Effective dynamic monitoring of heritage masonry buildings depends on reliable data from multi-sensor monitoring systems. Machine learning-based response prediction offers an intelligent solution to practical limitations of in-situ measurements. However, existing predictive models struggle to make reliable predictions in the presence of incomplete data. This study proposes a novel dual-phase residual-augmented regression (DPRAR) method for predicting long-term modal frequencies of masonry buildings by integrating random forests (RF) with a deep regression-based neural network (DRNN). Initially, the RF predicts responses from measured data to extract residuals between observed and predicted values, which serve as latent information. Subsequently, these residuals, combined with measured data, form an enhanced dataset to train the DRNN for the final predictions. The main contributions include integrating statistical and deep learning regressors and innovatively using residuals to address missing unmeasured factors. Validation on a heritage masonry building shows that DPRAR substantially improves dynamic behavior prediction despite limited environmental measurements.
{"title":"Dynamic response prediction of heritage masonry buildings by dual-phase hybrid regression modeling under partial monitoring parameters","authors":"Alireza Entezami , Hesam Kiarad , Hassan Sarmadi , Bahareh Behkamal","doi":"10.1016/j.dibe.2025.100802","DOIUrl":"10.1016/j.dibe.2025.100802","url":null,"abstract":"<div><div>Effective dynamic monitoring of heritage masonry buildings depends on reliable data from multi-sensor monitoring systems. Machine learning-based response prediction offers an intelligent solution to practical limitations of in-situ measurements. However, existing predictive models struggle to make reliable predictions in the presence of incomplete data. This study proposes a novel dual-phase residual-augmented regression (DPRAR) method for predicting long-term modal frequencies of masonry buildings by integrating random forests (RF) with a deep regression-based neural network (DRNN). Initially, the RF predicts responses from measured data to extract residuals between observed and predicted values, which serve as latent information. Subsequently, these residuals, combined with measured data, form an enhanced dataset to train the DRNN for the final predictions. The main contributions include integrating statistical and deep learning regressors and innovatively using residuals to address missing unmeasured factors. Validation on a heritage masonry building shows that DPRAR substantially improves dynamic behavior prediction despite limited environmental measurements.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"24 ","pages":"Article 100802"},"PeriodicalIF":8.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520160","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 availability of low-cost sensors (LCS) devices for indoor air monitoring has boosted air pollution field. This study aims to calibrate particulate matter (PM) LCS (PM1, PM2.5 and PM10) in four age groups/types of rooms (infants, preschoolers, schoolers and lunchroom) during occupancy and non-occupancy period in nursery and primary schools in Porto, considering the purpose of being used as a tool to empower schools to apply indoor air pollution (IAP) mitigation measures.
Sixteen LCS devices (AirVisual Pro) and three research-grade instruments (DustTrak DRX 8534/8533) were used to monitor PM in around 130 different samplings. Before calibration, a methodology approach based on local maxima was applied to all PM fractions of LCS data, since various error events were found. Thus, after identifying and removing these events, a merged dataset was created using 1-min mean of LCS and reference data. Calibration models were applied, such as simple and multiple linear regressions (LR and MLR), linear and non-linear support vector regression (SVR) and gradient boosting regression (GBR).
A strong linear relationship was observed between LCS device and reference data, especially for non-occupancy period and in finer PM fractions (Pearson's correlation reached 0.94 for PM1 and PM2.5). While PM10 exhibited a slightly weaker correlation than the other PM fractions. The calibration models, particularly SVR and GBR models, significantly improved the results depending on the PM fraction, age group/type of room and occupancy pattern. Overall, results indicated that LCS devices are an effective tool for managing IAQ in schools, based on PM sensor.
