Developments in the Built Environment最新文献

{"title":"Valorization of spent coffee grounds and casein for a new bio-composite material","authors":"Amira Mansour Khodja ,&nbsp;Prosper Pliya ,&nbsp;Souhila Rehab Bekkouche ,&nbsp;Javad Eslami","doi":"10.1016/j.dibe.2026.100872","DOIUrl":"10.1016/j.dibe.2026.100872","url":null,"abstract":"<div><div>This study investigates the potential of biopolymers derived from spent coffee grounds (SCG) and casein (Cas) for structural foundation. SCG was adopted for its sand-like physical properties, while Cas served as a binder to enhance mechanical performance. A central composite design (CCD) was used considering three factors: Cas content, NaOH concentration, and curing temperature. Mechanical and statistical analyses revealed that the bio-composite achieved a maximum unconfined compressive strength (UCS) of 7.7 MPa under optimal conditions, with curing temperature being the most influential factor. Linear regression models between secant modulus (E50) and UCS highlighted the enhancement of rigidity with curing time. Fired brick waste (FBW), rich in aluminosilicates, was incorporated into some mixtures, but its limited reactivity reduced interfacial bonding. Microstructural observations confirmed the formation of a dehydrated biopolymer gel coating SCG particles. Overall, the SCG–Cas system appears to be a promising sustainable material for circular economy applications.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"25 ","pages":"Article 100872"},"PeriodicalIF":8.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-carbon ternary binder bio-composites via accelerated carbonation for circular construction","authors":"Hossein Rahmani ,&nbsp;Hamed Rahimpour ,&nbsp;Mohammad Reza Hanafi ,&nbsp;Augonis Algirdas ,&nbsp;Sahar Zinatloo-Ajabshir","doi":"10.1016/j.dibe.2026.100874","DOIUrl":"10.1016/j.dibe.2026.100874","url":null,"abstract":"<div><div>This study demonstrates the significance of coupling biomass valorization with accelerated carbonation curing (ACC) as an integrated strategy for low-carbon construction materials. A bio-based ternary binder incorporating wood sawdust with shale ash, steel slag, and a reduced proportion of ordinary Portland cement (OPC) was developed to simultaneously enhance mechanical performance and enable CO₂ sequestration. The optimized formulation (25% shale ash, 15% OPC, 10% slag, and 50% sawdust) achieved a compressive strength of 7.2 MPa and an elastic modulus of 6 GPa, exceeding the performance of comparable wood-cement composites while using substantially less clinker. Microstructural analyses (SEM, XRD, FTIR, and XPS) confirmed portlandite depletion and the formation of calcite and low-Ca/Si C-(A)-S-H phases under ACC, resulting in matrix densification. Life cycle assessment (EN 15804 +A2) indicated a 65% reduction in greenhouse gas emissions relative to OPC-based systems, highlighting the combined structural and environmental benefits of the proposed approach.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"25 ","pages":"Article 100874"},"PeriodicalIF":8.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk-aware robotic infection control: Integrating computer vision and physics-informed decision-making for resilient ventilation in offices","authors":"Zhihao Ren ,&nbsp;Xin Li ,&nbsp;Shihui Ma ,&nbsp;Jung In Kim","doi":"10.1016/j.dibe.2026.100883","DOIUrl":"10.1016/j.dibe.2026.100883","url":null,"abstract":"<div><div>This study proposes a risk-aware robotic personalized ventilation framework that adaptively relocates a mobile air purifier to reduce airborne infection risk in offices. The system integrates computer vision-based occupancy sensing, a physics-informed surrogate model that predicts infection risk by learning single-source computational fluid dynamics (CFD) scalar transport fields and reconstructing multi-occupant conditions via linear superposition, and uncertainty-aware decision-making under infection-source uncertainty among detected occupants. Evaluated in a university meeting room, the surrogate achieves a mean absolute error of 0.77% in infection probability on blind test scenarios, significantly outperforming purely data-driven algorithms. Across 255 occupancy configurations, results reveal a Pareto conflict between aggregate cleaning efficiency and individual safety equity, motivating an adaptive planner that switches between risk-neutral and risk-averse policies. Compared with static baselines, the proposed strategy reduces mean risk by 35.5% and up to 74% in worst-case exposure. On-site prototype trials demonstrate a scalable, low-latency solution for upgrading indoor safety.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"25 ","pages":"Article 100883"},"PeriodicalIF":8.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Circular and sustainable smart textiles for buildings and living: Challenges, pathways, and perspectives","authors":"Urszula Stachewicz ,&nbsp;Vaida Jonaitienė ,&nbsp;Jan K. Kazak ,&nbsp;Georgios Priniotakis ,&nbsp;Joanna Knapczyk-Korczak ,&nbsp;Enrico Venturini ,&nbsp;Leonarda Francesca Liotta ,&nbsp;Joost van Hoof","doi":"10.1016/j.dibe.2026.100884","DOIUrl":"10.1016/j.dibe.2026.100884","url":null,"abstract":"<div><div>Over the past three decades, smart or functional textiles have gradually been introduced to the sectors of building and living, fulfilling a plethora of new applications. Although their show great promise for current and future applications in these domains, their application is often limited and hampered by a significant number of factors. Among these factors, a couple stand out in terms of their significance: sustainability and circularity, and functionalization (interactivity), including certification and standardization. A roadmap in these domains is proposed to remove barriers to the implementation and application of smart textiles in buildings and living. This is done by examining the state of the art in smart textile technology, analyzing the developments, setting the ambitions for the future, investigating the feasibility of options, and making choices and setting priorities. The roadmap can be used by industry and the wider community to make smart textiles a societal and commercial success story.