Pub Date : 2026-01-15DOI: 10.1016/j.dibe.2026.100854
Young-Eun Lee , Donghan Lee , Jihye Sung , Ilhwan You , Dongwhi Choi , Seung-Jung Lee
This study presents the systematic optimization and validation of a triboelectric nanogenerator (TENG) designed for practical application by addressing two key challenges: balancing mechanical strength with electrical conductivity and establishing system-level validation. Carbon black (CB), carbon nanotubes (CNTs), and carbon fibers (CFs) were incorporated at varying contents to determine the optimal composition. Incorporation of 0.5 vol% CNT yielded the optimal performance, achieving stable conductivity without compromising mechanical strength. Based on this optimized cement-based composite (CBC), a TENG system was fabricated consisting of a CBC electrode, a polydimethylsiloxane (PDMS) contact layer, and a nylon counter layer, which generated the highest average peak voltage of 22.4 V. Output performance was evaluated under different loads, excitation frequencies, and contact areas, with the device delivering a peak power of 3.364 μW at an optimal load resistance of 40 MΩ. Practical feasibility was demonstrated by powering a low-power electronic device. These findings highlight an optimized CBC-TENG design that integrates structural integrity with efficient energy harvesting, advancing the readiness of cement-based self-powered systems and offering a viable pathway for its integration into sustainable civil infrastructure.
{"title":"Study of energy harvesting from conductive cement nanocomposites using a triboelectric nanogenerator","authors":"Young-Eun Lee , Donghan Lee , Jihye Sung , Ilhwan You , Dongwhi Choi , Seung-Jung Lee","doi":"10.1016/j.dibe.2026.100854","DOIUrl":"10.1016/j.dibe.2026.100854","url":null,"abstract":"<div><div>This study presents the systematic optimization and validation of a triboelectric nanogenerator (TENG) designed for practical application by addressing two key challenges: balancing mechanical strength with electrical conductivity and establishing system-level validation. Carbon black (CB), carbon nanotubes (CNTs), and carbon fibers (CFs) were incorporated at varying contents to determine the optimal composition. Incorporation of 0.5 vol% CNT yielded the optimal performance, achieving stable conductivity without compromising mechanical strength. Based on this optimized cement-based composite (CBC), a TENG system was fabricated consisting of a CBC electrode, a polydimethylsiloxane (PDMS) contact layer, and a nylon counter layer, which generated the highest average peak voltage of 22.4 V. Output performance was evaluated under different loads, excitation frequencies, and contact areas, with the device delivering a peak power of 3.364 μW at an optimal load resistance of 40 MΩ. Practical feasibility was demonstrated by powering a low-power electronic device. These findings highlight an optimized CBC-TENG design that integrates structural integrity with efficient energy harvesting, advancing the readiness of cement-based self-powered systems and offering a viable pathway for its integration into sustainable civil infrastructure.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"25 ","pages":"Article 100854"},"PeriodicalIF":8.2,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077669","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}
Pub Date : 2026-01-14DOI: 10.1016/j.dibe.2026.100852
Ke Wu , Haihang Hu , Huakai Sun , Kai Zhu , Xingfu Yu , Ye Jin , Tianhang Zhang
Reliable recognition of evacuation signage under low-visibility conditions is vital for occupant safety. This study investigates the impact of chroma differences (ΔC∗) on visual recognition and introduces a perception-based model tailored for supra-threshold tasks. Through psychophysical testing, recognition performance was quantified using Color Visual Acuity (CVA) across varying brightness, chroma, and hue conditions. Results reveal that CVA decreases with increasing chroma due to perceptual saturation and varies significantly with hue, particularly reduced near yellow (90°) due to S-cone sensitivity limitations. Brightness (L∗) consistently enhances CVA across all conditions. A novel Perceived Color Difference (PCD) model was developed, based on spectral radiance differences weighted by human chromatic sensitivity. The model exhibits a robust logarithmic correlation with CVA, outperforming traditional ΔE metrics, which are optimized for near-threshold color discrimination rather than recognition. A dual-threshold criterion, CVA ≥4.0 and PCD ≥0.0005, is recommended to ensure effective recognition in safety-critical environments. The findings support the design of more effective evacuation signage by linking human visual responses to lighting conditions in low-visibility environments.
