Pub Date : 2024-12-09DOI: 10.1016/j.dibe.2024.100592
Bruna S. Santos , Wesley B.S. Machini , Gina Matias , Nelson F.S.T. Moreira , Paulo M.M. Portugal , Isabel Torres , António Tadeu , João A.S. Almeida
In this work, a comparative study on the mechanical, microstructural and chemical properties of mortars with enhanced chemical resistance was performed to investigate the effects of sulphuric acid attack. For this, specimens of ordinary and improved formulations were immersed in water and sulphuric acid at pH 0.0 for 14 days, assessing the relative residual compressive strength and corrosion depth. The sulphuric acid attack resulted in pronounced changes in the mechanical properties and severe corrosion for the ordinary mortar. In contrast, the improved mortars exhibited moderate to high acid resistance (relative residual strengths up to 94.6% and minimal corrosion depth of 0.5 mm). A significant quality gain of up to 49% and 180% was also observed when comparing the improved mortars with a reference mortar resistant to acid in terms of relative residual compressive strength and corrosion depth, respectively. The effect of sulphuric acid attack on the microstructure and chemical composition of mortars was further evaluated by mercury intrusion porosimetry (MIP), attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR), and scanning electron microscopy with energy dispersive X-ray (SEM-EDX).
{"title":"Mortars with enhanced chemical resistance: Effects of sulphuric acid exposure","authors":"Bruna S. Santos , Wesley B.S. Machini , Gina Matias , Nelson F.S.T. Moreira , Paulo M.M. Portugal , Isabel Torres , António Tadeu , João A.S. Almeida","doi":"10.1016/j.dibe.2024.100592","DOIUrl":"10.1016/j.dibe.2024.100592","url":null,"abstract":"<div><div>In this work, a comparative study on the mechanical, microstructural and chemical properties of mortars with enhanced chemical resistance was performed to investigate the effects of sulphuric acid attack. For this, specimens of ordinary and improved formulations were immersed in water and sulphuric acid at pH 0.0 for 14 days, assessing the relative residual compressive strength and corrosion depth. The sulphuric acid attack resulted in pronounced changes in the mechanical properties and severe corrosion for the ordinary mortar. In contrast, the improved mortars exhibited moderate to high acid resistance (relative residual strengths up to 94.6% and minimal corrosion depth of 0.5 mm). A significant quality gain of up to 49% and 180% was also observed when comparing the improved mortars with a reference mortar resistant to acid in terms of relative residual compressive strength and corrosion depth, respectively. The effect of sulphuric acid attack on the microstructure and chemical composition of mortars was further evaluated by mercury intrusion porosimetry (MIP), attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR), and scanning electron microscopy with energy dispersive X-ray (SEM-EDX).</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"21 ","pages":"Article 100592"},"PeriodicalIF":6.2,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Three-dimensional Ground penetrating radar (3D-GPR) has been widely applied in nondestructive testing of concealed cracks within asphalt pavement. However, due to the weak GPR echo characteristics of concealed cracks and their susceptibility to environmental noise, automatic recognition of crack echo features has always faced significant challenges. To address this issue, numerous semi-rigid base crack images were collected and extracted using feature point tensor voting with 3D-GPR's efficient, non-destructive road structure detection. In this paper, the radar image is gridded by the ECA-ResNet network, and the center point of the detected crack grid is used as the feature point, and the continuous path of the crack is reconstructed by the tensor voting algorithm. The results show that this method achieves 90% crack extraction, which is superior to traditional target detection networks such as YOLOv5 and Fast R-CNN, providing an effective tool for rapid non-destructive detection of pavement cracks.