{"title":"Cost-effective indoor air quality monitoring in schools: in-field calibration of PM low-cost sensor","authors":"J.P. Sá, H. Chojer, P.T.B.S. Branco, M.C.M. Alvim-Ferraz, F.G. Martins, S.I.V. Sousa","doi":"10.1016/j.dibe.2025.100762","DOIUrl":"10.1016/j.dibe.2025.100762","url":null,"abstract":"<div><div>The availability of low-cost sensors (LCS) devices for indoor air monitoring has boosted air pollution field. This study aims to calibrate particulate matter (PM) LCS (PM<sub>1</sub>, PM<sub>2.5</sub> and PM<sub>10</sub>) in four age groups/types of rooms (infants, preschoolers, schoolers and lunchroom) during occupancy and non-occupancy period in nursery and primary schools in Porto, considering the purpose of being used as a tool to empower schools to apply indoor air pollution (IAP) mitigation measures.</div><div>Sixteen LCS devices (AirVisual Pro) and three research-grade instruments (DustTrak DRX 8534/8533) were used to monitor PM in around 130 different samplings. Before calibration, a methodology approach based on local maxima was applied to all PM fractions of LCS data, since various error events were found. Thus, after identifying and removing these events, a merged dataset was created using 1-min mean of LCS and reference data. Calibration models were applied, such as simple and multiple linear regressions (LR and MLR), linear and non-linear support vector regression (SVR) and gradient boosting regression (GBR).</div><div>A strong linear relationship was observed between LCS device and reference data, especially for non-occupancy period and in finer PM fractions (Pearson's correlation reached 0.94 for PM<sub>1</sub> and PM<sub>2.5</sub>). While PM<sub>10</sub> exhibited a slightly weaker correlation than the other PM fractions. The calibration models, particularly SVR and GBR models, significantly improved the results depending on the PM fraction, age group/type of room and occupancy pattern. Overall, results indicated that LCS devices are an effective tool for managing IAQ in schools, based on PM sensor.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"24 ","pages":"Article 100762"},"PeriodicalIF":8.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221952","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 : 2025-12-01Epub Date: 2025-10-03DOI: 10.1016/j.dibe.2025.100772
Hao Yang , Huishan Wen , Junhui Zhang , Fujie Zhao , Ke Liu , Ting Yao , Mengmeng Wu
Recycled concrete particles, a primary component of construction and demolition waste (CDW), significantly contributes to the shear strength of CDW. The influence of particle shape on the mechanical properties of recycled concrete is of importance. This paper constructs four numerical simulation models with varying degrees of sphericity using discrete element simulation. The critical state line (CSL) of the samples is determined through triaxial shearing simulation tests, in conjunction with critical state soil mechanics analysis. The results indicate that particle sphericity markedly affects the macroscopic mechanical properties of recycled concrete particles. With the increase in particle sphericity under higher confining pressure, the deviatoric stress is decreased. Additionally, the cohesion of the samples rises with increasing sphericity, whereas the friction angle decreases. It is worth noting that with the increase of sphericity, the CSL slope of the sample shows a downward trend on both the q-p' and e-log p' planes.
{"title":"Effect of particle shape on mechanical properties of recycled concrete particles under the critical state soil mechanics","authors":"Hao Yang , Huishan Wen , Junhui Zhang , Fujie Zhao , Ke Liu , Ting Yao , Mengmeng Wu","doi":"10.1016/j.dibe.2025.100772","DOIUrl":"10.1016/j.dibe.2025.100772","url":null,"abstract":"<div><div>Recycled concrete particles, a primary component of construction and demolition waste (CDW), significantly contributes to the shear strength of CDW. The influence of particle shape on the mechanical properties of recycled concrete is of importance. This paper constructs four numerical simulation models with varying degrees of sphericity using discrete element simulation. The critical state line (CSL) of the samples is determined through triaxial shearing simulation tests, in conjunction with critical state soil mechanics analysis. The results indicate that particle sphericity markedly affects the macroscopic mechanical properties of recycled concrete particles. With the increase in particle sphericity under higher confining pressure, the deviatoric stress is decreased. Additionally, the cohesion of the samples rises with increasing sphericity, whereas the friction angle decreases. It is worth noting that with the increase of sphericity, the CSL slope of the sample shows a downward trend on both the <em>q-p'</em> and <em>e-log p'</em> planes.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"24 ","pages":"Article 100772"},"PeriodicalIF":8.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268611","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 : 2025-12-01Epub Date: 2025-11-01DOI: 10.1016/j.dibe.2025.100789
Xia Bian , Haichen Wang , Xiusong Shi , Weiheng Peng , Guizhong Xu , Chengchun Qiu
This study evaluates biochar's impact on MgO-slag stabilized slurry soil using physical, mechanical, and microstructural analyses (unconfined compressive strength tests, one-dimensional compression tests, X-ray diffraction (XRD), and scanning electron microscopy (SEM). Results show biochar significantly reduces stabilized soil density and post-curing water content. Soil pH decreased initially (0–10 % biochar), then increased (10–20 %), stabilizing beyond 20 %. Unconfined compressive strength (UCS) follows pH trends, indicating strength gains arise from supplementary reactions. Hence, an optimal biochar content of 20 % is identified with 48.1 % increase of 28-day UCS compared to biochar-free samples. Compression index (Cc) also shows a significantly improvement, decreased by 24.1 % (14-day) and 23.4 % (28-day) with 20 % biochar. Microstructural analysis revealed optimal biochar content enhances cementitious phase organization (e.g., C-S-H, hydrotalcite) and refines pores by absorbing free water and acting as nucleation sites. Optimized biochar integration thus improves mechanical performance, offering a low-carbon strategy for sustainable reuse of underground excavation slurries.