{"title":"Experimental study on human visual response to safety signage under emergency lighting conditions","authors":"Ke Wu , Haihang Hu , Huakai Sun , Kai Zhu , Xingfu Yu , Ye Jin , Tianhang Zhang","doi":"10.1016/j.dibe.2026.100852","DOIUrl":"10.1016/j.dibe.2026.100852","url":null,"abstract":"<div><div>Reliable recognition of evacuation signage under low-visibility conditions is vital for occupant safety. This study investigates the impact of chroma differences (Δ<em>C</em>∗) on visual recognition and introduces a perception-based model tailored for supra-threshold tasks. Through psychophysical testing, recognition performance was quantified using Color Visual Acuity (CVA) across varying brightness, chroma, and hue conditions. Results reveal that CVA decreases with increasing chroma due to perceptual saturation and varies significantly with hue, particularly reduced near yellow (90°) due to S-cone sensitivity limitations. Brightness (<em>L</em>∗) consistently enhances CVA across all conditions. A novel <strong><u>P</u></strong>erceived <strong><u>C</u></strong>olor <strong><u>D</u></strong>ifference (PCD) model was developed, based on spectral radiance differences weighted by human chromatic sensitivity. The model exhibits a robust logarithmic correlation with CVA, outperforming traditional ΔE metrics, which are optimized for near-threshold color discrimination rather than recognition. A dual-threshold criterion, CVA ≥4.0 and PCD ≥0.0005, is recommended to ensure effective recognition in safety-critical environments. The findings support the design of more effective evacuation signage by linking human visual responses to lighting conditions in low-visibility environments.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"25 ","pages":"Article 100852"},"PeriodicalIF":8.2,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077666","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}
Pub Date : 2026-01-12DOI: 10.1016/j.dibe.2026.100847
Yeeun Kim , Kihak Lee , Jiuk Shin
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.
{"title":"Interpretable machine learning framework for performance-based retrofit scheme of blast-damaged reinforced concrete columns","authors":"Yeeun Kim , Kihak Lee , 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":"2026-01-12","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}
Pub Date : 2026-01-08DOI: 10.1016/j.dibe.2026.100843
Yilin Wang , Yikun Su , Zhizhe Zheng , Zhichao Zhou , Xing Wang
High dust concentrations from road construction degrade air quality, threaten human health, and increase machinery wear and fuel use. Accurate prediction of dust concentrations is therefore critical for proactive environmental control and low-carbon construction. This study proposes a Bayesian-optimized neural network model that integrates spatial, temporal, and environmental information from multi-source data, including particulate sensors, meteorological parameters, and construction records. The convolutional neural network (CNN) captures spatial features, the long short-term memory (LSTM) learns temporal dependencies, and Bayesian optimization (BO) automatically tunes hyperparameters to enhance prediction performance. The proposed model achieves high accuracy (R2 = 0.884) and exhibits superior short-term and long-term robustness compared with conventional models. These results demonstrate that the BO-CNN-LSTM framework effectively improves dust prediction accuracy and stability, providing a practical and intelligent tool for dust mitigation, energy-efficient scheduling, and carbon reduction in road construction projects.
