Search Results (213)

This research has successfully addressed the challenges attributed with SMS, including the fragmented data, heavy reliance on experience, and lack of life cycle integration. This study presents the development and validation of a novel sustainable material selection (SMS) model using Artificial Intelligence (AI). The proposed model structures the process around four core life cycle phases—design, construction, operation and maintenance, and end of life—and incorporates a dual-interface system. This includes a main credits interface for high-level tracking of 100 total credits to trace the dynamics of SMS in relation to energy efficiency, indoor air quality, site selection, and efficient use of water. Further, it includes a detailed credit interface for granular assessment of specific material properties. A key innovation is the formalization of closed-loop feedback mechanisms between phases, ensuring that practical insights from construction and operation inform earlier design choices. The model’s functionality is demonstrated through a proof of concept for SMS considering thermal properties, showcasing its ability to contextualize benchmarks by climate, map properties to building components via a weighted networking system, and rank materials using a comprehensive database sourced from the academic literature. Automated scoring aligns with green building certification tiers, with an integrated alert system flagging suboptimal performance. The proposed model was validated through a structured practitioner survey, and the collected responses were analysed using descriptive and inferential statistical analysis. The result presents a scalable quantitative AI-assisted decision-making support model for optimizing material selection across different project phases. This work paves the way for further research with additional assessment criteria and better integration of AI and Machine Learning for SMS. Full article

Deploying public EV charging infrastructure while balancing efficiency, equity, and implementation feasibility remains a key challenge for sustainable urban mobility. This study develops an integrated, grid-based planning framework for Wuhan that combines attention-enhanced ConvLSTM demand forecasting with a trajectory-derived, rank-based accessibility index to support equitable network expansion. Using large-scale charging-platform status observations and citywide ride-hailing mobility traces, we generate grid-level demand surfaces and an accessibility layer that helps reveal structurally connected yet underserved areas, including demand-sparse zones that may be overlooked by utilization-only planning. We screen feasible grid cells to construct a new-station candidate set and formulate expansion as a constrained three-objective optimization problem solved by NSGA-II: maximizing demand-weighted neighborhood service coverage, minimizing the Group Parity Gap between low-accessibility populations and the citywide population, and minimizing grid-connection friction proxied by road-network distance to the nearest power substation. Practical deployment plans for 15 and 30 sites are selected from the Pareto set using TOPSIS under an explicit weighting scheme. Benchmarking against random selection and single-objective greedy baselines under identical candidate pools, constraints, and evaluation metrics demonstrates a persistent coverage–equity–cost tension: coverage-driven heuristics improve demand capture but worsen parity, whereas equity-prioritizing strategies reduce gaps at the expense of coverage and feasibility. Full article

This study investigates the tri-objective optimization of underground metro station structures, considering structural reliability, life-cycle economic cost, and annualized carbon emissions simultaneously. Using a representative metro station in Guangzhou as a case study, a multi-objective optimization framework is developed. The model defines structural failure probability, discounted life-cycle cost, and average annual carbon emissions as the primary objectives, with decision variables including concrete strength, cover thickness, the use of epoxy-coated reinforcement, and various maintenance/repair strategies. Material quantities are calculated through Building Information Modeling (BIM), while cost–carbon relationships are derived from industry price data and carbon emission factors. An improved multi-objective particle swarm optimization algorithm (OMOPSO) is used to derive the Pareto-optimal front. Case study results show that increasing cover thickness significantly improves durability and reduces carbon emissions with only moderate cost increases. In contrast, epoxy-coated reinforcement is excluded from the Pareto set due to its high cost under the given conditions. To facilitate practical decision-making, a weight-based solution selection method is introduced, and sensitivity analyses are performed to assess the model’s robustness. The study concludes by emphasizing the framework’s applicability and limitations: the findings are specific to the case context and require recalibration for use in other sites or construction practices. This research contributes by integrating durability, cost, and carbon considerations into an engineering-level optimization workflow, providing valuable decision support for sustainable metro station design. Full article

