Pub Date : 2025-01-02DOI: 10.1016/j.autcon.2024.105954
Diego Calvetti, Pedro Mêda, Eilif Hjelseth, Hipólito de Sousa
Digital Twins (DTw) in the construction industry combine multiple digital concepts aimed at achieving high levels of automation. While the industry pursues digital transition, professionals struggle to implement DTw due to their complexity and lack of standards. An incremental approach to deploying DTw can enable phased implementations, reducing costs and delivering faster outcomes. This paper applies Design Science Research (DSR) to develop, test, and improve an incremental Digital Twin (iDTw) framework for practical deployment. The iDTw is demonstrated and evaluated over three diversified use cases (Municipality Implementations, Residential House and Industrial Facility Operation) provided by experienced professionals from different backgrounds. With-case and cross-case analyses are conducted. iDTw results gave proper responses for the use cases, demonstrating the capability to drive awareness of DTw implementation. Finally, the iDTw combines theory and practice by offering a structured approach for assessing DTw smartness levels and tailored responses, bridging theoretical concepts with real-world applications.
{"title":"Incremental digital twin framework: A design science research approach for practical deployment","authors":"Diego Calvetti, Pedro Mêda, Eilif Hjelseth, Hipólito de Sousa","doi":"10.1016/j.autcon.2024.105954","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105954","url":null,"abstract":"Digital Twins (DTw) in the construction industry combine multiple digital concepts aimed at achieving high levels of automation. While the industry pursues digital transition, professionals struggle to implement DTw due to their complexity and lack of standards. An incremental approach to deploying DTw can enable phased implementations, reducing costs and delivering faster outcomes. This paper applies Design Science Research (DSR) to develop, test, and improve an incremental Digital Twin (iDTw) framework for practical deployment. The iDTw is demonstrated and evaluated over three diversified use cases (Municipality Implementations, Residential House and Industrial Facility Operation) provided by experienced professionals from different backgrounds. With-case and cross-case analyses are conducted. iDTw results gave proper responses for the use cases, demonstrating the capability to drive awareness of DTw implementation. Finally, the iDTw combines theory and practice by offering a structured approach for assessing DTw smartness levels and tailored responses, bridging theoretical concepts with real-world applications.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"13 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918042","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}
In infrastructure construction, engineering drawings combine graphic and textual information, with text playing a critical role in retrieving and measuring the similarity of these drawings in practical applications. However, existing research primarily focuses on graphics, neglecting the extraction and semantic representation of text. Existing Optical Character Recognition (OCR)-based methods face challenges in clustering text into coherent semantic modules, frequently dispersing related text across different regions. Therefore, this paper proposes a deep learning framework for the semantic extraction of text from engineering drawings. By integrating textual, positional, and image features, this framework enables semantic extraction and represents engineering drawings as knowledge graphs. An interactive attention-based approach is employed for associative retrieval of engineering drawings via subgraph matching. Evaluation on datasets from a transportation design institute and public sources demonstrates the framework's effectiveness in both semantic extraction and relational reasoning.
{"title":"Associative reasoning for engineering drawings using an interactive attention mechanism","authors":"Xu Xuesong, Xiao Gang, Sun Li, Zhang Xia, Wu Peixi, Zhang Yuanming, Cheng Zhenbo","doi":"10.1016/j.autcon.2024.105942","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105942","url":null,"abstract":"In infrastructure construction, engineering drawings combine graphic and textual information, with text playing a critical role in retrieving and measuring the similarity of these drawings in practical applications. However, existing research primarily focuses on graphics, neglecting the extraction and semantic representation of text. Existing Optical Character Recognition (OCR)-based methods face challenges in clustering text into coherent semantic modules, frequently dispersing related text across different regions. Therefore, this paper proposes a deep learning framework for the semantic extraction of text from engineering drawings. By integrating textual, positional, and image features, this framework enables semantic extraction and represents engineering drawings as knowledge graphs. An interactive attention-based approach is employed for associative retrieval of engineering drawings via subgraph matching. Evaluation on datasets from a transportation design institute and public sources demonstrates the framework's effectiveness in both semantic extraction and relational reasoning.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"2019 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918043","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 : 2025-01-02DOI: 10.1016/j.autcon.2024.105929
Lu Wan, Ferdinand Rossa, Torsten Welfonder, Ekaterina Petrova, Pieter Pauwels
Model Predictive Control (MPC) is a promising optimal control technique to reduce the energy consumption of Heating, Ventilation, and Air Conditioning systems in buildings. However, MPC currently involves significant manual efforts in data preparation, control model design, and software interface design. Better semantic representations of buildings, their systems, and telemetry data could help address these challenges. This paper proposes a standard semantic information model and tooling, tailored to BIM software, to streamline MPC design. The approach is tested in an office building, and the generated semantic graph is validated against a use case, where an MPC controller uses Resistance and Capacitance (RC) models that need to be parameterized. The results show that the automatically identified RC models achieve three-hour-ahead temperature predictions for two different rooms within 0.3 °C accuracy. This indicates that semantic data modelling can enable a scalable MPC configuration workflow and more efficient algorithm development and deployment in the future.