{"title":"Bayesian-optimized CNN-LSTM neural network for predicting road construction dust concentrations","authors":"Yilin Wang , Yikun Su , Zhizhe Zheng , Zhichao Zhou , Xing Wang","doi":"10.1016/j.dibe.2026.100843","DOIUrl":"10.1016/j.dibe.2026.100843","url":null,"abstract":"<div><div>High dust concentrations from road construction degrade air quality, threaten human health, and increase machinery wear and fuel use. Accurate prediction of dust concentrations is therefore critical for proactive environmental control and low-carbon construction. This study proposes a Bayesian-optimized neural network model that integrates spatial, temporal, and environmental information from multi-source data, including particulate sensors, meteorological parameters, and construction records. The convolutional neural network (CNN) captures spatial features, the long short-term memory (LSTM) learns temporal dependencies, and Bayesian optimization (BO) automatically tunes hyperparameters to enhance prediction performance. The proposed model achieves high accuracy (R<sup>2</sup> = 0.884) and exhibits superior short-term and long-term robustness compared with conventional models. These results demonstrate that the BO-CNN-LSTM framework effectively improves dust prediction accuracy and stability, providing a practical and intelligent tool for dust mitigation, energy-efficient scheduling, and carbon reduction in road construction projects.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"25 ","pages":"Article 100843"},"PeriodicalIF":8.2,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977702","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}
The drying of concrete has been recognized as a key phenomenon in the deterioration of concrete structures. Nevertheless, the complex conditions of real RC structures may lead to unforeseen responses observed from existing laboratory experiments on RC members. To clarify the influence of drying, a quasi-static cyclic loading experiment was conducted for one-third scale, three-story RC buildings under wet (saturated) and two-year-dried conditions. The significant decrease in the initial stiffness emphasized the influence of drying on the structural performance regarding residual stress and drying shrinkage cracks affecting the stress-transferring process. In addition, the different deformations of the frame structure indicated the influence of drying on the failure mode. The localized damage occurred early in the wet specimen due to the stress concentration. By contrast, the dried specimen showed only distributed damage during the same cycle. These influences emphasize the impact of drying, which should not be neglected in structural designs.
{"title":"Experimental investigation on the influence of drying on the seismic performance of three-story RC buildings","authors":"Puttipong Srimook , Tatsuya Asai , Masaomi Teshigawara , Pranjal Satya , Ippei Maruyama","doi":"10.1016/j.dibe.2026.100846","DOIUrl":"10.1016/j.dibe.2026.100846","url":null,"abstract":"<div><div>The drying of concrete has been recognized as a key phenomenon in the deterioration of concrete structures. Nevertheless, the complex conditions of real RC structures may lead to unforeseen responses observed from existing laboratory experiments on RC members. To clarify the influence of drying, a quasi-static cyclic loading experiment was conducted for one-third scale, three-story RC buildings under wet (saturated) and two-year-dried conditions. The significant decrease in the initial stiffness emphasized the influence of drying on the structural performance regarding residual stress and drying shrinkage cracks affecting the stress-transferring process. In addition, the different deformations of the frame structure indicated the influence of drying on the failure mode. The localized damage occurred early in the wet specimen due to the stress concentration. By contrast, the dried specimen showed only distributed damage during the same cycle. These influences emphasize the impact of drying, which should not be neglected in structural designs.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"25 ","pages":"Article 100846"},"PeriodicalIF":8.2,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977703","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}
Pub Date : 2026-01-07DOI: 10.1016/j.dibe.2025.100842
Wenming Jiang , Ying Zhou , Tianjiao Han , Wang Shen , Fei Han , Yao Wang , Li Jiang
With 2D drawings as the vital bridge between designers and constructors, this study proposes a geometric-parametric hidden-line removal algorithm to resolve the long-standing low efficiency and poor accuracy in generating large-scale rebar component drawings across multiple industries and applications. The method represents each bar with a lightweight “central axis + section parameters” model, reducing geometric complexity by transforming 3D solid intersections into parameter-domain analysis and avoiding the high computational cost of B-rep surface intersections. Curvature-driven adaptive triangulation is employed to accurately extract contours of concrete components, while a BVH based coarse-screening and precise-detection pipeline substantially accelerates occlusion computation. To satisfy engineering drawing standards, the algorithm introduces adaptive offset models for orthogonal and oblique intersection scenarios and incorporates refined treatments for bar ends and bends, ensuring consistent double-line width, and smooth geometric transitions. Experiments on 71 components with varying scales demonstrate that the proposed method requires only 10–30 % of the runtime of the OCC algorithm, achieving 67.14–92.16 % efficiency gains, a mean acceleration factor of 18.10, and a 95 % confidence interval of [15.84, 20.44], with stable performance across large-scale assemblies. The generated drawings meet engineering specifications and significantly reduce manual correction. The proposed approach provides an efficient, controllable, and scalable computational framework for automated drawing generation of large-scale rebar components, with strong transferability to bridge reinforcement, rail-transit pipelines, and other slender-structure applications. Future work may explore integrating the parametric centerline–based visibility determination framework—while preserving its core steps and principles—with AI models such as Random Forest, Neural Implicit Fields (NIF) and PolyDiff Model, enabling more efficient and generalizable hidden-line removal and visibility prediction across complex, cross-domain scenarios.