Compressing a mixture of soil, water, and stabilizer forms compressed stabilized earth blocks (CSEBs), a modernized earthen construction material capable of performance similar to that of engineered masonry with added sustainability achieved by usage of raw materials on-site, reduction in transportation costs of bulk materials to the build site, and improved thermal performance of built CSEB structures. CSEBs have a wide range of potential physical properties due to variations in base soil, mix composition, stabilizer, admixtures, and initial compression achieved in CSEB creation. While CSEB construction offers several opportunities to improve the sustainability of construction practices, assuring codifiable, standardized mix design for a target strength or durability remains a challenge as the mechanical character of the primary base soil varies from site to site. Quality control may be achieved through creation and testing of CSEB samples, but this adds time to a construction schedule. Such delays may be reduced through development of predictive CSEB compressive strength estimation models. This study experimentally determined CSEB compressive strength for six different mix compositions. Compressive strength predictive models were developed for 7-day and 28-day CSEB samples through multiple numerical models (i.e., linear regression, back-propagation neural network) designed and implemented to relate design inputs to 7-day and 28-day compressive strength. Model results provide insight into the predictive performance of linear regression and back-propagation neural networks operating on designed data streams. Performance, robustness, and significance of changes to the model dataset and feature set are characterized, revealing that linear regression outperformed neural networks on 28-day data and that inclusion of downstream data (i.e., cylinder compressive strength) did not significantly impact model performance. Full article

Construction logistics is central to optimising site operations and delivery processes, yet the need to meet dynamic site requirements while minimising transport movements presents a persistent challenge. Transport efficiency can be improved through both strategic and operational interventions at the business-unit level. This study examines transport-related distribution practices within the plasterboard supply chain in Auckland, New Zealand, and evaluates opportunities to enhance efficiency using established performance metrics. By integrating supply chain management and circular economy principles through spatial analysis and supply chain modelling, the research demonstrates the potential to achieve up to a three-fold improvement in vehicle capacity utilisation. The operational analysis—focused on general-purpose (non-specialist) transport—is grounded in real-world transport data that extends beyond conventional trip-centricity to capture a broader supply chain perspective. This approach addresses a key methodological gap by empirically validating analytical models in a specific operational context. In addition to quantifying efficiency gains, the study identifies context-specific inefficiencies that constrain construction transport performance and proposes sustainable solutions that extend beyond technological fixes. These include strategic organisational measures for improving fleet management, transport contracting and pricing, collaborative planning across supply chain actors, waste management practices, and collaborative logistics through integrated warehousing. By linking technical analysis with business-oriented insights, the research provides proof-of-concept for practical, scalable strategies for improved construction logistics and wider freight transport efficiency grounded in empirical evidence. Full article

Background: The healthcare sector plays a significant role in environmental degradation, particularly through energy consumption, emissions, and resource use associated with hospital operations. Despite growing global awareness of the impacts, environmental sustainability remains only partially embedded with the design, planning, management, and evaluation of hospital facilities, and empirical evidence is still limited. Methods: This exploratory study employed a mixed-method, two-phase approach. First, a scoping literature review identified key environmental dimensions and approaches for environmental sustainability in hospitals infrastructures. Second, a structured survey was distributed to Italian hospitals from Lombardy Region, between May and June 2024, to assess environmental performance and environmental strategy adoption. Results: Eight (n = 8) core environmental sustainability dimensions emerged from the review: energy efficiency, resource and waste management, transportation and mobility, materials and construction, environmental compliance, emissions, site sustainability, and design strategies. The subsequent based on these dimensions, gathered responses from (n = 18) healthcare facilities from Lombardy region, Italy. Findings revealed substantial gaps, since key measures such as on-site renewable capacity, water reuse systems, environmental certification application and health-island mitigation practices appear to be adopted sporadically. In addition, many of the surveyed facilities show consumption levels that exceed the benchmarks outlined in the literature. Discussion: The findings of this study reveal a notable misalignment between the sustainability debate, maturity promoted in the academic literature and the actual practices implemented in the Italian regional context. This mismatch highlights the importance of developing more uniform evaluation tools, policy requirements, and strengthening the organizational capabilities, to improve environmental performance in Italian hospital facilities. Full article

