Search Results (1,327)

Life cycle cost (LCC) analysis has become a key tool for evaluating the long-term economic and environmental performance of built assets, yet its application in marinas and marine infrastructure remains underdeveloped. This review provides the first structured attempt to apply LCC to marina infrastructure, addressing the lack of sector-specific models for pontoons, mooring systems, and marina operations. It also synthesizes research on LCC methodologies, challenges, and emerging trends relevant to coastal facilities, with a particular focus on pontoons, mooring systems, and marina management practices. Studies reveal persistent barriers to effective implementation, including fragmented data systems, inconsistent regulations, and limited sector-specific tools. Existing models, largely adapted from other construction contexts, often overlook the unique technical, environmental, and operational demands of marine assets. The review critically examines international standards, procurement frameworks, and methodological approaches, highlighting opportunities to integrate sustainability considerations and address gaps in cost forecasting. It also identifies the need for standardized data collection practices and risk-based maintenance strategies tailored to harsh marine environments. By mapping current knowledge and methodological limitations, this work provides a foundation for developing more accurate, sector-specific LCC models and guidance. This literature review contributes to the advancement of sustainable coastal infrastructure planning by consolidating scattered research, emphasizing knowledge gaps, and outlining priorities for future studies, supporting policymakers, practitioners, and researchers seeking to optimize investment decisions in marinas and related facilities. Full article

Health Technology Assessment (HTA) is a multidimensional and multidisciplinary approach for analyzing the medical–clinical, social, organizational, economic, ethical, and legal implications of a technology, through the evaluation of multiple dimensions such as efficacy, safety, costs, and social–organizational impact. In the healthcare context, “technology” refers to any tool—including pharmaceuticals (or, in this case, vaccines)—that is applied to healthcare practice. HTA focuses on assessing both the real and potential effects of a given technology, either prospectively or throughout its life cycle, as well as the consequences that the introduction or exclusion of an intervention may have on the healthcare system, the economy, and society at large. Full article

In this work a multi-criteria analysis and an optimization tool were developed, which allows the substitution of fossil-based solvents with bio-based alternatives based on Hansen solubility parameters and various physical parameters, such as the boiling point, evaporation rate, viscosity or wetting behavior. The proof of concept was achieved by formulating two different paints used in coil coatings using the bio-based solvents, and they performed equally as well as their fossil-based counterparts. A potential decrease in CO₂ emissions was determined by a life cycle assessment and cradle-to-grave analysis of bio- and fossil-based solvents, which showed a large sustainability bonus when using solvents based on biomass. The introduced methodology provides initial insights into substituting currently used solvents systematically. Overall, implementing bio-based solvents is a viable drop-in method to decrease the environmental impact of paints and coatings, while maintaining the same performance. Full article

Social impact assessment (SIA) is a key tool for advancing the Sustainable Development Goals (SDGs) within project contexts. Originating largely from international practice, SIA requires localization to fit national conditions. This research examines an Asian Development Bank (ADB) loan project in Hubei Province, China, comparing the SIA conducted under ADB procedures with China’s domestic practice of social stability risk assessment (SSRA)—China’s localized practice of SIA. Adopting a full project life cycle perspective, this article conducts a comprehensive gap analysis between SIA and SSRA from five key dimensions: cycle requirement of assessment, implementation of assessment activity, assessment outcome, application of assessment outcome, and assessment objective. While both approaches are quite similar in four aspects (assessment subject, assessment principle, risk investigation method, and risk classification), SIA places greater emphasis on social sustainability dimensions (poverty, gender, ethnic minority, and involuntary resettlement), whereas SSRA mainly focuses on social stability during the early project proposal and feasibility study stages. Building on the preceding analysis and comparison between SIA and SSRA, this research proposes a “Social Stability–Social Sustainability” progressive framework. Social stability serves as the foundational condition, while the framework emphasizes enhancing the long-term adaptive capacity of social governance through inclusion, participation, and resilience, thereby achieving genuine social sustainability. Full article