{"title":"Research on a rapid hidden-line removal and drawing algorithm for large-scale reinforced structures based on geometric parametric representation","authors":"Wenming Jiang , Ying Zhou , Tianjiao Han , Wang Shen , Fei Han , Yao Wang , Li Jiang","doi":"10.1016/j.dibe.2025.100842","DOIUrl":"10.1016/j.dibe.2025.100842","url":null,"abstract":"<div><div>With 2D drawings as the vital bridge between designers and constructors, this study proposes a geometric-parametric hidden-line removal algorithm to resolve the long-standing low efficiency and poor accuracy in generating large-scale rebar component drawings across multiple industries and applications. The method represents each bar with a lightweight “central axis + section parameters” model, reducing geometric complexity by transforming 3D solid intersections into parameter-domain analysis and avoiding the high computational cost of B-rep surface intersections. Curvature-driven adaptive triangulation is employed to accurately extract contours of concrete components, while a BVH based coarse-screening and precise-detection pipeline substantially accelerates occlusion computation. To satisfy engineering drawing standards, the algorithm introduces adaptive offset models for orthogonal and oblique intersection scenarios and incorporates refined treatments for bar ends and bends, ensuring consistent double-line width, and smooth geometric transitions. Experiments on 71 components with varying scales demonstrate that the proposed method requires only 10–30 % of the runtime of the OCC algorithm, achieving 67.14–92.16 % efficiency gains, a mean acceleration factor of 18.10, and a 95 % confidence interval of [15.84, 20.44], with stable performance across large-scale assemblies. The generated drawings meet engineering specifications and significantly reduce manual correction. The proposed approach provides an efficient, controllable, and scalable computational framework for automated drawing generation of large-scale rebar components, with strong transferability to bridge reinforcement, rail-transit pipelines, and other slender-structure applications. Future work may explore integrating the parametric centerline–based visibility determination framework—while preserving its core steps and principles—with AI models such as Random Forest, Neural Implicit Fields (NIF) and PolyDiff Model, enabling more efficient and generalizable hidden-line removal and visibility prediction across complex, cross-domain scenarios.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"25 ","pages":"Article 100842"},"PeriodicalIF":8.2,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939532","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}
Pub Date : 2026-01-05DOI: 10.1016/j.dibe.2026.100845
Omar A. Refaat , Hafiz Asad Ali , Yanshuai Wang , Jian-Guo Dai , Yazan Alrefaei
While photovoltaic (PV) panels drive the global shift to renewable energy, their end-of-life (EoL) disposal (forecast to exceed 78 million tonnes by 2050) poses urgent environmental and resource-recovery challenges. Current management practices, dominated by landfill disposal and low-value recycling, not only result in the loss of valuable elements but also risk leaching toxins. This review critically examines the potential uses of PV waste glass (PVWG) and non-pure PV waste glass (NPVWG) in Portland cement (PC) and alkali-activated material (AAM) systems. Through comparative analysis with conventional waste glass (CWG), the review highlights both shared chemical features yet also distinctive traits of PV panel waste, such as ethylene–vinyl acetate (EVA) layers and metallic residues, which may offer functional advantages in construction applications. Key research gaps are identified in durability performance, hazardous-element immobilization, and processing optimization. The findings set out a targeted research and policy agenda to advance PV waste valorization within a circular-economy framework for the construction sector.
