Search Results (418)

This study focuses on buildings in the Chinese–Baroque Historic Shopping District in Harbin. In view of global climate change and high carbon emissions from the construction industry, this study aims to quantify carbon emissions during the decoration process and explore low-carbon decoration strategies that suit the local characteristics. This research adopts a four-stage framework of “data collection–quantitative analysis–strategy design–verification and optimization” and integrates Life Cycle Assessment (LCA) and multi-objective optimization theory. Data are collected through questionnaires and field investigations, and simulations and analyses are carried out using Grasshopper and Honeybee. The results show that there are differences in carbon emissions between different decoration schemes. The chosen scheme of raw concrete and paint results in relatively low carbon emissions over the 10.12-year usage cycle. Based on this, design strategies such as extending the service life of decorations, rationally renovating windows, and preferentially selecting local low-carbon materials are proposed and applied to practical projects. This study not only fills a gap in the research on the low-carbon renovation of historical commercial blocks from the perspective of LCA but also provides practical solutions for the sustainable development of historical shopping blocks in Harbin and similar regions, promoting the low-carbon transformation of cities. Full article

Natural disasters are increasing in frequency and intensity, causing escalating humanitarian crises and complex housing challenges globally. Traditional post-disaster housing solutions often fall short, being slow, costly, and ill-adapted to specific community needs. This study addresses these limitations by proposing an innovative, technology-driven model for post-disaster housing that integrates parametric design with 3D printing. The objective is to develop a flexible and adaptable system capable of providing both immediate temporary shelter and evolving permanent housing solutions. In this study, the methodology of the proposed model for post-disaster housing solutions is structured around three main phases: the development of the theoretical framework, the parametric design process, and the implementation phase. In the first phase, a comprehensive literature review and conceptual analyses were conducted to examine the concept of disaster, post-disaster housing approaches, and advanced technologies, thereby establishing the conceptual foundation of the model. In the second phase, parametric modeling was carried out for a modular system using algorithmic design tools such as Grasshopper; the model’s applicability across various scales and its flexibility were analyzed. In the final phase, material selection and digital prototyping of the gridal system were undertaken using 3D printing technology to evaluate the model’s feasibility for rapid on-site production, assembly, and disassembly. The model prioritizes user participation, modularity, and configurability to ensure rapid response and socio-cultural sensitivity. Findings indicate that this integrated approach offers substantial benefits, including accelerated construction, reduced labor and material waste, enhanced design flexibility, and the use of local, sustainable materials. This research highlights the transformative potential of advanced manufacturing in providing resilient, user-centered, and environmentally sustainable post-disaster housing, advocating for governmental financial support to overcome adoption barriers and foster broader implementation. Full article

Daylighting plays a pivotal role in mosques, shaping their sacred atmosphere and enhancing the spiritual experience for worshippers. Beyond a mere architectural consideration, the integration of natural light into mosque design fundamentally influences the ambiance and functionality of these religious spaces. This study investigates the key factors that enhance daylight levels and visual comfort within prayer halls. It specifically evaluates illuminance levels, light distribution, and glare in four domed mosques located in Saudi Arabia. Field measurements were conducted beneath the domes of these prayer spaces, each featuring clerestory windows of varying forms and dimensions. Based on architectural specifications and material properties, daylight simulations and modeling were performed using the RADIANCE engine integrated with Grasshopper. The simulation results were validated against on-site illuminance measurements to ensure model accuracy and reliability. The primary objective was to assess whether the existing daylighting conditions comply with the recommended illuminance standards for reading and prayer, typically ranging from 150 to 500 lux. This study revealed that the illuminance levels in the central dome area exceeded the recommended values, reaching over 3000 lux. To improve daylight distribution, shading systems such as flat and curved shelves were added to the drum’s windows. This research concludes that the light shelves and vacuum double glazing significantly improved indoor daylight performance by preventing direct sunlight entry into the prayer hall and redirecting it towards the dome. This intervention successfully reduced excessive illuminance levels to a more optimal level of around 447–774 lux during the noon prayer period, ensuring a balanced and comfortable environment for worshippers. Full article

