Developments in the Built Environment最新文献_第3页

An integrated methodology for assessing circularity and environmental sustainability in BIPV systems 一种评估BIPV系统循环性和环境可持续性的综合方法

IF 8.2 2区工程技术

Developments in the Built Environment Pub Date : 2025-09-03 DOI: 10.1016/j.dibe.2025.100746

Letizia Giusti , Marianna Rotilio , Gianni Di Giovanni , Pierluigi Bonomo , Leidy Guante Henriquez

{"title":"An integrated methodology for assessing circularity and environmental sustainability in BIPV systems","authors":"Letizia Giusti , Marianna Rotilio , Gianni Di Giovanni , Pierluigi Bonomo , Leidy Guante Henriquez","doi":"10.1016/j.dibe.2025.100746","DOIUrl":"10.1016/j.dibe.2025.100746","url":null,"abstract":"<div><div>The construction sector's shift toward sustainable production models calls for robust methods to assess both environmental performance and product circularity. This study presents an integrated framework combining Life Cycle Assessment (LCA) and Circularity Assessment (CA) to evaluate six Building-Integrated Photovoltaic (BIPV) systems. Developed through a structured six-phase process, the framework merges life cycle-based and circularity-based indicators to overcome the limitations of using each method in isolation. The results show that neither approach alone offers a comprehensive assessment, underscoring the value of integration. Circularity indicators are particularly effective in early design phases due to their simplicity, while LCA is crucial in later stages for validating strategies and optimizing performance. The methodology supports innovation in circular construction by providing insights into key performance indicators, contributing to more sustainable architecturally sound solutions. Overall, this work advances the integration of CA and LCA, offering practical recommendations and enabling future replication in complex system evaluations.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"23 ","pages":"Article 100746"},"PeriodicalIF":8.2,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010077","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}

引用次数: 0

A mobile receiver WiFi-CSI approach for fall detection of construction workers 一种用于建筑工人摔倒检测的移动接收器WiFi-CSI方法

IF 8.2 2区工程技术

Developments in the Built Environment Pub Date : 2025-08-28 DOI: 10.1016/j.dibe.2025.100745

Yinong Hu , Heng Li , Mingzhou Cheng , Mingyu Zhang , Shuai Han , Waleed Umer

{"title":"A mobile receiver WiFi-CSI approach for fall detection of construction workers","authors":"Yinong Hu , Heng Li , Mingzhou Cheng , Mingyu Zhang , Shuai Han , Waleed Umer","doi":"10.1016/j.dibe.2025.100745","DOIUrl":"10.1016/j.dibe.2025.100745","url":null,"abstract":"<div><div>This study introduces a novel fall detection method for construction workers that uses WiFi Channel State Information (CSI) with mobile smartphone receivers, which addresses the high incidence of fall-related injuries at construction sites. The innovative approach utilizes Doppler frequency shift features captured through mobile receivers, which adapt to dynamic construction environments where workers continuously move, overcoming limitations of conventional static configurations. Our framework extracts characteristic CSI patterns from WiFi signals and employs an improved deep learning model to classify falls and common construction activities. Experimental validation demonstrates robust performance with accuracy exceeding 93 % across various distances and orientations. The mobile receiver design significantly enhances spatial adaptability while providing a non-invasive, privacy-preserving, and cost-effective solution that can be readily deployed using existing WiFi infrastructure and workers’ smartphones for construction site safety monitoring.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"23 ","pages":"Article 100745"},"PeriodicalIF":8.2,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144916748","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}

引用次数: 0

Environmental and economic optimization of relocation strategies for mobile prefabrication factories in infrastructure projects 基础设施项目中移动装配厂搬迁策略的环境和经济优化

IF 8.2 2区工程技术

Developments in the Built Environment Pub Date : 2025-08-28 DOI: 10.1016/j.dibe.2025.100744

Jianxiang Ma , Jianpeng Cao , Lorenzo Benedetti , Andrea Revolti , Edwin Zea Escamilla , Guillaume Habert

