Varying results in cutter wear and cutting performance can be observed based on different selections of shield operational parameters, particularly in hard rock or soil with a high quartz content. Improperly selecting operational parameters may result in excessive wear and reduced cutting performance, leading to longer project duration and increased costs. Furthermore, it is still challenging to balance cutter wear and cutting performance. To address these issues, a multi-objective optimization (MOO) framework based on the Light Gradient Boosting Machine (LightGBM) algorithm and the enhanced non-dominated sorting genetic-II (NSGA-II) algorithm is proposed to predict and optimize the cutter wear and cutting performance. To validate this framework, a shield tunneling project in China is presented. The results show that the efficiency and accuracy of predicting and optimizing the two objectives have been improved compared with other common methods. This MOO framework is valuable for operators to formulate rational operational control strategies.
{"title":"Multi-objective optimization control for shield cutter wear and cutting performance using LightGBM and enhanced NSGA-II","authors":"Ziwei Yin, Jianwei Jiao, Ping Xie, Hanbin Luo, Linchun Wei","doi":"10.1016/j.autcon.2024.105957","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105957","url":null,"abstract":"Varying results in cutter wear and cutting performance can be observed based on different selections of shield operational parameters, particularly in hard rock or soil with a high quartz content. Improperly selecting operational parameters may result in excessive wear and reduced cutting performance, leading to longer project duration and increased costs. Furthermore, it is still challenging to balance cutter wear and cutting performance. To address these issues, a multi-objective optimization (MOO) framework based on the Light Gradient Boosting Machine (LightGBM) algorithm and the enhanced non-dominated sorting genetic-II (NSGA-II) algorithm is proposed to predict and optimize the cutter wear and cutting performance. To validate this framework, a shield tunneling project in China is presented. The results show that the efficiency and accuracy of predicting and optimizing the two objectives have been improved compared with other common methods. This MOO framework is valuable for operators to formulate rational operational control strategies.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"96 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142987831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-01-10DOI: 10.1016/j.autcon.2024.105956
Limao Zhang, Junwei Ma, Jiaqi Wang, Qing Sun, Hui Yang
Aerial building machine (ABM) is a climbing formwork-based mechanical equipment, the design of which has been limited by cumbersome processes, insufficient intelligence, and conservative structures. This paper proposes a flexible design framework incorporating multiple reinforcement measures to optimize ABM structures under various wind conditions. Using parametric modeling and multi-objective optimization (MOO), the framework generates lightweight design solutions tailored to specific scenarios. An ABM project in China demonstrates the approach, producing a scheme set of four structures capable of withstanding extreme wind loads with stress ratios below 1.0. Compared to robust designs, the flexible method reduces steel consumption by 12.45 % during construction and 7.02 % under extreme wind conditions. Among reinforcement measures, the pin shaft and supporting point offer the best cost efficiency (21.95), while diagonal bracing performs the least favorably (3.82). The contributions of this research lie in introducing flexibility into ABM design through multiple local reinforcement measures.