Dynamic and Adaptive solar systems demonstrate a greater potential to enhance the satisfaction of occupants, in terms of indoor environment quality and the energy efficiency of the buildings, than conventional shading solutions. This study has evaluated Dynamic and Adaptive Photovoltaic Shading Systems (DAPVSSs) through a comprehensive analysis of six shading designs in which their energy production and the comfort of occupants were considered. Energy generation, thermal comfort, daylight, and glare control have been assessed in this study, considering multiple orientations throughout the seasons, and a variety of tools, such as Rhino 6.0, Grasshopper, ClimateStudio 2.1, and Ladybug, have been exploited for these purposes. The results showed that the prototypes that were geometrically more complex, designs 5 and 6 in particular, had approximately 485 kWh higher energy production and energy savings for cooling and 48% better glare control than the other simplified configurations while maintaining the minimum daylight as the threshold (min DF: 2%) due to adaptive and control methodologies. Design 6 demonstrated optimal balanced performance for all the aforementioned criteria, achieving 587 kWh/year energy production while maintaining the daylight factor within the 2.1–2.9% optimal range and ensuring visual comfort compliance during 94% of occupied hours. This research has established a framework that can be used to make well-informed design decisions that could balance energy production, occupants’ wellbeing, and architectural integration, while advancing sustainable building envelope technologies. Full article

Under the dual challenges of global energy crisis and climate change, the building sector, as a major carbon emitter consuming 33% of global primary energy, has seen its energy efficiency optimization become a critical pathway towards achieving carbon neutrality goals. The Window-to-Wall Ratio (WWR), serving as a core parameter in building envelope design, directly influences building energy consumption, with its optimized design playing a decisive role in balancing natural daylighting, ventilation efficiency, and thermal comfort. This study focuses on the traditional One-Seal dwellings (Yikeyin) in Kunming, China, establishing a dynamic wind field-thermal environment coupled analysis framework to investigate the impact mechanism of window dimensions (WWR and aspect ratio) on indoor thermal comfort under natural wind conditions in transitional climate zones. Utilizing the Grasshopper platform integrated with Ladybug, Honeybee, and Butterfly plugins, we developed parametric models incorporating Kunming’s Energy Plus Weather meteorological data. EnergyPlus and OpenFOAM were employed, respectively, for building heat-moisture balance calculations and Computational Fluid Dynamic (CFD) simulations, with particular emphasis on analyzing the effects of varying WWR (0.05–0.20) on temperature-humidity, air velocity, and ventilation efficiency during typical winter and summer weeks. Key findings include, (1) in summer, the baseline scenario with WWR = 0.1 achieves a dynamic thermal-humidity balance (20.89–24.27 °C, 65.35–74.22%) through a “air-permeable but non-ventilative” strategy, though wing rooms show humidity-heat accumulation risks; increasing WWR to 0.15–0.2 enhances ventilation efficiency (2–3 times higher air changes) but causes a 4.5% humidity surge; (2) winter conditions with WWR ≥ 0.15 reduce wing room temperatures to 17.32 °C, approaching cold thresholds, while WWR = 0.05 mitigates heat loss but exacerbates humidity accumulation; (3) a symmetrical layout structurally constrains central ventilation, maintaining main halls air changes below one Air Change per Hour (ACH). The study proposes an optimized WWR range of 0.1–0.15 combined with asymmetric window opening strategies, providing quantitative guidance for validating the scientific value of vernacular architectural wisdom in low-energy design. Full article

The Grasshopper Optimization Algorithm (GOA) has attracted significant attention due to its simplicity and effective search capabilities. However, its performance deteriorates when dealing with high-dimensional or complex optimization tasks. To address these limitations, this study proposes an improved variant of GOA, named Outpost Multi-population GOA (OMGOA). OMGOA integrates two novel mechanisms: the Outpost mechanism, which enhances local exploitation by guiding agents towards high-potential regions, and the multi-population enhanced mechanism, which promotes global exploration and maintains population diversity through parallel evolution and controlled information exchange. Comprehensive experiments were conducted to evaluate the effectiveness of OMGOA. Ablation studies were performed to assess the individual contributions of each mechanism, while multi-dimensional testing was used to verify robustness and scalability. Comparative experiments show that OMGOA has better optimization performance compared to other similar algorithms. In addition, OMGOA was successfully applied to a real-world engineering problem—lithology prediction from petrophysical logs—where it achieved competitive classification performance. Full article