{"title":"Three-dimensional ground-penetrating radar-based feature point tensor voting for semi-rigid base asphalt pavement crack detection","authors":"Zhiyong Huang , Guoyuan Xu , Xiaoning Zhang , Bo Zang , Huayang Yu","doi":"10.1016/j.dibe.2024.100591","DOIUrl":"10.1016/j.dibe.2024.100591","url":null,"abstract":"<div><div>Three-dimensional Ground penetrating radar (3D-GPR) has been widely applied in nondestructive testing of concealed cracks within asphalt pavement. However, due to the weak GPR echo characteristics of concealed cracks and their susceptibility to environmental noise, automatic recognition of crack echo features has always faced significant challenges. To address this issue, numerous semi-rigid base crack images were collected and extracted using feature point tensor voting with 3D-GPR's efficient, non-destructive road structure detection. In this paper, the radar image is gridded by the ECA-ResNet network, and the center point of the detected crack grid is used as the feature point, and the continuous path of the crack is reconstructed by the tensor voting algorithm. The results show that this method achieves 90% crack extraction, which is superior to traditional target detection networks such as YOLOv5 and Fast R-CNN, providing an effective tool for rapid non-destructive detection of pavement cracks.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"21 ","pages":"Article 100591"},"PeriodicalIF":6.2,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-06DOI: 10.1016/j.dibe.2024.100588
Yan Zhou , Yizhi Qiu , Liuzhuo Chen
RC columns exposed to harsh environments are susceptible to internal reinforcement corrosion, leading to a reduction in lateral deformation capacity. The accurate prediction of drift ratio limits (DRLs) for corroded RC columns (CRCCs) across various damage states is crucial for reliable damage assessment and seismic resilience analysis. Current literatures remain inadequate for predicting DRLs for CRCCs with diverse service life. To address this gap, this paper introduces a two-stage machine learning (ML) approach for the simultaneous prediction of DRLs in CRCCs, utilizing quasi-static test data from 290 corroded column specimens. In the first stage, a failure mode recognition model and a single-output DRL prediction model were developed using the XGBoost algorithm. This model is then combined with the SHAP method to facilitate feature importance ranking and model interpretability. Building on the insights gained from failure mode recognition and feature importance ranking in the first stage, a Deep Neural Network (DNN) was employed in the second stage to achieve multi-output prediction of DRLs. The findings indicate that the SHAP-based interpretable ML method offers profound understanding of the intricate associations between failure modes and DRLs, design parameters and corrosion rate. The proposed DNN model is capable of concurrently outputting multiple DRLs while balancing the accuracy and efficiency, and signifies a notable advancement beyond traditional methodologies for estimating the lateral deformation capacity of CRCCs.
{"title":"Two-stage prediction of drift ratio limits of corroded RC columns based on interpretable machine learning methods","authors":"Yan Zhou , Yizhi Qiu , Liuzhuo Chen","doi":"10.1016/j.dibe.2024.100588","DOIUrl":"10.1016/j.dibe.2024.100588","url":null,"abstract":"<div><div>RC columns exposed to harsh environments are susceptible to internal reinforcement corrosion, leading to a reduction in lateral deformation capacity. The accurate prediction of drift ratio limits (DRLs) for corroded RC columns (CRCCs) across various damage states is crucial for reliable damage assessment and seismic resilience analysis. Current literatures remain inadequate for predicting DRLs for CRCCs with diverse service life. To address this gap, this paper introduces a two-stage machine learning (ML) approach for the simultaneous prediction of DRLs in CRCCs, utilizing quasi-static test data from 290 corroded column specimens. In the first stage, a failure mode recognition model and a single-output DRL prediction model were developed using the XGBoost algorithm. This model is then combined with the SHAP method to facilitate feature importance ranking and model interpretability. Building on the insights gained from failure mode recognition and feature importance ranking in the first stage, a Deep Neural Network (DNN) was employed in the second stage to achieve multi-output prediction of DRLs. The findings indicate that the SHAP-based interpretable ML method offers profound understanding of the intricate associations between failure modes and DRLs, design parameters and corrosion rate. The proposed DNN model is capable of concurrently outputting multiple DRLs while balancing the accuracy and efficiency, and signifies a notable advancement beyond traditional methodologies for estimating the lateral deformation capacity of CRCCs.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"21 ","pages":"Article 100588"},"PeriodicalIF":6.2,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.1016/j.dibe.2024.100583
Peng Wang , Yajie Zhou , Yao Lu , Linyuwen Ke , Haoliang Wu , Weiwen Li , Christopher K.Y. Leung