In recent years, corporate perceptions of environmental sustainability have shifted from viewing it as a compliance burden to recognizing it as a strategic driver of innovation and competitive advantage, prompting a demand for effective sustainable design tools. Traditionally, tools like the Life Cycle Assessment (LCA) tool have been used to evaluate environmental impacts, yet their complexity, cost, and retrospective focus make them impractical for driving early-stage, disruptive innovation. Although biomimicry has emerged as a promising approach, adopting this novel interdisciplinary design practice within a corporate setting requires significant resources and time, disrupting established processes. Therefore, the biomimicry Life Principles (LPs) tool, a guiding sustainable design tool of the practice, provides an opportunity to lower the barrier to the entry of biomimicry within a corporate setting and potentially increases adoption of the broader practice. This comparative study seeks to explore the creative potential, and practical value of the biomimicry LPs tool compared to the traditional LCA approach while exploring the intrinsic motivation of R&D practitioners to implement these tools within a virtual product redesign workshop. To derive our conclusions, we employed a mixed-methods approach comprising a 23-item survey designed to assess practitioners’ intrinsic motivation and perceived practical value of the implemented tool alongside an external evaluation of the creativity of all generated design concepts. Together, these methods provide empirical evidence of the biomimicry LPs tool’s potential to enhance creative output, require minimal adoption effort, and act as a catalyst for whole-systems thinking in sustainable innovation. These findings offer compelling evidence to support their strategic addition to existing R&D toolkits and workflows. By highlighting the efficacy, accessibility, and intrinsic motivation of R&D professionals to use biomimicry LPs, the results underscore the viability of this tool to streamline the integration of biomimicry design thinking into real-world workflows. As such, they represent a pragmatic and scalable pathway to catalyze broader and deeper engagement with biomimicry across corporate contexts. Full article

Vibration loads during deep drilling are one of the main causes of reduced service life of drilling tools and emergency failure of downhole motors. This work investigates the adaptive operation of an original elastic element based on an open cylindrical shell used as part of a drilling shock absorber. The vibration protection device contains an adjustable radial clearance between the load-bearing shell and the rigid housing, which provides the effect of structural nonlinearity. This allows effective combination of two operating modes of the drilling shock absorber: normal mode, when the clearance does not close and the elastic element operates with increased compliance; and emergency mode, when the clearance closes and gradual load redistribution and increase in device stiffness occur. A nonconservative problem concerning the contact interaction of an elastic filler with a coaxially installed shaft and an open shell is formulated, and as the load increases, contact between the shell and the housing, installed with a radial clearance, is taken into account. Numerical finite element modeling is performed considering dry friction in contact pairs. The distributions of radial displacements, contact stresses, and equivalent stresses are examined, and deformation diagrams are presented for two loading modes. The influence of different cycle asymmetry coefficients on the formation of hysteresis loops and energy dissipation is analyzed. It is shown that with increasing load, clearance closure begins from local sectors and gradually covers almost the entire outer surface of the shell. This results in deconcentration of contact pressure between the shell and housing and reduction of peak concentrations of equivalent stresses in the open shell. The results confirm the effectiveness of the adaptive approach to designing shell shock absorbers capable of reliably withstanding emergency overloads, which is important for deep drilling where the exact range of external impacts is difficult to predict. Full article

In this paper, Distribution of Relaxation Time (DRT) analysis is presented as a powerful tool for understanding the aging mechanisms in lithium-ion batteries, with a focus on its application to estimating the State of Health (SOH). A novel parameter, the characteristic relaxation time, derived from DRT analysis, is introduced to enhance SOH estimation. By analyzing the ratio of the central relaxation time (τ) between the charge transfer and diffusion peaks, the battery status can be determined without the need for historical data. Experimental data from lithium-ion batteries, including 18650 cells and LR2032 coin cells, were examined until the end of their life. Nyquist and DRT plots across various frequency ranges revealed consistent aging trends, particularly in the charge transfer and diffusion processes. These processes appeared as shifting and merging peaks in the DRT plots, signifying progressive degradation. A polynomial equation fitted to the τ ratio graph achieved a high accuracy (Adj. R² = 0.9994), enabling reliable battery lifespan prediction. Validation with a Samsung Galaxy S9+ battery demonstrated that the method could estimate its remaining life, predicting a total lifespan of approximately 2100 cycles (compared to 1000 cycles already completed). These results confirm that SOH estimation is feasible without prior data and highlight the potential of DRT analysis for accurate and quantitative prediction of battery longevity. Full article

As climate extremes increasingly threaten global food security, precision tools for early detection of crop stress have become vital, particularly for root crops such as potato (Solanum tuberosum L.) and sweet potato (Ipomoea batatas L. Lam.), which are especially susceptible to environmental stressors throughout their life cycles. In this study, plants were monitored from the initial onset of seasonal stressors, including spring drought, heat, and episodes of excessive rainfall, through to harvest, capturing the full range of physiological and biochemical responses under seasonal, simulated conditions in greenhouses. The spectral data were obtained from regions of interest (ROIs) of each cultivar’s leaves, with over 3000 data points extracted per cultivar; these data were subsequently used for model development. A comprehensive classification framework was established by employing machine learning models, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Partial Least Squares-Discriminant Analysis (PLS-DA), to detect stress across various growth stages. Furthermore, severity levels were objectively defined using photoreflectance indices and principal component analysis (PCA) data visualizations, which enabled consistent and reliable classification of stress responses in both individual cultivars and combined datasets. All models achieved high classification accuracy (90–98%) on independent test sets. The application of the Successive Projections Algorithm (SPA) for variable selection significantly reduced the number of wavelengths required for robust stress classification, with SPA-PLS-DA models maintaining high accuracy (90–96%) using only a subset of informative bands. Furthermore, SPA-PLS-DA-based chemical imaging enabled spatial mapping of stress severity within plant tissues, providing early, non-invasive insights into physiological and biochemical status. These findings highlight the potential of integrating hyperspectral imaging and machine learning for precise, real-time crop monitoring, thereby contributing to sustainable agricultural management and reduced yield losses. Full article

