailored renewable energy sizing without manual programming or rule-based modifications. Machine learning surrogate models attained computational acceleration factors of 500–850× while preserving prediction accuracies above 95% (R² > 0.97), decreasing total optimization cycles from 38 h to 4.2 h and removing significant obstacles to practical application. Framework-optimized designs attained renovation costs averaging €815/m² (ranging from €650 to €1050/m²), comparable to or cheaper than the reported Passive House costs (€850–€1100/m²), while concurrently addressing criteria typically seen as mutually contradictory.

This research introduces significant scientific progress by integrating generative artificial intelligence with digital twin technologies to significantly reduce trade-off severity between competing objectives, as evidenced by the correlation analysis in Section 4.3 (r = −0.78), thus establishing validated computational frameworks for concurrent multi-domain optimization that were previously unattainable through conventional techniques. Proven cost-effectiveness, significant performance enhancements, and net-zero attainment across various building types and climates offer compelling evidence that integrated artificial intelligence frameworks serve as transformative solutions for decarbonizing the construction industry.

Several limitations affect the generalizability of these findings. The geographic sample is dominated by continental CFB climates (n = 11), with only one Mediterranean case, which restricts cross-climate conclusions. The framework’s performance depends on the availability of monitored data for digital twin calibration, and surrogate model accuracy was validated within the trained design space boundaries only. Furthermore, the framework has been validated exclusively on residential and office typologies; industrial or mixed-use buildings may require additional objective functions.

Future research directions encompass the integration of comprehensive lifespan assessments, modeling of occupant behavior, extensions for real-time operational management, and urban-scale implementations that handle inter-building energy dynamics. This study illustrates that artificial intelligence-enabled digital twin technologies facilitate systematic optimization of building restorations, achieving net-zero efficiency while maintaining structural integrity and architectural excellence. These technologies provide scalable solutions that comply with the European Union’s climate neutrality objectives and substantially aid in global climate change mitigation by fostering the sustainable transformation of the built environment.