{"title":"Potentials of upcycling Photovoltaic panels waste in construction: A comparative review","authors":"Omar A. Refaat , Hafiz Asad Ali , Yanshuai Wang , Jian-Guo Dai , Yazan Alrefaei","doi":"10.1016/j.dibe.2026.100845","DOIUrl":"10.1016/j.dibe.2026.100845","url":null,"abstract":"<div><div>While photovoltaic (PV) panels drive the global shift to renewable energy, their end-of-life (EoL) disposal (forecast to exceed 78 million tonnes by 2050) poses urgent environmental and resource-recovery challenges. Current management practices, dominated by landfill disposal and low-value recycling, not only result in the loss of valuable elements but also risk leaching toxins. This review critically examines the potential uses of PV waste glass (PVWG) and non-pure PV waste glass (NPVWG) in Portland cement (PC) and alkali-activated material (AAM) systems. Through comparative analysis with conventional waste glass (CWG), the review highlights both shared chemical features yet also distinctive traits of PV panel waste, such as ethylene–vinyl acetate (EVA) layers and metallic residues, which may offer functional advantages in construction applications. Key research gaps are identified in durability performance, hazardous-element immobilization, and processing optimization. The findings set out a targeted research and policy agenda to advance PV waste valorization within a circular-economy framework for the construction sector.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"25 ","pages":"Article 100845"},"PeriodicalIF":8.2,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037724","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}
Pub Date : 2026-01-02DOI: 10.1016/j.dibe.2026.100844
Hyeong-Ki Kim , Seungo Baek , Jeong Hoon Rhee , Gebremicael Liyew , Gun Kim
The long-term microstructural evolution of concrete under accelerated carbonation was investigated using ultrasonic wave velocity (V) and acoustic nonlinearity parameter (β) to assess multiscale material changes. Concrete specimens made with ordinary Portland cement (OPC) were exposed to 10 % CO2 for one year. During this period, V remained nearly constant until 100 days and then increased by ∼10 %, indicating stiffness enhancement. In comparison, β decreased by ∼50 % within 100 days due to densification but later rose to ∼200 %, reflecting the onset of microcracking. This trend in β was supported by SEM-BSE and MIP analyses, which revealed pore refinement alongside the formation of nanoscale voids (10–100 nm). The influence of slag incorporation (50 % replacement) and curing conditions on carbonation kinetics was also examined. The results show that carbonation-induced densification could be offset by shrinkage, highlighting the bilateral nature of carbonation and the strong potential of β for long-term field monitoring.
{"title":"Bilateral effects of accelerated carbonation on concrete microstructure: Insights from one-year ultrasonic measurements","authors":"Hyeong-Ki Kim , Seungo Baek , Jeong Hoon Rhee , Gebremicael Liyew , Gun Kim","doi":"10.1016/j.dibe.2026.100844","DOIUrl":"10.1016/j.dibe.2026.100844","url":null,"abstract":"<div><div>The long-term microstructural evolution of concrete under accelerated carbonation was investigated using ultrasonic wave velocity (<em>V</em>) and acoustic nonlinearity parameter (<em>β</em>) to assess multiscale material changes. Concrete specimens made with ordinary Portland cement (OPC) were exposed to 10 % CO<sub>2</sub> for one year. During this period, <em>V</em> remained nearly constant until 100 days and then increased by ∼10 %, indicating stiffness enhancement. In comparison, <em>β</em> decreased by ∼50 % within 100 days due to densification but later rose to ∼200 %, reflecting the onset of microcracking. This trend in <em>β</em> was supported by SEM-BSE and MIP analyses, which revealed pore refinement alongside the formation of nanoscale voids (10–100 nm). The influence of slag incorporation (50 % replacement) and curing conditions on carbonation kinetics was also examined. The results show that carbonation-induced densification could be offset by shrinkage, highlighting the bilateral nature of carbonation and the strong potential of <em>β</em> for long-term field monitoring.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"25 ","pages":"Article 100844"},"PeriodicalIF":8.2,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939534","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}
Pub Date : 2025-12-31DOI: 10.1016/j.dibe.2025.100841
Hakpyeong Kim , Juwon Hong , Eunseong Song , Taehoon Hong , Jun-Ki Choi
Construction robots continue to draw significant interest, yet real-world deployments face persistent failures. Despite reported benefits and technological progress, there is still no systematic, evidence-based understanding of why robots underperform on actual construction sites. This study reviews 75 peer-reviewed field deployments (2016–2025) identified from the Web of Science to examine which failure factors recur and how they vary by construction domain, activity, and functionality. Each case is coded to reveal how technical failures are intertwined with organizational, environmental, and human factors. Five dominant failure dimensions emerge: environmental challenges (n = 45), system integration issues (n = 44), hardware limitations (n = 39), scalability and cost constraints (n = 15), and human–robot interaction issues (n = 14). The first three dominate, highlighting gaps in adaptability, interoperability, and mechanical robustness, as well as site-level infrastructure and workforce readiness. Based on these findings, this study proposes a dual-level mitigation framework spanning robot-level and site-level strategies to guide more scalable and successful deployment.