Advanced energy storage solutions are required to mitigate grid destabilization caused by high-penetration renewable energy integration. Superconducting Magnetic Energy Storage (SMES) offers ultrafast response (<1 ms), high efficiency (>95%), and almost unlimited cycling life. However, its commercialization is hindered by the complex modeling of critical current with anisotropic behaviors and the computational inefficiency of high-dimensional optimization for megajoule (MJ)-class magnets. This paper proposes an integrated design framework synergizing a two-dimensional axisymmetric magnetic field model based on Conway’s current-sheet theory, a critical current anisotropy characterization model, and an adaptive genetic algorithm (AGA). A superconducting magnet optimization model incorporating co-calculation of electromagnetic parameters is established. A dual-module chromosome encoding strategy (discrete gap index + nonlinear increment) and parallel acceleration techniques were developed. This approach achieved efficient optimization of MJ-class magnets. For a single solenoid, the critical current increased by 22.6% (915 A) and energy storage capacity grew by 41.8% (1.12 MJ). A 20-unit array optimized by coordinated gap adjustment achieved a matched inductance/current of 0.15 H/827 A (20 MJ), which can enhance transient stability control capability in smart grids. The proposed method provides a computationally efficient design paradigm and user-friendly teaching software tool for high-current SMES magnets, supporting the development of large-scale High-Temperature Superconducting (HTS) magnets, promoting the deployment of large-scale HTS magnets in smart grids and high-field applications. Full article

The transition to a net-zero economy requires a shift towards circular economy principles, particularly within the burgeoning electric vehicle sector. This paper presents a systematic literature review of 57 studies published between 2017 and the end of August 2025, examining methodologies for assessing the circularity of electric vehicles. The analysis reveals a predominant focus on environmental impact quantification through life cycle assessment and material flow analysis, with limited direct application of tailored circularity assessment tools. A significant knowledge gap is identified in the integration of environmental, economic, and social dimensions within electric vehicle circularity assessments. Furthermore, the absence of electric vehicle-specific assessment tools and the challenges associated with data reliability and indicator measurement are highlighted. The paper proposes the adoption of digital product passports and a dynamic systems view to enhance electric vehicle circularity assessments. This approach aims to provide a more comprehensive, multidisciplinary understanding of electric vehicle lifecycle impacts, facilitating informed decision-making for sustainable e-mobility. Full article

Cracking in asphalt layers of rigid–flexible composite pavements under coupled ambient temperature fields and traffic loading represents a critical failure mode. Traditional models based on uniform temperature assumptions inadequately capture the crack propagation mechanisms. This study developed a thermo-mechanical coupling model that incorporates realistic temperature-modulus gradients to analyze the compressive-shear behavior and simulate crack propagation using the extended finite element method (XFEM) coupled with a modified Paris’ law. Key findings reveal that the asphalt layer exhibits a predominant compressive-shear stress state; increasing the base modulus from 10,000 MPa to 30,000 MPa reduces the maximum shear stress by 22.8% at the tire centerline and 8.6% at the edge; thermal stress predominantly drives crack initiation, whereas vehicle loading governs the propagation path; field validation via cored samples confirms inclined top-down cracking under thermo-mechanical coupling; and the fracture energy release rate (G_f) reaches a minimum of 155 J·m⁻² at 14:00, corresponding to a maximum fatigue life of 32,625 cycles, and peaks at 350 J·m⁻² at 01:00, resulting in a reduced life of 29,933 cycles—reflecting a 9.0% temperature-induced fatigue life variation. The proposed model, which integrates non-uniform temperature gradients, offers enhanced accuracy in capturing complex boundary conditions and stress states, providing a more reliable tool for durability design and assessment of composite pavements. Full article