{"title":"Effect of biochar on the mechanical properties of MgO activated slag stabilized slurry soil","authors":"Xia Bian , Haichen Wang , Xiusong Shi , Weiheng Peng , Guizhong Xu , Chengchun Qiu","doi":"10.1016/j.dibe.2025.100789","DOIUrl":"10.1016/j.dibe.2025.100789","url":null,"abstract":"<div><div>This study evaluates biochar's impact on MgO-slag stabilized slurry soil using physical, mechanical, and microstructural analyses (unconfined compressive strength tests, one-dimensional compression tests, X-ray diffraction (XRD), and scanning electron microscopy (SEM). Results show biochar significantly reduces stabilized soil density and post-curing water content. Soil pH decreased initially (0–10 % biochar), then increased (10–20 %), stabilizing beyond 20 %. Unconfined compressive strength (UCS) follows pH trends, indicating strength gains arise from supplementary reactions. Hence, an optimal biochar content of 20 % is identified with 48.1 % increase of 28-day UCS compared to biochar-free samples. Compression index (<em>Cc</em>) also shows a significantly improvement, decreased by 24.1 % (14-day) and 23.4 % (28-day) with 20 % biochar. Microstructural analysis revealed optimal biochar content enhances cementitious phase organization (e.g., C-S-H, hydrotalcite) and refines pores by absorbing free water and acting as nucleation sites. Optimized biochar integration thus improves mechanical performance, offering a low-carbon strategy for sustainable reuse of underground excavation slurries.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"24 ","pages":"Article 100789"},"PeriodicalIF":8.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466184","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 : 2025-12-01Epub Date: 2025-11-01DOI: 10.1016/j.dibe.2025.100792
Zheng Li , Guoqing Song , Qingwen Zhang , Yuliang Liu , Jiangtao Yu , Feng Fan
This study examined group differences in the crossed effects of indoor environmental parameters on human comfort in open-plan offices in severe cold regions, considering gender, age, education, BMI and seating location. Field measurements of thermal, acoustic, air quality, and lighting conditions were conducted in 22 offices with 1352 surveys. Thermal comfort was affected by illumination: at 20–23 °C, higher illuminance reduced thermal comfort, whereas lower illuminance enhanced coolness perception. Females tolerated higher CO2 (>1200 ppm) and noise (>52 dB) at low temperatures. Participants over 25 years old were more sensitive to the temperature–light crossed effect, and those with doctoral degrees were more responsive to air quality. Underweight subjects’ comfort was linked to PM2.5 concentration, while overweight subjects preferred low temperature and low light. For subjects near windows, low illumination improved thermal comfort in warm conditions, and for subjects near doors, low temperatures improved air quality comfort under high pollutants.