{"title":"Multi-objective optimization for flexible design of aerial building machine under various wind conditions","authors":"Limao Zhang, Junwei Ma, Jiaqi Wang, Qing Sun, Hui Yang","doi":"10.1016/j.autcon.2024.105956","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105956","url":null,"abstract":"Aerial building machine (ABM) is a climbing formwork-based mechanical equipment, the design of which has been limited by cumbersome processes, insufficient intelligence, and conservative structures. This paper proposes a flexible design framework incorporating multiple reinforcement measures to optimize ABM structures under various wind conditions. Using parametric modeling and multi-objective optimization (MOO), the framework generates lightweight design solutions tailored to specific scenarios. An ABM project in China demonstrates the approach, producing a scheme set of four structures capable of withstanding extreme wind loads with stress ratios below 1.0. Compared to robust designs, the flexible method reduces steel consumption by 12.45 % during construction and 7.02 % under extreme wind conditions. Among reinforcement measures, the pin shaft and supporting point offer the best cost efficiency (21.95), while diagonal bracing performs the least favorably (3.82). The contributions of this research lie in introducing flexibility into ABM design through multiple local reinforcement measures.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"6 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-01-10DOI: 10.1016/j.autcon.2024.105950
Liao Jian, Wenge Qiu, Yunjian Cheng
Mobile measurements can rapidly acquire tunnel information. However, cumulative errors in yaw angles occur in the absence or weakness of global positioning system (GPS) signals. This paper presents a method for 3D reconstruction of shield tunnels based on tunnel centerlines using non-repeating spinning mid-range LiDAR (SML) points and photos. First, a low-cost mobile measurement system (MMS) was built. Subsequently, the raw data were transformed into the tunnel centerline coordinate system (TCCS), including coarse registration with centerline alignment and fine registration based on convex hull areas. The multi-sensor data were fused in the TCCS, and photos were projected onto the SML points and unwrapped. Kilometrage corrections were applied by weighting the errors between the survey control points on panoramic images and their geodetic coordinates. Finally, the reconstructed data were located using image segmentation and indexing. This approach demonstrates higher registration accuracy in subway scenes than mainstream algorithms.
{"title":"Centerline-based registration for shield tunnel 3D reconstruction using spinning mid-range LiDAR point cloud and multi-cameras","authors":"Liao Jian, Wenge Qiu, Yunjian Cheng","doi":"10.1016/j.autcon.2024.105950","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105950","url":null,"abstract":"Mobile measurements can rapidly acquire tunnel information. However, cumulative errors in yaw angles occur in the absence or weakness of global positioning system (GPS) signals. This paper presents a method for 3D reconstruction of shield tunnels based on tunnel centerlines using non-repeating spinning mid-range LiDAR (SML) points and photos. First, a low-cost mobile measurement system (MMS) was built. Subsequently, the raw data were transformed into the tunnel centerline coordinate system (TCCS), including coarse registration with centerline alignment and fine registration based on convex hull areas. The multi-sensor data were fused in the TCCS, and photos were projected onto the SML points and unwrapped. Kilometrage corrections were applied by weighting the errors between the survey control points on panoramic images and their geodetic coordinates. Finally, the reconstructed data were located using image segmentation and indexing. This approach demonstrates higher registration accuracy in subway scenes than mainstream algorithms.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"11 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142988195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-01-08DOI: 10.1016/j.autcon.2024.105946
Sudao He, Gang Zhao, Jun Chen, Shenghan Zhang, Dhanada Mishra, Matthew Ming-Fai Yuen
Infrared (IR) thermography combined with Unmanned Aerial Vehicles (UAVs) offers an innovative approach for automated building façades inspections. However, extracting quantitative defect information from a single image poses a significant challenge. To address this, this paper introduces a Weakly-aligned Cross-modal Learning framework for subsurface defect segmentation using UAVs. This framework consists of two main components: the Multimodal Feature Description Network (MFDN) and the Prompt-aided Cross-modal Graph Learning (PCGL) algorithm. Initially, RGB–IR image pairs are processed by MFDN to extract feature descriptors for multi-modal alignment. The PCGL algorithm identifies visually critical areas through graph partitioning on a Wasserstein graph. These critical areas are transferred to the aligned IR image, and a Wasserstein Adjacency Graph (WAG) is constructed based on masked superpixel segmentation. Finally, the defects contours are pinpointed by detecting abnormal vertices of the WAG. The effectiveness is validated through controlled laboratory experiments and field applications on tiled façades.