This study investigates strategies for urban-scale renewable energy integration through a photovoltaic-centric approach, with a case study of a district in Barcelona. The methodology integrates spatial and morphological data using a geographic information system (GIS)-based and clustering framework to address challenges of CO₂ emissions, air pollution, and energy inefficiency. Rooftop availability and photovoltaic (PV) design constraints are analysed under current urban regulations. The spatial analysis incorporates building geometry and solar exposure, while an evolutionary optimisation algorithm in Grasshopper refines shading analysis, energy yield, and financial performance. Clustering methods (K-means and 3D proximity) group PV panels by solar irradiance uniformity and spatial coherence to enhance system efficiency. Eight PV deployment scenarios are evaluated, incorporating submodule integrated converter technology under a solar power purchase agreement model. Results show distinct trade-offs among PV scenarios. The standard fixed tilted (31.5° tilt, south-facing) scenario offers a top environmental and performance ratio (PR) = 66.81% but limited financial returns. In contrast, large- and huge-sized modules offer peak financial returns, aligning with private-sector priorities but with moderate energy efficiency. Medium- and large-size scenarios provide balanced outcomes, while a small module and its optimised rotated version scenarios maximise energy output yet suffer from high capital costs. A hybrid strategy combining standard fixed tilted with medium and large modules balances environmental and economic goals. The district’s morphology supports “solar neighbourhoods” and demonstrates how multi-scenario evaluation can guide resilient PV planning in Mediterranean cities. Full article

Accurate SOH prediction provides a reliable reference for lithium-ion battery maintenance. However, novel algorithms are still needed because few studies have considered the correlations between monitored parameters in Euclidean space and non-Euclidean space at different time points. To address this challenge, a novel gated-temporal network assisted by improved grasshopper optimization (IGOA-GGNN-TCN) is developed. In this model, features obtained from lithium-ion batteries are used to construct graph data based on cosine similarity. On this basis, the GGNN-TCN is employed to obtain the potential correlations between monitored parameters in Euclidean and non-Euclidean spaces. Furthermore, IGOA is introduced to overcome the issue of hyperparameter optimization for GGNN-TCN, improving the convergence speed and the local optimal problem. Competitive results on the Oxford dataset indicate that the SOH prediction performance of proposed IGOA-GGNN-TCN surpasses conventional methods, such as convolutional neural networks (CNNs) and gate recurrent unit (GRUs), achieving an R² value greater than 0.99. The experimental results demonstrate that the proposed IGOA-GGNN-TCN framework offers a novel and effective approach for state-of-health (SOH) estimation in lithium-ion batteries. By integrating improved grasshopper optimization (IGOA) with hybrid graph-temporal modeling, the method achieves superior prediction accuracy compared to conventional techniques, providing a promising tool for battery management systems in real-world applications. Full article

The city of Al Ain is a fast-developing area. With building typology varying from low-rise to mid-rise, sustainable design in buildings is needed. As the majority of the city’s population is Emirati Citizens, the percentage of expats is increasing. The expats tend to live in mid-rise buildings. One of the central midrise areas is AL Ain Square. This study aims to investigate how an optimized mashrabiya pattern can impact the energy and the Predicted Mean Vote (PMV) in a 3-bedroom apartment, fully oriented to the south, of an expat family. The methodology is as follows: case study selection, Weather analysis, Modeling/Validation of the base case scenario, Optimization of the mashrabiya pattern, Simulation of various scenarios, and Results. Analyzing the selected case study is the initial step of the methodology. This analysis begins with the district, building typology, and the chosen apartment. The weather analysis is relevant for using the mashrabiya (screen device) and the need to improve energy consumption and thermal comfort. The modeling of the base case shall be performed in Rhino Grasshopper. The validation is based on a one-year electricity bill provided by the owner. The optimization of mashrabiya patterns is an innovative process, where various designs are compared and then optimized to select the most efficient pattern. The solutions to the selected scenarios will then yield the results of the optimal scenario. This study is relevant to industry, academia, and local authorities as an innovative approach to retrofitting buildings. Additionally, the research presents a creative vision that suggests optimized mashrabiya patterns can significantly enhance energy savings, with the hexagonal grid configuration demonstrating the highest efficiency. This finding highlights the potential for geometry-driven shading optimization tailored to specific climatic and building conditions. Contrasting earlier mashrabiya studies that assess one static pattern, we couple a geometry-agnostic evolutionary solver with a utility-calibrated EnergyPlus model to test thousands of square, hexagonal, and triangular permutations. This workflow uncovers a previously undocumented non-linear depth perforation interaction. It validates a hexagonal screen that reduces annual cooling energy by 12.3%, establishing a replicable, grid-specific retrofit method for hot-arid apartments. Full article