{"title":"Environmental and economic optimization of relocation strategies for mobile prefabrication factories in infrastructure projects","authors":"Jianxiang Ma , Jianpeng Cao , Lorenzo Benedetti , Andrea Revolti , Edwin Zea Escamilla , Guillaume Habert","doi":"10.1016/j.dibe.2025.100744","DOIUrl":"10.1016/j.dibe.2025.100744","url":null,"abstract":"<div><div>Mobile off-site prefabrication can enhance complex linear infrastructure projects, yet the absence of a general and robust relocation rule limits its practical implementation in the construction industry. This research proposes an integrated model that combines Life Cycle Assessment and Geographic Information Systems to optimize a three-layer mobile supply network. A hyperloop infrastructure case study demonstrates that relocating a pneumatic mobile factory four times reduces carbon emissions by 62 % and costs by 49 % compared to a stationary facility, primarily due to shortened outbound transportation distances. Scenarios-based sensitivity analyses confirm the adaptability of mobile factories to supply diverse projects and recommend relocating the factory every 50–80 km to balance sustainability and practical feasibility. Although direct impacts from factory reconfigurations are modest, they serve as necessary constraints to prevent impractical relocation numbers. The model offers practical guidance for developing sustainable relocation strategies for mobile prefabrication factories used in large-scale infrastructure construction.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"24 ","pages":"Article 100744"},"PeriodicalIF":8.2,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048508","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}

引用次数: 0

Multifunctional wood composite with integrated phase change material for energy harvesting, self-healing, flame retardancy, and recycling 多功能木材复合材料与集成相变材料的能量收集，自愈，阻燃和回收

IF 8.2 2区工程技术

Developments in the Built Environment Pub Date : 2025-08-27 DOI: 10.1016/j.dibe.2025.100743

Emmanuel Igbokwe, Chengbin Yu, Guoqiang Li

{"title":"Multifunctional wood composite with integrated phase change material for energy harvesting, self-healing, flame retardancy, and recycling","authors":"Emmanuel Igbokwe, Chengbin Yu, Guoqiang Li","doi":"10.1016/j.dibe.2025.100743","DOIUrl":"10.1016/j.dibe.2025.100743","url":null,"abstract":"<div><div>Oriented strand boards have been widely used as sheath for roofing or dry walls in residential buildings. Residential buildings account for a large portion of energy consumption. Therefore, an oriented strand board with energy harvesting capability is highly desired. In this study, we prepared an oriented strand board by constructing sandwich structures. The face sheets of the sandwich are made of short wood fiber reinforced shape memory vitrimer with flame retardancy. The core of the sandwich is made of form-stable phase change material by impregnating paraffin wax into an open-cell polyurethane foam. Comprehensive characterizations were conducted on the sandwich composite. Key results demonstrated that the wood composites exhibited high mechanical strength, effective thermal regulation, complete delamination healing and penetration hole closing, and excellent recyclability. A model house was simulated to quantify energy absorption by the composite during the summertime, showing substantial thermal energy absorbed through the latent heat.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"23 ","pages":"Article 100743"},"PeriodicalIF":8.2,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144920163","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}

引用次数: 0

Ultrasonic tomography with deep learning for detecting embedded components and internal damage of concrete structures 基于深度学习的超声层析成像检测混凝土结构的嵌入构件和内部损伤

IF 8.2 2区工程技术

Developments in the Built Environment Pub Date : 2025-08-27 DOI: 10.1016/j.dibe.2025.100742

Inad Alqurashi , Mastour Alsulami , Ninel Alver , Necati Catbas

{"title":"Ultrasonic tomography with deep learning for detecting embedded components and internal damage of concrete structures","authors":"Inad Alqurashi , Mastour Alsulami , Ninel Alver , Necati Catbas","doi":"10.1016/j.dibe.2025.100742","DOIUrl":"10.1016/j.dibe.2025.100742","url":null,"abstract":"<div><div>Ultrasonic tomography is a powerful nondestructive technique for evaluating internal defects in concrete structures. This study presents a deep learning–enhanced approach utilizing a nanoscale object detection model to automate the localization and quantification of internal defects and embedded structural components, including reinforcement bars and ducts. Controlled concrete samples containing artificial defects of varying shapes and depths, along with embedded rebars and ducts, were designed. Ultrasonic signals were collected using a MIRA A1040 tomograph and reconstructed into 3D volumes via Synthetic Aperture Focusing Technique (SAFT). These volumes were converted into 2D slices and segmented using Chan-Vese segmentation and morphological post-processing. A partial histogram matching procedure unified color scales across segmented slices, minimizing color-related biases before model training. Segmentation-assisted labeling provided robust ground truth annotations, resulting in 7220 labeled images. The trained AI model accurately detected delaminations, rebars, and ducts (both grouted and ungrouted), achieving a mean Average Precision (<span><span><span>[email protected]</span></span><svg><path></path></svg></span>) of 0.73 and an Average Intersection-over-Union (IoU) of 0.80. Testing on real-world bridge data demonstrated the model's generalization to unseen conditions. Key innovations include automated segmentation-based labeling, robust color standardization via histogram matching, and a lightweight deep learning model optimized for real-time deployment on resource-constrained devices. This integrated approach has the potential to reduce manual interpretation and subjective variability, providing an effective, scalable NDT/E solution for rapid assessment and monitoring of concrete infrastructure through advanced ultrasonic imaging combined with standardized, machine learning-based defect detection.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"23 ","pages":"Article 100742"},"PeriodicalIF":8.2,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925141","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}