Glass fiber reinforced polymer (GFRP) has been considered as an advanced material to replace conventional steel reinforcements in concrete structures to address the corrosion issue. The degradation of GFRP rebars, which may threaten both the durability and safety of infrastructures, is a major concern. To predict the 100-year strength retention of GFRP under various temperature and relative humidity conditions, the environmental reduction factor () is applied in engineering. The conventional based on the assumption of logarithmic degradation model is commonly utilized during the degradation phase spanning from a few years to decades; however, it is not applicable to the initial and perpetual degradation phases. To address this issue, a novel environmental reduction factor () based on the exponential degradation model considering temperature and relative humidity is proposed in this study. Both and are mathematically deduced from empirical degradation data and then evaluated in a case study involving GFRP-concrete samples soaked in water at 23, 40 or 60 °C for up to 12 months within the authors’ dataset. Experimental results show that the GFRP tensile strength degradation is closer to the exponential model, reaching a plateau (47.4%) after 12-month exposure to 60 °C water. Moreover, the tensile strength retention of GFRP rebars in Vancouver (10 °C), Shanghai (16 °C) and Houston (22 °C) is predicted considering various scenarios of relative humidities (0–90%). Further research indicates that (0.65–0.78) exhibits a smaller value compared to (0.81) at a temperature of 22 °C and a relative humidity of 90% following a 100-year exposure period, thereby providing engineers with a more conservative design approach for GFRP in real-world scenarios. Nevertheless, this exponential degradation model requires a thorough consideration of severe degradation state during the extended aging period, which may not be applicable to GFRP structures exhibiting exceptional durability.
{"title":"Predicting long-term tensile degradation of GFRP rebars embedded in concrete with a reconsidered environmental reduction factor CE","authors":"Peng Wang , Yajie Zhou , Yao Lu , Linyuwen Ke , Haoliang Wu , Weiwen Li , Christopher K.Y. Leung","doi":"10.1016/j.dibe.2024.100583","DOIUrl":"10.1016/j.dibe.2024.100583","url":null,"abstract":"<div><div>Glass fiber reinforced polymer (GFRP) has been considered as an advanced material to replace conventional steel reinforcements in concrete structures to address the corrosion issue. The degradation of GFRP rebars, which may threaten both the durability and safety of infrastructures, is a major concern. To predict the 100-year strength retention of GFRP under various temperature and relative humidity conditions, the environmental reduction factor (<span><math><mrow><msub><mi>C</mi><mi>E</mi></msub></mrow></math></span>) is applied in engineering. The conventional <span><math><mrow><msub><mi>C</mi><mi>E</mi></msub></mrow></math></span> based on the assumption of logarithmic degradation model is commonly utilized during the degradation phase spanning from a few years to decades; however, it is not applicable to the initial and perpetual degradation phases. To address this issue, a novel environmental reduction factor (<span><math><mrow><msubsup><mi>C</mi><mi>E</mi><mo>′</mo></msubsup></mrow></math></span>) based on the exponential degradation model considering temperature and relative humidity is proposed in this study. Both <span><math><mrow><msub><mi>C</mi><mi>E</mi></msub></mrow></math></span> and <span><math><mrow><msubsup><mi>C</mi><mi>E</mi><mo>′</mo></msubsup></mrow></math></span> are mathematically deduced from empirical degradation data and then evaluated in a case study involving GFRP-concrete samples soaked in water at 23, 40 or 60 °C for up to 12 months within the authors’ dataset. Experimental results show that the GFRP tensile strength degradation is closer to the exponential model, reaching a plateau (47.4%) after 12-month exposure to 60 °C water. Moreover, the tensile strength retention of GFRP rebars in Vancouver (10 °C), Shanghai (16 °C) and Houston (22 °C) is predicted considering various scenarios of relative humidities (0–90%). Further research indicates that <span><math><mrow><msubsup><mi>C</mi><mi>E</mi><mo>′</mo></msubsup></mrow></math></span> (0.65–0.78) exhibits a smaller value compared to <span><math><mrow><msub><mi>C</mi><mi>E</mi></msub></mrow></math></span> (0.81) at a temperature of 22 °C and a relative humidity of 90% following a 100-year exposure period, thereby providing engineers with a more conservative design approach for GFRP in real-world scenarios. Nevertheless, this exponential degradation model requires a thorough consideration of severe degradation state during the extended aging period, which may not be applicable to GFRP structures exhibiting exceptional durability.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"20 ","pages":"Article 100583"},"PeriodicalIF":6.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study explores the bond strength of steel reinforcing bars in normal concrete (NC) and ultra-high-performance concrete (UHPC) with steel fibers, focusing on the behavior of lap-spliced rebars. Key variables such as rebar diameter (D), spacing (s), lap-splice length (L1), and hook length (L2) were evaluated to understand their impact on bond performance. Both NC and UHPC increase in bond strength with greater spacing and lap-splice length, with UHPC demonstrating significantly stronger bond characteristics due to its higher compressive strength. A novel predictive method for calculating bond strength is proposed, offering practical guidance for designing lap-spliced rebars in construction, and includes reduction factors tailored for realistic construction settings. This method achieves an error margin below ±16.5%, providing an accurate approach for practitioners. This rational method incorporates both engineering and economic considerations, particularly valuable for applications in precast concrete structures where enhanced bond performance and durability are essential.