The growing sophistication of malware and other cyber threats presents significant challenges for detection and prevention in modern cybersecurity systems. In this paper an efficient and novel malware classification model using the Hybrid Resnet-Transformer Network (HRT-Net) and Improved Grasshopper Optimization Algorithm (IGOA) is proposed. Convolutional layers in the resnet50 model effectively extract local features from malware patterns, while the Transformer focuses on long-range dependencies and complex patterns by leveraging multi-head attention. The extracted local and global features are concatenated to create a rich feature representation, enabling precise malware detection. The Improved Grasshopper Optimization Algorithm with dynamic mutation coefficient and dynamic inertia motion weights is employed to select an optimal subset of features, reducing computational complexity and enhancing classification performance. Finally, the Ensemble Learning technique is used to robustly classify malware samples. Experimental evaluations on the Malimg dataset demonstrate the high efficiency of the proposed method, achieving an impressive accuracy of 99.77%, which shows greater efficiency compared to other recent studies. Full article

China’s urban residential building stock is extensive and spans a wide range of construction periods. With the continuous enhancement of building energy efficiency standards, the chronological characteristics and variability of residential building envelopes are evident. Through field research and typological analysis of residential buildings in Jinan, a cold region of China, three construction eras were classified: Period I (1980–1985), Period II (1986–1995), and Period III (1996–2005). Building performance and economic benefits across these periods are modeled using Rhino 7.3 and Grasshopper. The NSGA-II algorithm, as the core of Wallacei2.7, is employed for multi-objective optimization. Through K-means clustering, TOPSIS comprehensive ranking, and Pearson correlation analysis, the optimized processes and solutions are provided for urban residential renovation decisions in different periods and target preferences. The results show that the optimal comprehensive performance solutions for Period I, Period II, and Period III achieve energy savings of 40.92%, 29.62%, and 15.81%, respectively, and increase annual indoor comfort hours by 872.64 h/year, 633.57 h/year, and 564.11 h/year. For Period I and II residential buildings, the most effective energy efficiency retrofit measures include increasing exterior wall and roof insulation, replacing exterior window types, and reducing exterior window k-value. The overall trend in energy savings rates and economic benefits across the three periods shows a decline. For Period III residential buildings, systematic strategies, such as solar thermal collector systems and photovoltaic technology, are required to enhance energy efficiency. Full article

Indoor environmental quality profoundly impacts student learning outcomes and teacher effectiveness, particularly in primary education, where children spend most of their developmental years. The study compares the New Zealand Ministry of Education’s Designing Quality Learning Spaces (DQLS) version 2.0 for primary school classrooms with international standards set by OECD countries to develop IAQ and thermal comfort best practices in New Zealand across six climate zones. The research evaluates indoor air quality (IAQ) and thermal comfort factors affecting students’ and teachers’ health and performance. Using Ladybug and Honeybee plugin tools in Grasshopper with Energy Plus, integrated into Rhino 7 software, the study employed advanced building optimisation methods, using multi-criteria optimisation and parametric modelling. This approach enabled a comprehensive analysis of building envelope parameters for historical classroom designs, the Avalon block (constructed between 1955 and 2000). Optimise window-to-wall ratios, ceiling heights, window placement, insulation values (R-values), clothing insulation (Clo), and window opening schedules. Our findings demonstrate that strategic modifications to the building envelope can significantly improve occupant comfort and energy performance. Specifically, increasing ceiling height by 0.8 m, raising windows by 0.3 m vertically, and reducing the window-to-wall ratio to 25% created optimal conditions across multiple performance criteria. These targeted adjustments improved adaptive thermal comfort, ventilation, carbon dioxide, and energy efficiency while maintaining local and international standards. The implications of the findings extend beyond the studied classrooms, offering evidence-based strategies for overall design and building performance guidelines in educational facilities. This research demonstrates the efficacy of applying computational design optimisation during early design phases, providing policymakers and architects with practical solutions that could inform future revisions of New Zealand’s school design standards and align them more closely with international best practices for educational environments. Full article