{"title":"Enabling scalable Model Predictive Control design for building HVAC systems using semantic data modelling","authors":"Lu Wan, Ferdinand Rossa, Torsten Welfonder, Ekaterina Petrova, Pieter Pauwels","doi":"10.1016/j.autcon.2024.105929","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105929","url":null,"abstract":"Model Predictive Control (MPC) is a promising optimal control technique to reduce the energy consumption of Heating, Ventilation, and Air Conditioning systems in buildings. However, MPC currently involves significant manual efforts in data preparation, control model design, and software interface design. Better semantic representations of buildings, their systems, and telemetry data could help address these challenges. This paper proposes a standard semantic information model and tooling, tailored to BIM software, to streamline MPC design. The approach is tested in an office building, and the generated semantic graph is validated against a use case, where an MPC controller uses Resistance and Capacitance (RC) models that need to be parameterized. The results show that the automatically identified RC models achieve three-hour-ahead temperature predictions for two different rooms within 0.3 °C accuracy. This indicates that semantic data modelling can enable a scalable MPC configuration workflow and more efficient algorithm development and deployment in the future.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"37 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918045","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 : 2025-01-01DOI: 10.2174/1871527321666220530093927
Youness Kadil, Houda Filali
Since the authors are not responding to the editor’s requests to fulfill the editorial requirement, therefore, the article has been withdrawn.
Bentham Science apologizes to the readers of the journal for any inconvenience this may have caused.
The Bentham Editorial Policy on Article Withdrawal can be found at https://benthamscience.com/editorial-policies-main.php
Bentham science disclaimer: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.
{"title":"WITHDRAWN: Nigella Fixed Oil Mitigates Memory Impairment in a Rat-Aged Model of Cognitive Decline; A Pivotal Role of BDNF and CREB\u0000Signaling Pathways in the Hippocampus","authors":"Youness Kadil, Houda Filali","doi":"10.2174/1871527321666220530093927","DOIUrl":"10.2174/1871527321666220530093927","url":null,"abstract":"<p><p>Since the authors are not responding to the editor’s requests to fulfill the editorial requirement, therefore, the article has been\u0000withdrawn.</p><p><p>Bentham Science apologizes to the readers of the journal for any inconvenience this may have caused.</p><p><p>The Bentham Editorial Policy on Article Withdrawal can be found at https://benthamscience.com/editorial-policies-main.php</p><p><strong>Bentham science disclaimer: </strong>It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously\u0000submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere\u0000must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting\u0000the article for publication the authors agree that the publishers have the legal right to take appropriate action against the\u0000authors, if plagiarism or fabricated information is discovered. By submitting a manuscript the authors agree that the copyright\u0000of their article is transferred to the publishers if and when the article is accepted for publication.</p>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"abs/2205.05518 1","pages":""},"PeriodicalIF":9.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75708814","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 : 2024-12-30DOI: 10.1016/j.autcon.2024.105941
Hai-Wei Wang, Rih-Teng Wu
Tile spalling poses significant threats to pedestrians on sidewalks. Recently, deep learning-based approaches have been developed for autonomous building assessments. However, training a supervised model typically requires a large labeled dataset, which is often unavailable in new domain tasks. Moreover, data acquisition and ground-truth labeling are costly. This paper presents an unsupervised framework for anomaly detection of tile spalling. The framework incorporates uncertainty estimation and contrastive learning by training a segmentation model on a source dataset containing known classes, excluding spalling. Spalling is subsequently identified as outlier pixels based on elevated uncertainty scores. Additionally, a synthetic pattern, dubbed “Spalling Craft”, is developed for outlier exposure to further enhance model performance. The proposed approach outperforms state-of-the-art baselines by approximately 18.4%, 46.6%, and 31.7% in AUC, AP, and FPR95 scores, respectively. Compared to supervised learning methods, the framework significantly improves data efficiency while achieving strong performance in tile spalling segmentation.