Civil engineering today faces the challenge of responding to climate change, rapid urbanization, and the need to reduce environmental impacts. These factors drive the search for more sustainable approaches and the adoption of digital technologies. This article addresses three principal dimensions: advanced low-impact materials, resilient structural designs, and digital tools applied throughout the infrastructure life cycle. To this end, a systematic search was conducted considering studies published between 2020 and 2025, including both experimental and review works. The results show that materials such as geopolymers, biopolymers, natural fibers, and nanocomposites can significantly reduce the carbon footprint; however, they still face regulatory, cost, and adoption barriers. Likewise, modular, adaptable, and performance-based design proposals enhance infrastructure resilience against extreme climate events. Finally, digital tools such as Building Information Modeling, digital twins, artificial intelligence, the Internet of Things, and 3D printing provide improvements in planning, construction, and maintenance, though with limitations related to interoperability, investment, and training. In conclusion, the integration of materials, design, and digitalization presents a promising pathway toward safer, more resilient, and sustainable infrastructure, aligning with the Sustainable Development Goals and the concept of smart cities. Full article

This paper addresses the underrepresentation of traffic activity in Life Cycle Assessment (LCA) practice despite its critical influence on the energy and environmental footprint of both electrified and conventional vehicles. To bridge this gap, the paper proposes a new framework that enhances the integration of traffic dynamics into fleet LCAs while maintaining computational simplicity. The approach combines Macroscopic Fundamental Diagrams (MFDs), which estimate network-level traffic performance, with an average-speed-based emissions model to evaluate on-road energy use and emissions performance of traffic. This quantification is further extended by applying life cycle inventory emission factors to account for upstream and downstream impacts, including energy production, vehicle manufacturing, and end-of-life treatment. The framework is demonstrated through a case study involving urban traffic networks in Zurich and Thessaloniki. Results illustrate the method’s capacity to evaluate multiple vehicles within realistic flow scenarios and adaptability to variable traffic conditions, offering a practical and scalable tool for improved energy and environmental assessment of road transport fleets. Full article

Climate change is intensifying the frequency and severity of extreme weather events, threatening the resilience of buildings and urban infrastructure. While technical solutions for climate adaptation in buildings are well documented, their economic viability remains a critical, yet underexplored, dimension of decision-making. This novel systematic review analyzes publications with an exclusive focus on climate adaptation strategies for buildings using cost-based evaluation methods. This review categorises the literature into three methodological clusters: Cost–Benefit Analysis (CBA), Life Cycle Costing (LCC), and alternative methods including artificial intelligence, simulation, and multi-criteria approaches. CBA emerges as the most frequently used and versatile tool, often applied to evaluate micro-scale flood protection and nature-based solutions. LCC is valuable for assessing long-term investment efficiency, particularly in retrofit strategies targeting energy and thermal performance. Advanced methods, such as genetic algorithms and AI-driven models, are gaining traction but face challenges in data availability and transparency. Most studies focus on residential buildings and flood-related hazards, with a growing interest in heatwaves, wildfires, and compound risk scenarios. Despite methodological advancements, challenges persist—including uncertainties in climate projections, valuation of non-market benefits, and limited cost data. This review highlights the need for integrated frameworks that combine economic, environmental, and social metrics, and emphasises the importance of stakeholder-inclusive, context-sensitive decision-making. Ultimately, aligning building adaptation with financial feasibility and long-term sustainability is achievable through improved data quality, flexible methodologies, and supportive policy instruments. Full article

Bridges play an increasingly indispensable role in endorsing the economic and social development of societies by linking highways and facilitating the mobility of people and goods. Concurrently, they are susceptible to high traffic volumes and an intricate service environment over their lifespans, resulting in undergoing a progressive deterioration process. Hence, efficient measures of maintenance, repair, and rehabilitation planning are critical to boost the performance condition, safety, and structural integrity of bridges while evading less costly interventions. To this end, this research paper furnishes a mixed review method, comprising systematic literature and scientometric reviews, for the meticulous examination and analysis of the existing research work in relation with maintenance fund allocation models of bridges (BriMai_all). With that in mind, Scopus and Web of Science databases are harnessed collectively to retrieve peer-reviewed journal articles on the subject, culminating in 380 indexed journal articles over the study period (1990–2025). In this respect, VOSviewer and Bibliometrix R package are utilized to create a visualization network of the literature database, covering keyword co-occurrence analysis, country co-authorship analysis, institution co-authorship analysis, journal co-citation analysis, journal co-citation, core journal analysis, and temporal trends. Subsequently, a rigorous systematic literature review is rendered to synthesize the adopted tools and prominent trends of the relevant state of the art. Particularly, the conducted multi-dimensional review examines the six dominant methodical paradigms of bridge maintenance management: (1) multi-criteria decision making, (2) life cycle assessment, (3) digital twins, (4) inspection planning, (5) artificial intelligence, and (6) optimization. It can be argued that this research paper could assist asset managers with a practical guide and a protocol to plan maintenance expenditures and implement sustainable practices for bridges under deterioration. Full article