Grassland degradation threatens ecosystem function and livelihoods, especially in alpine regions where ecosystems are highly sensitive to disturbance. To compare the effectiveness of common restoration measures at fine spatial scales, we examined four household-level practices in the Ruoergai alpine grassland: year-round grazing exclusion (GE), seeding with grazing exclusion (SGE), seasonal grazing rest (GR), and balancing grazing capacity (BG). Using Sentinel-2 remote sensing data, we monitored vegetation dynamics (NDVI, EVI2, and NIRv) and applied a Propensity Score Matching–Difference-in-Differences (PSM–DID) framework, which constructs comparable control areas without any restoration measures and evaluates whether treatment sites experienced greater pre-to-post restoration changes than their matched controls, thereby strengthening causal inference. All four measures produced statistically significant pre-to-post increases in vegetation indices relative to their matched controls, with GE and SGE showing the largest DID-estimated effects. However, these DID-estimated gains did not persist beyond the implementation year, and in some cases (e.g., SGE, BG), the vegetation indices in treated areas fell below those of the controls, indicating limited persistence. GR and BG yielded smaller DID-estimated effects, reflecting the potential influence of socioeconomic incentives and regulatory challenges on restoration outcomes. These findings highlight the need for sustained management and incentive-aligned policies to maintain restoration benefits in alpine grasslands. Full article

Construction of new seawater reverse osmosis desalination (SWRO) plants in the state of California (USA) requires environmental permits containing rather strict conditions. The California Ocean Plan requires the use of subsurface intake systems (SSIs) unless they are deemed to be not feasible. The Governor of California requested that the State Water Resources Control Board (State Board) study the issue of accelerating the desalination plant permitting process and making it more efficient. The State Board formed an independent scientific Panel to study the issue of SSI feasibility and to submit a report. The Panel recommendations included the following: the feasibility assessment (FA) for SSIs should be streamlined for completion within a maximum of three years, and this requirement should be added to the Ocean Plan; applicants need to perform a financial feasibility study before pursuing SSI capacities exceeding 38,000 m³/d (10 MGD) for wells or 100,000 m³/d (25 MGD) for galleries because project financing may be denied for such larger capacity systems; the mitigation options for each site–SSI combination in the screening process should be addressed by both the project proponent and regulatory agencies as early as practicable in the overall permitting process; and the impacts of SSIs on local aquifers and associated wetland systems must be assessed during the analyses conducted during the FA and during post-construction monitoring. The Panel further concluded that the design and evaluation of SSI–site combinations are highly site-specific, involving technically complex issues, which require both the applicant and the reviewing state agencies to have the expertise to design and review the applications. Economic feasibility must consider cost to the consumer and the engineering risk that can preclude project financing. Projected capacities exceeding the above noted limits may not by financed due to risks of failure or could require government guarantees to lenders. The current permitting system in California is likely to preclude construction of large seawater desalination facilities that can provide another source of potable water for coastal communities in California during severe droughts. Without seawater desalination, the potable water supply in California would suffer a greater sustainability and resilience risk during future periods of extended drought. Full article

In the era of growing energy demand and the need to reduce greenhouse gas emissions, the development of renewable energy sources, including photovoltaic farms, is becoming a key element of a sustainable energy transition. In this context, the careful selection of cadastral plots on which farms can be built is crucial, as appropriate location influences the investment’s energy efficiency and minimizes environmental and planning risks. This article presents a proprietary methodology for identifying cadastral plots that are suitable for locating a photovoltaic farm. The presented methodology integrates the Fuzzy-AHP multi-criteria analysis method and the Fuzzy Membership fuzzy logic method, thereby reducing the subjectivity of expert assessments and improving the accuracy of estimating the values of factors considered in the research. A key element of the methodology is a detailed analysis of land and building register data, which results in the identification of specific plots with high investment potential. The multi-criteria analysis considered eight key factors related to climate, terrain, land cover, and cadastral data. Based on this, eight plots and 32 plot complexes were selected as the most suitable for the construction of PV farms. The most favorable locations were identified primarily in the eastern part of Częstochowa Poviat, as well as in the northern municipalities. The proposed methodology provides a ready-to-use, practical solution to the investment challenge of selecting specific cadastral plots for new solar investments. According to the reviewed literature, each of the 40 designated sites could support a photovoltaic farm of an estimated capacity of at least 1 MW. The obtained results provide significant input into the renewable energy investment planning process and emphasize that careful selection of plot locations is crucial for the investment’s success and the region’s sustainable energy transformation. Full article