引用次数: 0

Revolutionizing sustainable construction through predictive modeling of green concrete 通过绿色混凝土的预测建模革新可持续建筑

IF 8.2 2区工程技术

Developments in the Built Environment Pub Date : 2025-08-26 DOI: 10.1016/j.dibe.2025.100740

Deep Saha, Biswajit Paul, Bijan Sarkar

{"title":"Revolutionizing sustainable construction through predictive modeling of green concrete","authors":"Deep Saha, Biswajit Paul, Bijan Sarkar","doi":"10.1016/j.dibe.2025.100740","DOIUrl":"10.1016/j.dibe.2025.100740","url":null,"abstract":"<div><div>Concrete is the most commonly used building material in the world, yet because of its enormous carbon emissions, other greenhouse gas emissions, and resource depletion during production, it has a major negative impact on the environment. In response, academic and business leaders have been creating cutting-edge substitutes for conventional concrete. Among them, green concrete has become a prominent sustainable option. This study presents a comparative modeling framework for predicting the compressive strength development of normal and green concrete using both linear and exponential regression techniques. Experimental strength data at 7, 14, and 28 days have been used to develop regression models for both concrete types. A linear regression model has been constructed for normal concrete, yielding a strong correlation (R<sup>2</sup> = 0.844), whereas green concrete, characterized by the delayed pozzolanic activity of supplementary cementitious materials, has been best represented by an exponential model. The exponential regression provided an excellent fit to green concrete strength data, capturing the nonlinear strength gain pattern typical of mixes incorporating fly ash and recycled aggregates. In addition, a Pareto analysis has been performed to identify the most critical curing periods contributing to strength development. Results show that approximately 86 % of the 28-day compressive strength in green concrete has been achieved within the first 14 days, emphasizing the importance of early-age curing and mix optimization. Overall, the study demonstrates how predictive modeling of strength development in green concrete not only supports more efficient mix optimization but also contributes to advancing sustainable construction practices by promoting the use of eco-friendly materials and data-driven decision-making.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"23 ","pages":"Article 100740"},"PeriodicalIF":8.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908696","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}

引用次数: 0

Digital guidework for augmented thin-tile vaulting construction 增强型薄瓦拱顶结构的数字导向系统

IF 8.2 2区工程技术

Developments in the Built Environment Pub Date : 2025-08-23 DOI: 10.1016/j.dibe.2025.100738

Robin Oval , Vittorio Paris , Rafael Pastrana , Edvard P.G. Bruun , Salvador Gomis Aviño , Sigrid Adriaenssens , Wesam Al Asali

{"title":"Digital guidework for augmented thin-tile vaulting construction","authors":"Robin Oval , Vittorio Paris , Rafael Pastrana , Edvard P.G. Bruun , Salvador Gomis Aviño , Sigrid Adriaenssens , Wesam Al Asali","doi":"10.1016/j.dibe.2025.100738","DOIUrl":"10.1016/j.dibe.2025.100738","url":null,"abstract":"<div><div>Masonry vaults are mechanically efficient structures but deemed uneconomical because of falsework construction. Even a craft like thin-tile vaulting, which does not require centering to support the vault during construction, needs time-consuming guidework to aid the builders follow the vault’s geometry. However, this visual support can be digitized, using augmented reality to create digital guidework. The proposed methodology provides a framework that empowers vault builders to remain in control of their analog craft by providing only the right digital visual information. This methodology was developed through a preliminary prototype that led to a demonstrator built in an uncontrolled outdoor environment. Construction results showed productivity gain around 30% in terms of time, and shape accuracy under 1% of the span. The static holographic projection of the guidework could be extended in future research into an interactive aid, through mixed reality for further construction productivity and accuracy, as well as for training and design.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"24 ","pages":"Article 100738"},"PeriodicalIF":8.2,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048509","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}

引用次数: 0

High impact resistance with less material: a comparative study of Kagome truss and conventional rebar in concrete panels 用更少的材料提高抗冲击性：Kagome桁架与混凝土面板中常规钢筋的比较研究

IF 8.2 2区工程技术

Developments in the Built Environment Pub Date : 2025-08-22 DOI: 10.1016/j.dibe.2025.100741