{"title":"Weakly-aligned cross-modal learning framework for subsurface defect segmentation on building façades using UAVs","authors":"Sudao He, Gang Zhao, Jun Chen, Shenghan Zhang, Dhanada Mishra, Matthew Ming-Fai Yuen","doi":"10.1016/j.autcon.2024.105946","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105946","url":null,"abstract":"Infrared (IR) thermography combined with Unmanned Aerial Vehicles (UAVs) offers an innovative approach for automated building façades inspections. However, extracting quantitative defect information from a single image poses a significant challenge. To address this, this paper introduces a Weakly-aligned Cross-modal Learning framework for subsurface defect segmentation using UAVs. This framework consists of two main components: the Multimodal Feature Description Network (MFDN) and the Prompt-aided Cross-modal Graph Learning (PCGL) algorithm. Initially, RGB–IR image pairs are processed by MFDN to extract feature descriptors for multi-modal alignment. The PCGL algorithm identifies visually critical areas through graph partitioning on a Wasserstein graph. These critical areas are transferred to the aligned IR image, and a Wasserstein Adjacency Graph (WAG) is constructed based on masked superpixel segmentation. Finally, the defects contours are pinpointed by detecting abnormal vertices of the WAG. The effectiveness is validated through controlled laboratory experiments and field applications on tiled façades.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"6 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-01-08DOI: 10.1016/j.autcon.2024.105924
Ranjith K. Soman, Karim Farghaly, Grant Mills, Jennifer Whyte
Despite the growing emphasis on digital twins in construction, there is limited understanding of how to enable effective human interaction with these systems, limiting their potential to augment decision-making. This paper investigates the research question: “How can construction control rooms be utilized as digital twin interfaces to enhance the accuracy and efficiency of decision-making in the digital twin construction workflow?”. Design science research was used to develop a framework for human-digital twin interfaces, and it was evaluated in a real-world construction project. Findings reveal that control rooms can serve as dynamic interfaces within the digital twin ecosystem, improving coordination efficiency and decision-making accuracy. This finding is significant for practitioners and researchers, as it highlights the role of digital twin interfaces in augmenting decision-making. The paper opens avenues for future studies of human-digital twin interaction and machine learning in construction, such as imitation learning, codifying tacit knowledge, and new HCI paradigms.
{"title":"Digital twin construction with a focus on human twin interfaces","authors":"Ranjith K. Soman, Karim Farghaly, Grant Mills, Jennifer Whyte","doi":"10.1016/j.autcon.2024.105924","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105924","url":null,"abstract":"Despite the growing emphasis on digital twins in construction, there is limited understanding of how to enable effective human interaction with these systems, limiting their potential to augment decision-making. This paper investigates the research question: “How can construction control rooms be utilized as digital twin interfaces to enhance the accuracy and efficiency of decision-making in the digital twin construction workflow?”. Design science research was used to develop a framework for human-digital twin interfaces, and it was evaluated in a real-world construction project. Findings reveal that control rooms can serve as dynamic interfaces within the digital twin ecosystem, improving coordination efficiency and decision-making accuracy. This finding is significant for practitioners and researchers, as it highlights the role of digital twin interfaces in augmenting decision-making. The paper opens avenues for future studies of human-digital twin interaction and machine learning in construction, such as imitation learning, codifying tacit knowledge, and new HCI paradigms.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"84 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-01-08DOI: 10.1016/j.autcon.2024.105952
Robin Oval, John Orr, Paul Shepherd
Reinforced concrete is a major contributor to the environmental impact of the construction industry, due not only to its cement content, but also its steel tensile reinforcement, estimated to represent around 40% of the material embodied carbon. Reinforcement has a significant contribution because of construction rationalisation, resulting in regular cages of steel bars, despite the availability of structural-optimisation algorithms and additive-manufacturing technologies. This paper fuses computational design and digital fabrication, to optimise the reinforcement layout of concrete structures, by designing with constrained layout optimisation of strut-and-tie models where the ties are produced with robotic filament winding. The methodology is presented, implemented in open-source code, and illustrated on beam and plate reinforcement applications. The numerical studies yield a discussion about parameter selection and constraint influence on material and construction efficiency trade-offs. Small-scale physical prototypes up to 50 cm × 50 cm provide a proof-of-concept.