{"title":"Bonding behavior of lap-spliced reinforcing bars embedded in ultra-high-performance concrete with steel fibers","authors":"Krairerk Aiamsri , Teerasak Yaowarat , Suksun Horpibulsuk , Apichat Suddeepong , Apinun Buritatum , Kanchana Hiranwatthana , Kirati Nitichote","doi":"10.1016/j.dibe.2024.100585","DOIUrl":"10.1016/j.dibe.2024.100585","url":null,"abstract":"<div><div>This study explores the bond strength of steel reinforcing bars in normal concrete (NC) and ultra-high-performance concrete (UHPC) with steel fibers, focusing on the behavior of lap-spliced rebars. Key variables such as rebar diameter (D), spacing (s), lap-splice length (L<sub>1</sub>), and hook length (L<sub>2</sub>) were evaluated to understand their impact on bond performance. Both NC and UHPC increase in bond strength with greater spacing and lap-splice length, with UHPC demonstrating significantly stronger bond characteristics due to its higher compressive strength. A novel predictive method for calculating bond strength is proposed, offering practical guidance for designing lap-spliced rebars in construction, and includes reduction factors tailored for realistic construction settings. This method achieves an error margin below ±16.5%, providing an accurate approach for practitioners. This rational method incorporates both engineering and economic considerations, particularly valuable for applications in precast concrete structures where enhanced bond performance and durability are essential.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"20 ","pages":"Article 100585"},"PeriodicalIF":6.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.1016/j.dibe.2024.100578
Shenghua Zhou , Xinru Man , Dezhi Li , S. Thomas Ng , Ran Wei , Yaowen Xu , Lugang Yu
Conventional expertise-based approaches yield less-than-optimal window placement schemes (WPSs). The simulation-based window placement approaches (e.g., EnergyPlus) also suffer from being time-consuming. This study newly proposes a multi-objective window placement approach that can optimize WPSs under multiple objectives with high efficiency. It includes (i) transforming WPS-related building information from BIM's IFC to EnergyPlus's IDF, (ii) developing a surrogate model for WPS performance assessment, and (iii) optimizing WPSs under multiple objectives. The proposed approach is demonstrated using a dormitory building in Beijing, showcasing its ability to rapidly derive Pareto frontiers of optimized WPSs assuming different objective weightings. Compared to expertise-based methods, it shows a 30.66% and 16.47% performance enhancement for flexible-sized and fixed-sized window placements. Compared to simulation-based methods, it reduces time consumption by 98.10% while maintaining 94.3% accuracy in the case study. This study provides a well-performing and highly efficient window placement approach, making large-scale WPS optimizations feasible for designers.