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"25 ","pages":"Article 100884"},"PeriodicalIF":8.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable machine learning framework for performance-based retrofit scheme of blast-damaged reinforced concrete columns","authors":"Yeeun Kim ,&nbsp;Kihak Lee ,&nbsp;Jiuk Shin","doi":"10.1016/j.dibe.2026.100847","DOIUrl":"10.1016/j.dibe.2026.100847","url":null,"abstract":"<div><div>Explainable artificial intelligence (xAI) has been widely used to improve learning performance because it helps users understand the learning processes. This paper proposes an xAI-based framework to build retrofit schemes for blast-damaged RC columns. This framework includes a multi-stage learner rapidly predicting blast resistance levels using simple structural details. The extensive data for the blast resistance was analyzed with a three-step interpreting process: (1) partial dependence plot (PDP) to initially judge whether the retrofit is effective, (2) 1D accumulated local effect (ALE) to set the quantitative retrofit thresholds for ductility- and stiffness-related variables, and (3) 2D ALE to build effective retrofit schemes considering the interactive effects of retrofit variables on blast resistance. Based on the interpretation results, the various retrofit schemes were recommended for the column failure types and expected damage conditions. Overall, multiple retrofit schemes were required for the columns to accommodate the expected severe and moderate damage conditions.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"25 ","pages":"Article 100847"},"PeriodicalIF":8.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Material- and stock-level environmental impact assessment of two circular carbon reduction strategies applied to the European building sector","authors":"Giulia Pristerà ,&nbsp;Nicolas Alaux ,&nbsp;Alexander Passer ,&nbsp;Karen Allacker","doi":"10.1016/j.dibe.2026.100885","DOIUrl":"10.1016/j.dibe.2026.100885","url":null,"abstract":"<div><div>The uptake of renewable energy sources (RES) and recycled content (RC) in the manufacturing of nine construction materials is modelled at the material and building stock level, evaluating 19 environmental indicators with an LCA approach. The goal is to assess the effectiveness of these circular measures and gain insight into how different modelling approaches might affect the results. In the RES scenario, the impact reduction is similar for all assessed materials, while in the RC scenario it varies among the materials. This conclusion holds for the two modelling approaches used. The more detailed modelling approach revealed trade-offs between the environmental indicators, such as the increase in abiotic resource depletion impacts in the RES scenario. By adopting a consistent modelling approach and considering a broad range of impact categories, this work sets the foundations for comprehensive stock-level assessments of these measures, within the context of scenario analysis for policy support.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"25 ","pages":"Article 100885"},"PeriodicalIF":8.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Durability of slag-fly ash blended geopolymer composites incorporating reject brine waste","authors":"Abdulkader El-Mir ,&nbsp;Joud Hwalla ,&nbsp;Joseph J. Assaad ,&nbsp;Amr El-Dieb ,&nbsp;Hilal El-Hassan","doi":"10.1016/j.dibe.2025.100836","DOIUrl":"10.1016/j.dibe.2025.100836","url":null,"abstract":"<div><div>This study investigates the incorporation of rejected brine waste (BW) into geopolymer (GP) composites made of different binder blends of blast furnace slag (BFS) and fly ash (FA), while varying the alkali-activated solution-to-binder (A/B) ratio. The mechanical and durability performance of the GP mortars was assessed through various tests, including compressive strength, bulk resistivity, water absorption, sorptivity, accelerated carbonation, and resistance to sulfuric and hydrochloric acid attacks. Microstructure characterization was carried out to evaluate the changes to the mineralogy due to acid exposure. Results indicated that the BW-based GP mixture composed of 100 % BFS with an A/B ratio of 0.55 exhibited superior mechanical and durability performance among all BW-based mixes. Furthermore, BW integration into the GP mortar had a limited impact on its performance, morphology, and durability and caused insignificant leaching of sodium, chloride, and sulfate ions. Yet, the electrical conductivity was marginally improved (up to 7 %) due to BW incorporation. Sulfuric acid exposure revealed the vulnerability of FA-rich mixes, with extensive gypsum formation and structural degradation. Compared to hydrochloric acid, GP composites exposed to sulfuric acid experienced up to 1.5 and 3 times less mass and strength retention, respectively. Microstructure analysis revealed the deterioration of crystalline and amorphous phases in GP composites having higher FA and alkaline solution contents upon acid exposure. These findings provide evidence for the ability to replace potable water with BW and cement with GP binders while also recycling industrial waste in cement-free composites.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"25 ","pages":"Article 100836"},"PeriodicalIF":8.