{"title":"Structural design and fabrication of concrete reinforcement with layout optimisation and robotic filament winding","authors":"Robin Oval, John Orr, Paul Shepherd","doi":"10.1016/j.autcon.2024.105952","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105952","url":null,"abstract":"Reinforced concrete is a major contributor to the environmental impact of the construction industry, due not only to its cement content, but also its steel tensile reinforcement, estimated to represent around 40% of the material embodied carbon. Reinforcement has a significant contribution because of construction rationalisation, resulting in regular cages of steel bars, despite the availability of structural-optimisation algorithms and additive-manufacturing technologies. This paper fuses computational design and digital fabrication, to optimise the reinforcement layout of concrete structures, by designing with constrained layout optimisation of strut-and-tie models where the ties are produced with robotic filament winding. The methodology is presented, implemented in open-source code, and illustrated on beam and plate reinforcement applications. The numerical studies yield a discussion about parameter selection and constraint influence on material and construction efficiency trade-offs. Small-scale physical prototypes up to 50 cm <mml:math altimg=\"si1.svg\" display=\"inline\"><mml:mo>×</mml:mo></mml:math> 50 cm provide a proof-of-concept.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"41 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-01-08DOI: 10.1016/j.autcon.2024.105959
Samira Azhari, Sara Jamshidian, Mohammadjavad Hamidia
Manual visual inspection is the conventional method for post-earthquake damage assessment, which is unsafe, subjective, and prone to human error. This paper presents an automated rapid and non-contact seismic damage state prediction methodology for reinforced concrete columns using crack image analysis. For surface damage quantification, three features of crack texture complexity including percolation, heterogeneity, and Renyi entropy-based dimensions are measured. Various shallow- and deep-learning-rooted algorithms are trained using a large collected experimental database to develop FEMA P-58-compliant repair activity predictive models. Based on the structural parameters, geometric features, and image-extracted indices, 10 groups of input features are defined. For the overfitting assessment and generalizability evaluation of models, five-fold cross-validations are conducted. Among shallow learning-based algorithms, CatBoost algorithm performs best for the scenarios that rely on vision-derived intricacy indices. Using the deep learning-based multilayer perceptron model as a feedforward artificial neural network, 92 % accuracy is achieved for the testing dataset.
{"title":"AI-driven computer vision-based automated repair activity identification for seismically damaged RC columns","authors":"Samira Azhari, Sara Jamshidian, Mohammadjavad Hamidia","doi":"10.1016/j.autcon.2024.105959","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105959","url":null,"abstract":"Manual visual inspection is the conventional method for post-earthquake damage assessment, which is unsafe, subjective, and prone to human error. This paper presents an automated rapid and non-contact seismic damage state prediction methodology for reinforced concrete columns using crack image analysis. For surface damage quantification, three features of crack texture complexity including percolation, heterogeneity, and Renyi entropy-based dimensions are measured. Various shallow- and deep-learning-rooted algorithms are trained using a large collected experimental database to develop FEMA P-58-compliant repair activity predictive models. Based on the structural parameters, geometric features, and image-extracted indices, 10 groups of input features are defined. For the overfitting assessment and generalizability evaluation of models, five-fold cross-validations are conducted. Among shallow learning-based algorithms, CatBoost algorithm performs best for the scenarios that rely on vision-derived intricacy indices. Using the deep learning-based multilayer perceptron model as a feedforward artificial neural network, 92 % accuracy is achieved for the testing dataset.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"20 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-01-07DOI: 10.1016/j.autcon.2024.105960
June Moh Goo, Xenios Milidonis, Alessandro Artusi, Jan Boehm, Carlo Ciliberto
It is essential to detect and segment cracks in various infrastructures, such as roads and buildings, to ensure safety, longevity, and cost-effective maintenance. Despite deep learning advancements, precise crack detection across diverse conditions remains challenging. This paper introduces Hybrid-Segmentor, a deep learning model combining Convolutional Neural Networks-based and Transformer-based architectures to extract both fine-grained local features and global crack patterns, significantly enhancing crack detection for improved infrastructure maintenance. Hybrid-Segmentor, trained on a large custom dataset created by merging multiple open-source datasets, can accurately detect cracks on different types of surfaces, crack shapes, and sizes. The model demonstrates robustness and versatility by accurately detecting discontinuities, vague cracks, non-crack regions within crack areas, blurred images, and complex crack contours. Furthermore, when compared against other recent models for crack segmentation, the proposed model achieves state-of-the-art performance, significantly outperforming them across five key metrics: accuracy (0.971), precision (0.807), recall (0.756), F1-score (0.774), and IoU (0.631).