{"title":"A multi-objective window placement approach using BIM and surrogate model","authors":"Shenghua Zhou , Xinru Man , Dezhi Li , S. Thomas Ng , Ran Wei , Yaowen Xu , Lugang Yu","doi":"10.1016/j.dibe.2024.100578","DOIUrl":"10.1016/j.dibe.2024.100578","url":null,"abstract":"<div><div>Conventional expertise-based approaches yield less-than-optimal window placement schemes (WPSs). The simulation-based window placement approaches (e.g., EnergyPlus) also suffer from being time-consuming. This study newly proposes a multi-objective window placement approach that can optimize WPSs under multiple objectives with high efficiency. It includes (i) transforming WPS-related building information from BIM's IFC to EnergyPlus's IDF, (ii) developing a surrogate model for WPS performance assessment, and (iii) optimizing WPSs under multiple objectives. The proposed approach is demonstrated using a dormitory building in Beijing, showcasing its ability to rapidly derive Pareto frontiers of optimized WPSs assuming different objective weightings. Compared to expertise-based methods, it shows a 30.66% and 16.47% performance enhancement for flexible-sized and fixed-sized window placements. Compared to simulation-based methods, it reduces time consumption by 98.10% while maintaining 94.3% accuracy in the case study. This study provides a well-performing and highly efficient window placement approach, making large-scale WPS optimizations feasible for designers.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"20 ","pages":"Article 100578"},"PeriodicalIF":6.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.1016/j.dibe.2024.100580
Violetta K. Kytinou , Zoi S. Metaxa , Adamantis G. Zapris , Ramonna I. Kosheleva , Vasilis D. Prokopiou , Nikolaos D. Alexopoulos
The present article investigates the possibility of Extruded Polystyrene (XPS) waste to be used as a lightweight aggregate in cement-based materials for structural applications. The developed material offers a promising solution for rehabilitation and energy efficiency upgrades of existing civil infrastructures, providing thermal insulation without adding excessive weight to reinforced concrete structures. Through a comprehensive experimental approach, this contribution evaluates the mechanical and thermal performance of cement-based composites with varying XPS content (up to 100 %) as sand replacement. Results demonstrate a balance between enhanced thermal insulation and maintained mechanical robustness, with optimal XPS content ranges identified for specific application needs. The incorporation of XPS waste into construction materials supports sustainability by repurposing non-biodegradable materials, while meeting the dual requirements of structural performance and energy efficiency. This research supports the exploitation of XPS-modified cement-based materials in structural rehabilitation of existing structures and innovative construction applications, promoting greener and more efficient building materials that could be utilized in ground structures, such as buildings.
{"title":"Exploitation of extruded polystyrene (XPS) waste for lightweight, thermal insulation and rehabilitation building applications","authors":"Violetta K. Kytinou , Zoi S. Metaxa , Adamantis G. Zapris , Ramonna I. Kosheleva , Vasilis D. Prokopiou , Nikolaos D. Alexopoulos","doi":"10.1016/j.dibe.2024.100580","DOIUrl":"10.1016/j.dibe.2024.100580","url":null,"abstract":"<div><div>The present article investigates the possibility of Extruded Polystyrene (XPS) waste to be used as a lightweight aggregate in cement-based materials for structural applications. The developed material offers a promising solution for rehabilitation and energy efficiency upgrades of existing civil infrastructures, providing thermal insulation without adding excessive weight to reinforced concrete structures. Through a comprehensive experimental approach, this contribution evaluates the mechanical and thermal performance of cement-based composites with varying XPS content (up to 100 %) as sand replacement. Results demonstrate a balance between enhanced thermal insulation and maintained mechanical robustness, with optimal XPS content ranges identified for specific application needs. The incorporation of XPS waste into construction materials supports sustainability by repurposing non-biodegradable materials, while meeting the dual requirements of structural performance and energy efficiency. This research supports the exploitation of XPS-modified cement-based materials in structural rehabilitation of existing structures and innovative construction applications, promoting greener and more efficient building materials that could be utilized in ground structures, such as buildings.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"20 ","pages":"Article 100580"},"PeriodicalIF":6.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.1016/j.dibe.2024.100542
Samuel G.A. Wood, Alice E.E. Handy, Katherine Roberts, Henry C. Burridge
{"title":"Corrigendum to “Assessing classroom ventilation rates using CO2 data from a nationwide study of UK schools and identifying school-wide correlation factors” [Develop. Built Environ. 19 100520]","authors":"Samuel G.A. Wood, Alice E.E. Handy, Katherine Roberts, Henry C. Burridge","doi":"10.1016/j.dibe.2024.100542","DOIUrl":"10.1016/j.dibe.2024.100542","url":null,"abstract":"","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"20 ","pages":"Article 100542"},"PeriodicalIF":6.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.1016/j.dibe.2024.100586
Peter E.D. Love , Jane Matthews , Weili Fang
As construction organizations are confronted with uncertainty and imperfect information, they find accommodating the likelihood of rework in their projects challenging. Bayesian statistical models cannot be utilized to predict rework as objective, and even subjective probabilities are unknown. In uncertainty settings, algorithms such as smart heuristics – simple task-specific decision strategies that function under specific conditions – have been shown to achieve equal and better performance in problems of inference than machine learning models. However, algorithms to effectively deal with the uncertainty of rework in construction have yet to be developed. Hence, the motivation for this paper is to examine how psychological artificial intelligence, which applies insights from psychology (e.g., mental and social processes) to design algorithms, can be potentially used to develop smart heuristics that can cater to the uncertainty of rework in construction in varying conditions and contexts. To this end, the contributions of this paper are twofold as it: (1) brings to the fore a new line of inquiry to deal with not only the uncertainty of rework using psychological insights to design simple algorithms but also unexpected events in general; and (2) provides guidance to ensure the design of algorithms to deal with the uncertainty that reflects the actualities of practice.