{"title":"A multi-dimensional failure factor affecting the on-site adoption of construction robots","authors":"Hakpyeong Kim , Juwon Hong , Eunseong Song , Taehoon Hong , Jun-Ki Choi","doi":"10.1016/j.dibe.2025.100841","DOIUrl":"10.1016/j.dibe.2025.100841","url":null,"abstract":"<div><div>Construction robots continue to draw significant interest, yet real-world deployments face persistent failures. Despite reported benefits and technological progress, there is still no systematic, evidence-based understanding of why robots underperform on actual construction sites. This study reviews 75 peer-reviewed field deployments (2016–2025) identified from the Web of Science to examine which failure factors recur and how they vary by construction domain, activity, and functionality. Each case is coded to reveal how technical failures are intertwined with organizational, environmental, and human factors. Five dominant failure dimensions emerge: environmental challenges (n = 45), system integration issues (n = 44), hardware limitations (n = 39), scalability and cost constraints (n = 15), and human–robot interaction issues (n = 14). The first three dominate, highlighting gaps in adaptability, interoperability, and mechanical robustness, as well as site-level infrastructure and workforce readiness. Based on these findings, this study proposes a dual-level mitigation framework spanning robot-level and site-level strategies to guide more scalable and successful deployment.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"25 ","pages":"Article 100841"},"PeriodicalIF":8.2,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939531","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}
Pub Date : 2025-12-30DOI: 10.1016/j.dibe.2025.100839
Haifeng Jin , Zhen Xu , Ziheng Xu , Nan Li , Paul M. Goodrum
Falls from height (FFH) remain the leading cause of fatalities in construction, highlighting persistent challenges in personal fall protection system (PFPS) planning. Despite regulations, anchorage placements still rely on subjective judgment and static layouts, limiting adaptability to complex site risks. This study develops a computer vision-assisted optimization framework integrating hazard zone modeling and worker posture detection. Vision-based posture data and hazard zone models construct spatial risk fields, providing a basis for anchorage planning. A multi-objective model is formulated to enhance safety performance and reduce swing fall risk, while a simulation module based on genetic algorithms computes Pareto-optimal layouts. Computer vision posture detection is embedded into the iterative module, enabling adaptive adjustments to deviations between planned and observed postures. A high-rise piping construction case study demonstrates the framework's effectiveness in producing safety-resilient and efficient anchorage plans. The proposed method advances PFPS toward intelligent and data-driven safety management.
{"title":"Computer vision-assisted multi-objective spatial optimization of fall protection systems in construction: Integrating hazard zone modeling and posture detection","authors":"Haifeng Jin , Zhen Xu , Ziheng Xu , Nan Li , Paul M. Goodrum","doi":"10.1016/j.dibe.2025.100839","DOIUrl":"10.1016/j.dibe.2025.100839","url":null,"abstract":"<div><div>Falls from height (FFH) remain the leading cause of fatalities in construction, highlighting persistent challenges in personal fall protection system (PFPS) planning. Despite regulations, anchorage placements still rely on subjective judgment and static layouts, limiting adaptability to complex site risks. This study develops a computer vision-assisted optimization framework integrating hazard zone modeling and worker posture detection. Vision-based posture data and hazard zone models construct spatial risk fields, providing a basis for anchorage planning. A multi-objective model is formulated to enhance safety performance and reduce swing fall risk, while a simulation module based on genetic algorithms computes Pareto-optimal layouts. Computer vision posture detection is embedded into the iterative module, enabling adaptive adjustments to deviations between planned and observed postures. A high-rise piping construction case study demonstrates the framework's effectiveness in producing safety-resilient and efficient anchorage plans. The proposed method advances PFPS toward intelligent and data-driven safety management.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"25 ","pages":"Article 100839"},"PeriodicalIF":8.2,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939530","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}
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