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An interpretable machine learning framework for residential building hourly operational carbon emission prediction with indoor and outdoor environment","authors":"Qiansheng Fang ,&nbsp;Wentao Li ,&nbsp;Fukang Sun","doi":"10.1016/j.dibe.2025.100837","DOIUrl":"10.1016/j.dibe.2025.100837","url":null,"abstract":"<div><div>Operational carbon emission prediction is the essential component of building carbon emission management. Hence, an interpretable machine learning framework is proposed for residential building hourly operational carbon emission prediction with indoor and outdoor environment parameters. In the proposed framework, a dynamic correlation graph is introduced to structure building carbon emission and environment parameter time series, which used to represent the relation among environment parameters and building carbon emission. The TCN-GCN algorithm and SHAP method are utilized to construct the hourly operational carbon emission prediction model for residential buildings. To validate the feasibility and effectiveness of the proposed method, case study are implemented with the experiment dataset from Smartline project in Cornwall, UK. The experiment results demonstrate the proposed DCG-TCN-GCN method can accurately predict residential building operational carbon emission with 0.7578–0.8416 Corr, and 0.1288–0.1827 RSE. The research results can provide benefits to more sustainable energy-efficient and low-carbon residential buildings.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"25 ","pages":"Article 100837"},"PeriodicalIF":8.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Causal and correlation analysis of high-rise building fires using text mining and ISM: Evidence from China","authors":"Fuyi Yao ,&nbsp;Jialuo Du ,&nbsp;Yingbo Ji ,&nbsp;Wenjing Tong ,&nbsp;Yangyang Leng","doi":"10.1016/j.dibe.2026.100870","DOIUrl":"10.1016/j.dibe.2026.100870","url":null,"abstract":"<div><div>Research on high-rise building fire causal mechanisms remains limited. This study proposes a comprehensive fire cause analysis model for high-rise buildings by integrating text mining with the interpretive structural modelling (ISM) method. Based on text mining of a Chinese dataset containing 123 fire accident investigation reports from 2000 to 2024, 16 causes of high-rise building fire causes were objectively identified. Using UCINET analysis, 14 key causes were extracted, and the Apriori algorithm was applied to reveal 14 strong correlations among them. Subsequently, the ISM method was employed to structure these causes into six hierarchical levels. Results indicate that insufficient fire laws and policies, together with inadequate administrative management, are the fundamental root causes. At the uppermost level, direct causes include fire-related human activities, electrical malfunctions, and defects in fire protection facilities. The findings enhance the objectivity of fire cause identification and provide valuable insights for fire prevention and in high-rise buildings.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"25 ","pages":"Article 100870"},"PeriodicalIF":8.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable machine learning-aided prediction of steel corrosion in concrete using advanced multi-scale feature selection and optimization techniques","authors":"Jin-Yang Gui ,&nbsp;Zhao-Hui Lu ,&nbsp;Chun-Qing Li","doi":"10.1016/j.dibe.2026.100873","DOIUrl":"10.1016/j.dibe.2026.100873","url":null,"abstract":"<div><div>Corrosion of reinforcing steel in concrete structures, especially in chloride-rich environments, remains a leading cause of structural degradation and presents significant challenges for maintenance. Traditional steel corrosion inspection techniques, both direct and indirect, often fall short in terms of accuracy, efficiency, and cost-effectiveness. In this paper, an interpretable machine learning (ML)-aided framework is developed for predicting steel corrosion degree in concrete, which integrates multi-scale feature selection (MSFS), optimal algorithm determination, and SHapley Additive exPlanations (SHAP) techniques, addressing key limitations of conventional ML models, such as limited feature selection, poor generalization, and black-box opacity. The learning capability of the computational model is verified through extensive comparisons with multiple baseline algorithms. A digital example is presented to demonstrate the accuracy and efficiency of the developed framework. From the example, it is found that the MSFS method can identify key features of steel corrosion, such as crack width (<em>w</em>), geometric ratios (<em>c</em>_<em>b</em>/<em>d</em>, <em>c</em>_<em>l</em>/<em>d</em>, <em>c</em>_<em>b</em>/<em>c</em>_<em>l</em>), and concrete properties (<em>f</em>_<em>c</em>, <em>W</em>/<em>C</em>). This ensures an optimal balance between accuracy and generalization, as validated by 5-fold cross-validation and independent dataset testing, with the optimal model achieving a test set <em>R</em>² of 0.94 and a 33.6% reduction in RMSE compared to the default model. It is also found that the SHAP technique can further vindicate <em>w</em> and <em>c</em>_<em>b</em>/<em>d</em> as the most influential factors governing internal corrosion. This paper pioneers the ML computational models for the prediction of structural deterioration, e.g., steel corrosion in concrete, which can replace traditional mathematical model-based prediction. These innovations represent a significant step toward the future of digital-driven structural performance prediction.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"25 ","pages":"Article 100873"},"PeriodicalIF":8.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}