{"title":"Psychological artificial intelligence: Designing algorithms to deal with the uncertainty of rework in construction","authors":"Peter E.D. Love , Jane Matthews , Weili Fang","doi":"10.1016/j.dibe.2024.100586","DOIUrl":"10.1016/j.dibe.2024.100586","url":null,"abstract":"<div><div>As construction organizations are confronted with uncertainty and imperfect information, they find accommodating the likelihood of rework in their projects challenging. Bayesian statistical models cannot be utilized to predict rework as objective, and even subjective probabilities are unknown. In uncertainty settings, algorithms such as smart heuristics – <em>simple task-specific decision strategies that function under specific conditions</em> – have been shown to achieve equal and better performance in problems of inference than machine learning models. However, algorithms to effectively deal with the uncertainty of rework in construction have yet to be developed. Hence, the motivation for this paper is to examine how psychological artificial intelligence, which applies insights from psychology (e.g., mental and social processes) to design algorithms, can be potentially used to develop smart heuristics that can cater to the uncertainty of rework in construction in varying conditions and contexts. To this end, the contributions of this paper are twofold as it: (1) brings to the fore a <em>new</em> line of inquiry to deal with not only the uncertainty of rework using psychological insights to design simple algorithms but also unexpected events in general; and (2) provides guidance to ensure the design of algorithms to deal with the uncertainty that reflects the actualities of practice.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"20 ","pages":"Article 100586"},"PeriodicalIF":6.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.1016/j.dibe.2024.100584
Xiaohua Liu , Lu Deng , Henglong Zhang , Jiangmiao Yu
The recycled asphalt technology is considered to have environmental sustainability prospects due to the resource conservation of old material recycling. Nonetheless, how to quantitatively evaluate the environmental impact of the entire life cycle of recycled asphalt pavement (RAP) still needs to be sorted out. Based on the theory of life cycle management, through the Life-cycle assessment (LCA) method, this study establishes a quantitative assessment model for the environmental impact of recycled asphalt pavement. A quantitative assessment model is established for the full life cycle environmental impact of recycled asphalt pavement. The model can output a list of environmental impacts for each stage of the life cycle, and can also conduct characteristic impact assessments based on five major impact categories: energy consumption (EC), global warming potential (GWP), acidification potential (AP), human health hazards (HTP), and particulate matter emissions. The results indicate that the acquisition of raw materials is the dominant stage for the environmental impact of cold recycled asphalt pavement, with a proportion of over 50% for each major impact category. In the construction of highways, using recycled modified asphalt mixture can reduce the total emissions by 12,976 kg per kilometer. In addition, the life cycle inventory (LCI) analysis shows that the environmental impact of recycled asphalt pavement is mainly quantified by energy consumption and various pollutant emissions, such as CO2, CH4, SO2, CO, N2O, NMVOC, particulate matter, and asphalt smoke. The raw material extraction stage has been identified as the stage with the greatest environmental impact, making significant contributions in energy consumption, global warming potential, acidification potential, human health hazards, and particulate matter emissions. This indicates that utilizing cold recycling technology and increasing the use of recycled RAP materials are efficient ways to promote energy conservation, reduce emissions, and minimize the environmental impact of asphalt pavement throughout its lifecycle.
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