{"title":"A systematic investigation of group differences in crossed effects of indoor environmental parameters on human comfort in open-plan offices in severe cold regions","authors":"Zheng Li , Guoqing Song , Qingwen Zhang , Yuliang Liu , Jiangtao Yu , Feng Fan","doi":"10.1016/j.dibe.2025.100792","DOIUrl":"10.1016/j.dibe.2025.100792","url":null,"abstract":"<div><div>This study examined group differences in the crossed effects of indoor environmental parameters on human comfort in open-plan offices in severe cold regions, considering gender, age, education, BMI and seating location. Field measurements of thermal, acoustic, air quality, and lighting conditions were conducted in 22 offices with 1352 surveys. Thermal comfort was affected by illumination: at 20–23 °C, higher illuminance reduced thermal comfort, whereas lower illuminance enhanced coolness perception. Females tolerated higher CO<sub>2</sub> (>1200 ppm) and noise (>52 dB) at low temperatures. Participants over 25 years old were more sensitive to the temperature–light crossed effect, and those with doctoral degrees were more responsive to air quality. Underweight subjects’ comfort was linked to PM<sub>2.5</sub> concentration, while overweight subjects preferred low temperature and low light. For subjects near windows, low illumination improved thermal comfort in warm conditions, and for subjects near doors, low temperatures improved air quality comfort under high pollutants.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"24 ","pages":"Article 100792"},"PeriodicalIF":8.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466185","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 : 2025-12-01Epub Date: 2025-10-13DOI: 10.1016/j.dibe.2025.100775
Jan P. Höffgen, Frank Dehn
The study explores the use of thermally activated concrete fines as substitution for cement to recycle mineral waste and reduce emissions. Concrete fines from waste recycling have a significant impact on compressive strength, with considerable variation due to their varying compositions. In the present study, 12 artificial concrete fines with varying compositions are thermally activated and assessed for their strength contribution. Increasing aggregate content within artificial fines results in a decrease in compressive strength, with aggregate mineralogy and binder composition having a major impact. Ultimately, this study proposes a model for predicting the impact of fines composition on compressive strength based on mass loss during dehydration. For thermally activated cement paste, the new model proposes no influence on compressive strength compared to the reference (). Paste precursors containing hydration products from alternative binders may even surpass the reference (), while an increasing amount of inert aggregates reduces strength ().
{"title":"Influence of thermally activated artificial concrete fines composition on mortar strength development","authors":"Jan P. Höffgen, Frank Dehn","doi":"10.1016/j.dibe.2025.100775","DOIUrl":"10.1016/j.dibe.2025.100775","url":null,"abstract":"<div><div>The study explores the use of thermally activated concrete fines as substitution for cement to recycle mineral waste and reduce emissions. Concrete fines from waste recycling have a significant impact on compressive strength, with considerable variation due to their varying compositions. In the present study, 12 artificial concrete fines with varying compositions are thermally activated and assessed for their strength contribution. Increasing aggregate content within artificial fines results in a decrease in compressive strength, with aggregate mineralogy and binder composition having a major impact. Ultimately, this study proposes a model for predicting the impact of fines composition on compressive strength based on mass loss during dehydration. For thermally activated cement paste, the new model proposes no influence on compressive strength compared to the reference (<span><math><mrow><mi>k</mi><mo>=</mo><mn>1</mn><mo>.</mo><mn>0</mn></mrow></math></span>). Paste precursors containing hydration products from alternative binders may even surpass the reference (<span><math><mrow><mi>k</mi><mo>></mo><mn>1</mn><mo>.</mo><mn>0</mn></mrow></math></span>), while an increasing amount of inert aggregates reduces strength (<span><math><mrow><mi>k</mi><mo><</mo><mn>1</mn><mo>.</mo><mn>0</mn></mrow></math></span>).</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"24 ","pages":"Article 100775"},"PeriodicalIF":8.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363257","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 : 2025-12-01Epub Date: 2025-09-24DOI: 10.1016/j.dibe.2025.100754
Xinyu Fan, Xuxu Yang, Feifei Hou, Cuipu Xi, Yijun Wang
The presence of foreign objects on railway tracks poses serious safety risks and may lead to accidents or service disruptions. However, existing detection systems based on deep learning are often constrained by small datasets, limited sample diversity, and low realism in synthesized training images. To address these issues, this paper proposes PLCA-pix2pixGAN (Perceptual Loss and Channel Attention Enhanced pix2pix GAN) to generate high-quality synthetic images for data augmentation. The method overlays object templates onto real-world track images to build a composite dataset and applies interpretable augmentation to simulate lighting and weather changes. To enhance fidelity, a channel attention mechanism enables region-aware reconstruction, and a multi-objective loss combines perceptual loss with adaptive weighting to balance pixel-level accuracy and semantic consistency. Experiments show the proposed method achieves an average SSIM of 0.9106 across object categories, demonstrating its effectiveness in generating realistic, structurally consistent images for safety-critical foreign object detection in railway systems.