Construction and demolition (C&D) waste remains a critical challenge in India due to accelerated urbanisation and material-intensive construction practices. This study integrates survey-based assessment with machine learning to identify key causes of C&D waste and recommend targeted minimization strategies. Data were collected from 116 professionals representing junior, middle, and senior management, spanning age groups from 20 to 60+ years, and working across building construction, consultancy, project management, roadworks, bridges, and industrial structures. The majority of respondents (57%) had 6–20 years of experience, ensuring representation from both operational and decision-making roles. The Relative Importance Index (RII) method was applied to rank waste causes and minimization techniques based on industry perceptions. To enhance robustness, Random Forest, Gradient Boosting, and Linear Regression models were tested, with Random Forest performing best (R² = 0.62), providing insights into the relative importance of different strategies. Findings show that human skill and quality control are most critical in reducing waste across concrete, mortar, bricks, steel, and tiles, while proper planning is key for excavated soil and quality sourcing for wood. Recommended strategies include workforce training, strict quality checks, improved planning, and prefabrication. The integration of perception-based analysis with machine learning offers a comprehensive framework for minimising C&D waste, supporting cost reduction and sustainability in construction projects. The major limitation of this study is its reliance on self-reported survey data, which may be influenced by subjectivity and regional bias. Additionally, results may not fully generalize beyond the Indian construction context due to the sample size and sectoral skew. The absence of real-time site data and limited access to integrated waste management systems also restrict predictive accuracy of the machine learning models. Nevertheless, combining industry perception with robust data-driven techniques provides a valuable framework for supporting sustainable construction management. Full article

Abandoned mines, as emblematic heritage spaces in the process of deindustrialization, preserve collective production memory and serve as vital symbols of local identity. However, current redevelopment practices primarily emphasize physical restoration while overlooking the visual expression and interactive communication of regional culture. This study introduces an augmented reality (AR)–based visual narrative framework that integrates regional culture to bridge the gap between spatial renewal and cultural regeneration. Drawing on semiotics and spatial narrative theory, a multidimensional “space–symbol–memory” translation mechanism is constructed, and a coupling model linking tangible material elements with intangible cultural connotations is established. Supported by technologies such as simultaneous localization and mapping (SLAM), semantic segmentation, and level of detail (LOD) rendering, a multilayer “position–perception–presentation” module system is designed to achieve stable anchoring of virtual and physical spaces and enable multilevel narrative interaction. Through task-oriented mechanisms and user co-creation, the system effectively enhances immersion, cultural identity, and learning outcomes. Experimental validation in a representative mine site confirms the feasibility of the proposed framework. While the study focuses on a single case, the modular and mechanism-based design indicates that the framework can be adapted to cultural tourism, educational communication, and community engagement applications. The key innovation lies in introducing an iterative “evidence–experience–co-creation” model, providing a new methodological reference for the digital reuse of abandoned mines and the sustainable preservation of industrial heritage. Full article

Characterized by excessively long working hours, high personnel turnover, and frequent off-site work, the construction industry renders construction workers highly vulnerable to Work–Family Conflict (WFC). This conflict not only exacerbates role tension but also depletes their self-control resources. However, existing studies focusing on WFC among construction workers remain scarce, with insufficient exploration into the underlying psychological mechanisms governing this phenomenon. Grounded in the Conservation of Resources (COR) theory, this study develops a theoretical model that identifies Self-Control Ability (SC) as the core mediator in the relationships between WFC and construction workers’ Job Satisfaction (JS) as well as Life Satisfaction (LS). By establishing a Structural Equation Model (SEM), we analyzed questionnaire data from 407 construction workers in Hunan Province, China. The results demonstrate that WFC exerts a direct negative effect on both JS and LS, while self-control ability plays a partial mediating role in these associations. These findings extend the application of Boundary Theory and Self-Control Theory to the context of specialized labor. Practically, they offer evidence-based insights for organizations to enhance worker well-being, including the design of psychological resource replenishment programs and the optimization of shift schedules, thereby contributing to the sustainable development of the construction industry. Full article