Se-Eon Park , Eunyoung Hwang , Jeong-Il Choi , Bang Yeon Lee

{"title":"High impact resistance with less material: a comparative study of Kagome truss and conventional rebar in concrete panels","authors":"Se-Eon Park , Eunyoung Hwang , Jeong-Il Choi , Bang Yeon Lee","doi":"10.1016/j.dibe.2025.100741","DOIUrl":"10.1016/j.dibe.2025.100741","url":null,"abstract":"<div><div>This study experimentally validated a three-dimensional Kagome truss as a highly efficient alternative to conventional rebar for enhancing the projectile impact resistance of concrete panels. In this experiment, three types of panels with identical dimensions were fabricated: conventional RC panel (CP-2), RC panel with increased rebar (CP-4), and Kagome truss reinforced concrete panel (CP-K). High-velocity projectile impact tests were conducted to analyze the failure patterns on the front and rear faces, penetration depth, mass loss ratio, and damaged area of each panel. The experimental results showed that the CP-K panel withstood a greater number of impacts before perforation compared to the CP-2 and CP-4 panels. Furthermore, the CP-K panel exhibited smaller front and rear failure zones, less penetration depth, and a lower mass loss ratio. Notably, despite using a lower weight of reinforcement than the CP-4 panel, the Kagome truss-reinforced CP-K panel demonstrated superior impact resistance, establishing its potential as a material-efficient alternative for protective structures.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"23 ","pages":"Article 100741"},"PeriodicalIF":8.2,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896678","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}

引用次数: 0

Explosive dynamic response of steel-reinforced concrete-filled steel tubular columns under fire conditions 火灾条件下钢管混凝土柱的爆炸动力响应

IF 8.2 2区工程技术

Developments in the Built Environment Pub Date : 2025-08-21 DOI: 10.1016/j.dibe.2025.100739

Chengquan Wang , Tengfang Dong , Xinquan Wang , Weiwei Zhang , Xiao Li , Hongguo Diao , Yukai Fang

引用次数: 0

Compressive strength prediction of fly ash/slag-based geopolymer concrete using EBA-optimised chemistry-informed interpretable deep learning model 利用eba优化的化学信息可解释深度学习模型预测粉煤灰/矿渣基地聚合物混凝土的抗压强度

IF 8.2 2区工程技术

Developments in the Built Environment Pub Date : 2025-08-20 DOI: 10.1016/j.dibe.2025.100736

Yang Yu , Iman Munadhil Abbas Al-Damad , Stephen Foster , Ali Akbar Nezhad , Ailar Hajimohammadi

{"title":"Compressive strength prediction of fly ash/slag-based geopolymer concrete using EBA-optimised chemistry-informed interpretable deep learning model","authors":"Yang Yu , Iman Munadhil Abbas Al-Damad , Stephen Foster , Ali Akbar Nezhad , Ailar Hajimohammadi","doi":"10.1016/j.dibe.2025.100736","DOIUrl":"10.1016/j.dibe.2025.100736","url":null,"abstract":"<div><div>Geopolymer concrete (GPC) is a sustainable alternative to conventional Portland cement concrete, utilising industrial by-products like fly ash (FA) and ground-granulated blast-furnace slag (GGBS). However, optimising GPC's compressive strength (CS) often requires costly and time-consuming experimental trials. This study develops a deep learning (DL) model based on convolutional neural networks (CNN) to predict the CS of FA/GGBS-based GPC. The model integrates key mix parameters such as material proportions, curing conditions, and the chemical composition of FA/GGBS binders, making it chemistry-informed. The CNN architecture includes two convolution layers, global max-pooling, and two fully connected layers, with 11 input variables and a single output for CS prediction. To optimise model accuracy, the enhanced bat algorithm (EBA) is designed for metaparameter tuning. The model is trained and tested on a comprehensive dataset comprising experimental data extracted from published literature. The results demonstrate that the EBA-optimised CNN outperforms traditional learning models, including support vector machine (SVM), extreme gradient boosting (XGBoost), and artificial neural networks (ANN), with higher performance in terms of R<sup>2</sup>, MAE, and RMSE. The model achieved R<sup>2</sup> values of 0.997 for training and 0.978 for testing. Additionally, the Shapley additive explanations (SHAP) method was used to interpret the model, identifying the Na<sub>2</sub>O to binder ratio and curing age as the most influential factors on CS. This study highlights the potential of DL techniques, particularly chemistry-informed CNN with metaparameter optimisation, for accurately predicting the strength of GPC, providing a cost-effective solution for mix design and performance evaluation.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"23 ","pages":"Article 100736"},"PeriodicalIF":8.2,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893845","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}

引用次数: 0