{"title":"Track foreign object image augmentation based on the proposed PLCA-pix2pixGAN method","authors":"Xinyu Fan, Xuxu Yang, Feifei Hou, Cuipu Xi, Yijun Wang","doi":"10.1016/j.dibe.2025.100754","DOIUrl":"10.1016/j.dibe.2025.100754","url":null,"abstract":"<div><div>The presence of foreign objects on railway tracks poses serious safety risks and may lead to accidents or service disruptions. However, existing detection systems based on deep learning are often constrained by small datasets, limited sample diversity, and low realism in synthesized training images. To address these issues, this paper proposes PLCA-pix2pixGAN (Perceptual Loss and Channel Attention Enhanced pix2pix GAN) to generate high-quality synthetic images for data augmentation. The method overlays object templates onto real-world track images to build a composite dataset and applies interpretable augmentation to simulate lighting and weather changes. To enhance fidelity, a channel attention mechanism enables region-aware reconstruction, and a multi-objective loss combines perceptual loss with adaptive weighting to balance pixel-level accuracy and semantic consistency. Experiments show the proposed method achieves an average SSIM of 0.9106 across object categories, demonstrating its effectiveness in generating realistic, structurally consistent images for safety-critical foreign object detection in railway systems.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"24 ","pages":"Article 100754"},"PeriodicalIF":8.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159129","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 : 2025-12-01Epub Date: 2025-11-21DOI: 10.1016/j.dibe.2025.100799
Seungwoo Park , Lang Fu , Hyungjoon Seo
This study presents an automated structural damage detection methodology for 19th–20th century heritage buildings using a Roughness–CANUPO(1)–CANUPO(2) (R–C–C) machine learning algorithm combined with 3D laser scanning. To address the limitations of traditional inspection methods in heritage conservation, a non-destructive testing (NDT) integrating surface roughness analysis and machine learning was applied to six heritage buildings constructed with red brick, limestone, and terracotta. High-resolution point cloud data (PCD) were acquired using terrestrial laser scanning, and Local Neighbour Radius (LNR) values were optimised to maximize the separation of crack and wall surface features during roughness-based filtering. A two-stage CANUPO classifier based on the support vector machine learning (SVM), trained on roughness-derived features, was employed to automatically distinguish cracks from undamaged surfaces and joints. Experimental results confirmed that optimal LNR and filtration ratio tuning were essential for effective crack visibility and classification performance. Specifically, under optimised conditions, maximum crack visibility reached 47.28 % and 32.74 % for red brick walls, 63.48 % and 30.23 % for limestone walls, and 82.56 % and 30.34 % for terracotta columns. These results highlight the importance of adapting LNR values and filtering strategies to material-specific surface geometries, particularly in curved components like terracotta columns where 3D curvature influences roughness behaviour. The R–C–C approach enables scalable and accurate structural condition assessment without physical contact, offering a practical tool for the structural monitoring and long-term preservation of historically significant architecture.