{"title":"Hybrid-Segmentor: Hybrid approach for automated fine-grained crack segmentation in civil infrastructure","authors":"June Moh Goo, Xenios Milidonis, Alessandro Artusi, Jan Boehm, Carlo Ciliberto","doi":"10.1016/j.autcon.2024.105960","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105960","url":null,"abstract":"It is essential to detect and segment cracks in various infrastructures, such as roads and buildings, to ensure safety, longevity, and cost-effective maintenance. Despite deep learning advancements, precise crack detection across diverse conditions remains challenging. This paper introduces Hybrid-Segmentor, a deep learning model combining Convolutional Neural Networks-based and Transformer-based architectures to extract both fine-grained local features and global crack patterns, significantly enhancing crack detection for improved infrastructure maintenance. Hybrid-Segmentor, trained on a large custom dataset created by merging multiple open-source datasets, can accurately detect cracks on different types of surfaces, crack shapes, and sizes. The model demonstrates robustness and versatility by accurately detecting discontinuities, vague cracks, non-crack regions within crack areas, blurred images, and complex crack contours. Furthermore, when compared against other recent models for crack segmentation, the proposed model achieves state-of-the-art performance, significantly outperforming them across five key metrics: accuracy (0.971), precision (0.807), recall (0.756), F1-score (0.774), and IoU (0.631).","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"75 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-01-03DOI: 10.1016/j.autcon.2024.105955
Nadiia Kopiika, Andreas Karavias, Pavlos Krassakis, Zehao Ye, Jelena Ninic, Nataliya Shakhovska, Sotirios Argyroudis, Stergios-Aristoteles Mitoulis
Critical infrastructure is vital for connectivity and economic growth but faces systemic threats from human-induced damage, climate change and natural disasters. Rapid, multi-scale damage assessments are essential, yet integrated, automated methodologies remain underdeveloped. This paper presents a multi-scale tiered approach, which addresses this gap, by demonstrating how automated damage characterisation can be achieved using digital technologies. The methodology is then applied and validated through a case study in Ukraine involving 17 bridges damaged by targeted human interventions. Technology is deployed across regional to component scales, integrating assessments using Sentinel-1 SAR images, crowdsourced data, and high-resolution images for deep learning to enable automatic damage detection and characterisation. The interferometric coherence difference and semantic segmentation of images are utilised in a tiered multi-scale approach to enhance the reliability of damage characterisation at various scales. This integrated methodology automates and accelerates decision-making, facilitating more efficient restoration and adaptation efforts and ultimately enhancing infrastructure resilience.