Neonicotinoids are a novel class of insecticides that exhibit environmental persistence and off-target effects on both humans and ecosystems. Therefore, there is a need for sensitive and selective sensors to monitor concentrations of neonicotinoids in environmental water and soil systems. Molecularly imprinted polymer (MIP)-based sensors are an emerging technology with strong potential for reliable, sensitive, and selective detection of neonicotinoids. Moreover, MIPs are versatile and compatible with a wide range of analytical techniques, which can further enhance measurement capabilities in the development of practical and robust sensors. Despite this promise, many routes remain underexplored for neonicotinoid detection. This review reports on the current state of neonicotinoid chemical sensors and detection methods using MIPs and highlights potential applications of MIP-based approaches as cost-effective and easy-to-operate solutions for monitoring neonicotinoids. Recent sensors incorporating MIPs and electrochemical or optical techniques for neonicotinoid detection are described and compared. Approaches employing magnetic solid-phase extraction and quartz crystal microbalance are also discussed. Additionally, the influence of monomer choice for MIP synthesis, as well as the use of additives and nanomaterials for sensor construction and analyte detection, is reviewed. These methods may promote sustainability, reusability, ratiometric or simultaneous detection of neonicotinoids, and sensor portability for on-site monitoring. Full article

Tetracycline (TC) is chemically stable and recalcitrant to natural degradation. Peroxymonosulfate (PMS)-based advanced oxidation processes offer an effective removal strategy, the efficacy of which relies on high-performance heterogeneous catalysts. Titanium dioxide (TiO₂) is an ideal material due to its stability and environmental compatibility, yet its practical application is hindered by inadequate PMS activation capacity, particle agglomeration, and difficult recovery. To address these limitations, a heterogeneous Fe/TiO₂ catalyst was constructed via Fe³⁺ doping, innovatively utilizing polyvinylpyrrolidone (PVP) as a structure-directing agent. PVP’s steric hindrance effectively suppressed nanoparticle agglomeration and enabled high dispersion of Fe active sites, simultaneously enhancing catalytic activity and stability. Under optimized conditions, the Fe/TiO₂/PMS system achieved 94.3% TC degradation, following pseudo-first-order kinetics and significantly outperforming pure TiO₂ used in this experimental system. Radical quenching verified sulfate radicals (SO₄•⁻) as the dominant species. The catalyst demonstrated excellent recyclability, retaining over 80% degradation efficiency after six cycles and enabling convenient magnetic separation. Moreover, in complex water matrices (tap water and seawater), it sustained high removal efficiency (>90% initially, >70% after six cycles), highlighting its superior anti-interference capability and practical potential. This work offers a strategic material design strategy for efficient and robust TC removal in challenging water environments. Full article

Despite preventive measures, the construction industry continues to exhibit high accident rates. In response, visual detection system was developed to support safety management on construction sites and promote sustainable working environments. The solution integrates the YOLOv8 algorithm with asynchronous video processing, incident registration, an open API, and a web-based interface. The system detects the absence of safety helmets (NHD) and worker falls (FD). Its low hardware requirements make it suitable for small and medium-sized construction enterprises, contributing to resource efficiency and digital transformation in line with sustainable development goals. This study advances practice by providing an integrated, low-resource solution that unites multi-hazard detection, event documentation, and system interoperability, addressing a key gap in existing research and implementations. The contribution includes an operational architecture proven to run in real time, addressing a gap between model-centred research and deployable, OHS applications. The system was validated using two independent test datasets, each comprising 100 images: one for NHD and one for FD. For NHD, the system achieved a precision of 0.93, an accuracy of 0.88, and an F1-score of 0.79. For FD, a precision of 1.00, though with a limited recall of 0.45. The results demonstrate the system’s potential for sustainable construction site safety monitoring. Full article