{"title":"Automatic damage detection in 19th–20th century heritage buildings using R-C-C fusion machine learning with 3D laser scanning","authors":"Seungwoo Park , Lang Fu , Hyungjoon Seo","doi":"10.1016/j.dibe.2025.100799","DOIUrl":"10.1016/j.dibe.2025.100799","url":null,"abstract":"<div><div>This study presents an automated structural damage detection methodology for 19th–20th century heritage buildings using a Roughness–CANUPO(1)–CANUPO(2) (R–C–C) machine learning algorithm combined with 3D laser scanning. To address the limitations of traditional inspection methods in heritage conservation, a non-destructive testing (NDT) integrating surface roughness analysis and machine learning was applied to six heritage buildings constructed with red brick, limestone, and terracotta. High-resolution point cloud data (PCD) were acquired using terrestrial laser scanning, and Local Neighbour Radius (LNR) values were optimised to maximize the separation of crack and wall surface features during roughness-based filtering. A two-stage CANUPO classifier based on the support vector machine learning (SVM), trained on roughness-derived features, was employed to automatically distinguish cracks from undamaged surfaces and joints. Experimental results confirmed that optimal LNR and filtration ratio tuning were essential for effective crack visibility and classification performance. Specifically, under optimised conditions, maximum crack visibility reached 47.28 % and 32.74 % for red brick walls, 63.48 % and 30.23 % for limestone walls, and 82.56 % and 30.34 % for terracotta columns. These results highlight the importance of adapting LNR values and filtering strategies to material-specific surface geometries, particularly in curved components like terracotta columns where 3D curvature influences roughness behaviour. The R–C–C approach enables scalable and accurate structural condition assessment without physical contact, offering a practical tool for the structural monitoring and long-term preservation of historically significant architecture.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"24 ","pages":"Article 100799"},"PeriodicalIF":8.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614411","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 : 2025-12-01Epub Date: 2025-11-10DOI: 10.1016/j.dibe.2025.100801
Joana Fernandes, Paulo Ferrão
The construction industry accounts for over 30 % of global resource extraction, generates 25 % of solid waste, and contributes 11 % of total greenhouse gas emissions, including from material processing. Refurbishment strategies are crucial for mitigating these impacts by extending building lifespans. Effective decarbonization requires a comprehensive analysis of refurbishment design regarding resource management and global warming.
However, existing assessment methods are often fragmented. To address this, the Carbon Circularity Method, has been developed specifically for refurbishment projects, combining multi-level assessments of Circular Economy (CE) practices with embodied carbon quantification. Aligned with established standards, it defines clear system boundaries, refines End-of-Life strategies, and introduces carbon-informed CE quantification.
Findings show that, while high Disassembly Indexes facilitate CE practices, circularity potential is constrained by components condition. Integrating embodied carbon helps prioritize low-carbon-intensive products, reinforces CE strategies, and enables comprehensive CE and climate impact evaluation, offering a valuable tool for improving building environmental performance.
{"title":"Bridging circular economy and embodied carbon: A quantitative assessment method for building refurbishment design","authors":"Joana Fernandes, Paulo Ferrão","doi":"10.1016/j.dibe.2025.100801","DOIUrl":"10.1016/j.dibe.2025.100801","url":null,"abstract":"<div><div>The construction industry accounts for over 30 % of global resource extraction, generates 25 % of solid waste, and contributes 11 % of total greenhouse gas emissions, including from material processing. Refurbishment strategies are crucial for mitigating these impacts by extending building lifespans. Effective decarbonization requires a comprehensive analysis of refurbishment design regarding resource management and global warming.</div><div>However, existing assessment methods are often fragmented. To address this, the Carbon Circularity Method, has been developed specifically for refurbishment projects, combining multi-level assessments of Circular Economy (CE) practices with embodied carbon quantification. Aligned with established standards, it defines clear system boundaries, refines End-of-Life strategies, and introduces carbon-informed CE quantification.</div><div>Findings show that, while high Disassembly Indexes facilitate CE practices, circularity potential is constrained by components condition. Integrating embodied carbon helps prioritize low-carbon-intensive products, reinforces CE strategies, and enables comprehensive CE and climate impact evaluation, offering a valuable tool for improving building environmental performance.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"24 ","pages":"Article 100801"},"PeriodicalIF":8.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520159","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 : 2025-12-01Epub Date: 2025-11-05DOI: 10.1016/j.dibe.2025.100796
Wonjae Yoo , Hyoungsub Kim
This study presents a novel surrogate modeling approach to predict indoor thermal environments in dense urban contexts. By explicitly incorporating key shading parameters—average surface Sky View Factor (SVF) and sunlight hours (SH)—the model addresses limitations in conventional surrogates that overlook or simplify surrounding configurations. Indoor air temperature was selected as the primary output metric to directly capture thermal responses to urban geometry without the confounding effects of building systems. Validation results show high accuracy (MAPE: 1.25 %, MAE: 0.215 °C). Sensitivity analysis confirms that excluding SVF or SH significantly degrades predictive performance (MAPE increases of 8.87 % and 6.86 %, respectively). In fixed urban contexts, core zone volume becomes the dominant factor, while west-facing zones show highest sensitivity to shading effects—revealing how variable importance shifts across different urban configurations. These findings underscore the critical role of SVF and SH in capturing the shading effects essential for accurate indoor temperature prediction.
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