{"title":"Rapid post-disaster infrastructure damage characterisation using remote sensing and deep learning technologies: A tiered approach","authors":"Nadiia Kopiika, Andreas Karavias, Pavlos Krassakis, Zehao Ye, Jelena Ninic, Nataliya Shakhovska, Sotirios Argyroudis, Stergios-Aristoteles Mitoulis","doi":"10.1016/j.autcon.2024.105955","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105955","url":null,"abstract":"Critical infrastructure is vital for connectivity and economic growth but faces systemic threats from human-induced damage, climate change and natural disasters. Rapid, multi-scale damage assessments are essential, yet integrated, automated methodologies remain underdeveloped. This paper presents a multi-scale tiered approach, which addresses this gap, by demonstrating how automated damage characterisation can be achieved using digital technologies. The methodology is then applied and validated through a case study in Ukraine involving 17 bridges damaged by targeted human interventions. Technology is deployed across regional to component scales, integrating assessments using Sentinel-1 SAR images, crowdsourced data, and high-resolution images for deep learning to enable automatic damage detection and characterisation. The interferometric coherence difference and semantic segmentation of images are utilised in a tiered multi-scale approach to enhance the reliability of damage characterisation at various scales. This integrated methodology automates and accelerates decision-making, facilitating more efficient restoration and adaptation efforts and ultimately enhancing infrastructure resilience.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"29 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-01-03DOI: 10.1016/j.autcon.2024.105939
Xiang Wang, Ming Zhang, Yiyang Yang, Fu Xiao, Xiaowei Luo
Building fire safety equipment (BFSE) management is increasingly complex and time-consuming. The objective of this paper is to develop augmented reality (AR)-enabled systems for BFSE based on cognitive ergonomics theory and explore the impacts of AR interaction modes on enhancing inspection performance. An experiment was conducted with 48 participants divided into three groups: control group with no AR assistance, visual-based AR group, and audiovisual-based AR group. Results indicate that the developed AR applications improve work efficiency, with the audiovisual-based system achieving the best task performance in BFSE inspections. The developed AR applications reduced cognitive load during inspections, although participants using the audiovisual-based AR system reported higher cognitive load regarding time pressure compared to the visual-based group. The findings contribute to developing efficient, user-friendly BFSE systems and understanding AR interaction modes, further validating the role of audiovisual-based AR interactions in improving facility management efficiency as well as building inspection and maintenance.
建筑消防安全设备（BFSE）管理日益复杂和耗时。本文旨在基于认知工效学理论，为 BFSE 开发支持增强现实（AR）的系统，并探索 AR 交互模式对提高检查性能的影响。实验将 48 名参与者分为三组：无 AR 辅助的对照组、基于视觉的 AR 组和基于视听的 AR 组。结果表明，所开发的 AR 应用程序提高了工作效率，其中基于视听的系统在 BFSE 检查中取得了最佳任务绩效。所开发的 AR 应用程序降低了检查过程中的认知负荷，尽管与基于视觉的组相比，使用基于视听的 AR 系统的参与者在时间压力方面的认知负荷更高。研究结果有助于开发高效、用户友好的 BFSE 系统和了解 AR 交互模式，进一步验证了基于视听的 AR 交互在提高设施管理效率以及建筑检测和维护方面的作用。
{"title":"Enhancing building fire safety inspections with cognitive ergonomics-driven augmented reality: Impact of interaction modes","authors":"Xiang Wang, Ming Zhang, Yiyang Yang, Fu Xiao, Xiaowei Luo","doi":"10.1016/j.autcon.2024.105939","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105939","url":null,"abstract":"Building fire safety equipment (BFSE) management is increasingly complex and time-consuming. The objective of this paper is to develop augmented reality (AR)-enabled systems for BFSE based on cognitive ergonomics theory and explore the impacts of AR interaction modes on enhancing inspection performance. An experiment was conducted with 48 participants divided into three groups: control group with no AR assistance, visual-based AR group, and audiovisual-based AR group. Results indicate that the developed AR applications improve work efficiency, with the audiovisual-based system achieving the best task performance in BFSE inspections. The developed AR applications reduced cognitive load during inspections, although participants using the audiovisual-based AR system reported higher cognitive load regarding time pressure compared to the visual-based group. The findings contribute to developing efficient, user-friendly BFSE systems and understanding AR interaction modes, further validating the role of audiovisual-based AR interactions in improving facility management efficiency as well as building inspection and maintenance.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"26 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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