Recent advances in visual programming tools for algorithmic modelling have significantly expanded the possibilities for designing industrial products. This study analyses the capacity and adaptability of Grasshopper, a graphical algorithm editor integrated with Rhinoceros 3D, as a parametric design tool in the development of product platforms. Three case studies were conducted to evaluate the impact of parameter configuration in product families: perfume bottles, outdoor furniture, and desk organisers. The analysis provided insight into the ability of Grasshopper to (1) automate the generation of product variants within platforms; (2) enable the flexible creation of scalable, customised design alternatives; and (3) improve efficiency in the platform design process in terms of time and technical resources. The results show that Grasshopper provides strong capabilities for customising geometric parameters compared to traditional modelling in Rhinoceros 3D. However, its adaptability is more limited when customisation involves interdependent parameters, such as those related to ergonomics or usability, due to the difficulty of translating these requirements into algorithmic structures. In addition, the initial definition of parameters and constraints may restrict modifications in later design phases. These findings underline the need for algorithm models that support iterative adjustments and flexible reconfiguration throughout all phases of the design process. Full article

Effective path planning in complex underwater environments serves as a critical determinant of autonomous underwater vehicle (AUVs) energy efficiency, while simultaneously influencing sensor operational demands and battery state-of-charge (SOC) dynamics. Systematic trajectory tracking emerges as a pivotal methodology for SOC optimization, enabling enhanced energy management through precision navigation control. This paper proposes a path planning and trajectory tracking control framework for autonomous underwater vehicles (AUVs) combined with battery state of charge (SOC) optimization. The framework incorporates the Grasshopper Optimization Algorithm (GOA) with the Artificial Potential Field Algorithm (APF) to achieve global path planning and local path optimization while minimizing energy consumption as an objective. Specifically, GOA is used for global path planning. APF further optimizes the path by introducing a SOC optimization strategy, in which high SOC consumption points are regarded as repulsive points and low SOC consumption points are regarded as attractive points. In addition, the trajectory tracking control adopts the model predictive control (MPC) method to ensure the accurate tracking of the planned path and dynamically manage the SOC states. Simulation results show that the proposed framework outperforms traditional methods in obstacle avoidance capability and SOC consumption, effectively improving energy efficiency and trajectory tracking accuracy. Full article

The increasing popularity of edible insects as a sustainable food source necessitates stringent safety measures to monitor pesticide contamination. This study aimed to assess and enhance a QuEChERS-based extraction method coupled with gas chromatography–tandem mass spectrometry (GC-MS/MS) for the quantification of pesticide residues in edible insects (bamboo caterpillars, house crickets, silkworm pupae, giant water bugs, and grasshoppers) by combining multiple individual insect specimens into a single, homogenized sample—five replicates were tested. The method was optimized by evaluating various extraction parameters and showed strong linearity for all 47 target pesticides, with correlation coefficients (R²) ranging from 0.9940 to 0.9999. The limits of detection (LODs) varied between 1 and 10 µg/kg, while the limits of quantification (LOQs) ranged from 10 to 15 µg/kg. Recovery studies conducted at three fortification levels (10, 100, and 500 µg/kg) revealed recoveries ranging from 64.54% to 122.12%, that over 97.87% of the pesticides exhibited satisfactory recoveries within the range of 70–120%, and relative standard deviations (RSDs) below 20%, between 1.86% and 6.02%. Matrix effects (%MEs) range from −33.01% to 24.04%, and to those that experienced no effect. More than 94% of the analytes showed minimal ion suppression or enhancement. These results conform to the SANTE guidelines for monitoring pesticide residues in edible insects, enhancing food safety standards and safeguarding consumer protection. Full article