{"title":"Unsupervised anomaly detection for tile spalling segmentation using synthetic outlier exposure and contrastive learning","authors":"Hai-Wei Wang, Rih-Teng Wu","doi":"10.1016/j.autcon.2024.105941","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105941","url":null,"abstract":"Tile spalling poses significant threats to pedestrians on sidewalks. Recently, deep learning-based approaches have been developed for autonomous building assessments. However, training a supervised model typically requires a large labeled dataset, which is often unavailable in new domain tasks. Moreover, data acquisition and ground-truth labeling are costly. This paper presents an unsupervised framework for anomaly detection of tile spalling. The framework incorporates uncertainty estimation and contrastive learning by training a segmentation model on a source dataset containing known classes, excluding spalling. Spalling is subsequently identified as outlier pixels based on elevated uncertainty scores. Additionally, a synthetic pattern, dubbed “Spalling Craft”, is developed for outlier exposure to further enhance model performance. The proposed approach outperforms state-of-the-art baselines by approximately 18.4%, 46.6%, and 31.7% in AUC, AP, and FPR95 scores, respectively. Compared to supervised learning methods, the framework significantly improves data efficiency while achieving strong performance in tile spalling segmentation.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"14 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918061","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 : 2024-12-28DOI: 10.1016/j.autcon.2024.105945
Li Xu, Yang Zou, Yuqian Lu, Alice Chang-Richards
The integration of automation technologies has improved the efficiency of industrialised construction (IC), yet a deeper understanding of their effects on the manufacturing and assembly stages remains necessary. This paper provides a systematic review of how various automation technologies influence these key stages in IC, analysing 53 articles. It explores the deployment of 22 technologies, including the Internet of Things (IoT), deep learning, digital twins, and robotics, and identifies seven benefits for IC: (1) interoperability, (2) scheduling optimisation, (3) production traceability, (4) production safety, (5) manufacturability, (6) quality assurance, and (7) constructability. To further advance automation in IC, future research should address critical challenges, including enhancing data quality, expanding empirical testing, exploring emerging technologies in depth, and integrating fragmented workflows. This article underscores the need of strategic technology deployment to seamlessly integrate various processes in future construction practices, offering insights into the transformative potential of automation.
{"title":"Automation in manufacturing and assembly of industrialised construction","authors":"Li Xu, Yang Zou, Yuqian Lu, Alice Chang-Richards","doi":"10.1016/j.autcon.2024.105945","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105945","url":null,"abstract":"The integration of automation technologies has improved the efficiency of industrialised construction (IC), yet a deeper understanding of their effects on the manufacturing and assembly stages remains necessary. This paper provides a systematic review of how various automation technologies influence these key stages in IC, analysing 53 articles. It explores the deployment of 22 technologies, including the Internet of Things (IoT), deep learning, digital twins, and robotics, and identifies seven benefits for IC: (1) interoperability, (2) scheduling optimisation, (3) production traceability, (4) production safety, (5) manufacturability, (6) quality assurance, and (7) constructability. To further advance automation in IC, future research should address critical challenges, including enhancing data quality, expanding empirical testing, exploring emerging technologies in depth, and integrating fragmented workflows. This article underscores the need of strategic technology deployment to seamlessly integrate various processes in future construction practices, offering insights into the transformative potential of automation.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"34 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918010","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 : 2024-12-27DOI: 10.1016/j.autcon.2024.105951
Liangfu Ge, Ayan Sadhu
Ground robotic systems are essential for structural inspection, enhancing mobility, efficiency, and safety while minimizing risks in manual inspections. These systems automate 3D mapping and defect assessment in aging. However, current robotic platforms often require the integration of various sensors and complex parameter tuning, raising costs and limiting efficiency. This paper proposes a streamlined unmanned ground vehicle-based inspection platform, integrating only LiDAR and a low-cost monocular camera. Operated via the Robot Operating System, the platform deploys efficient instance segmentation, Simultaneous Localization and Mapping, and fusion algorithms, eliminating complex tuning across environments. A self-attention-enhanced YOLOv7 algorithm is proposed for accurate damage segmentation with limited datasets, while an enhanced KISS-ICP (Keep It Small and Simple-Iterative Closest Point) algorithm is developed to optimize point cloud odometry for efficient mapping and localization. By introducing camera-LiDAR information fusion, the proposed platform achieves structural mapping, damage localization, quantification, and 3D visualization. Laboratory and full-scale bridge tests demonstrated its high accuracy, efficiency, and robustness.
地面机器人系统对于结构检查至关重要，可以提高机动性、效率和安全性，同时最大限度地降低人工检查的风险。这些系统自动化三维绘图和老化缺陷评估。然而，目前的机器人平台往往需要集成各种传感器和复杂的参数调整，这提高了成本，限制了效率。本文提出了一种仅集成激光雷达和低成本单目摄像机的流线型无人地面车辆检测平台。该平台通过机器人操作系统运行，部署了高效的实例分割、同步定位和映射以及融合算法，消除了跨环境的复杂调优。提出了一种自关注增强的YOLOv7算法，用于在有限数据集下进行准确的损伤分割；开发了一种增强的KISS-ICP （Keep It Small and Simple-Iterative nearest Point）算法，用于优化点云测程，实现高效的映射和定位。该平台通过引入摄像头-激光雷达信息融合，实现了结构制图、损伤定位、量化和三维可视化。实验室和全尺寸桥梁测试证明了该方法的准确性、效率和稳健性。
{"title":"Deep learning-enhanced smart ground robotic system for automated structural damage inspection and mapping","authors":"Liangfu Ge, Ayan Sadhu","doi":"10.1016/j.autcon.2024.105951","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105951","url":null,"abstract":"Ground robotic systems are essential for structural inspection, enhancing mobility, efficiency, and safety while minimizing risks in manual inspections. These systems automate 3D mapping and defect assessment in aging. However, current robotic platforms often require the integration of various sensors and complex parameter tuning, raising costs and limiting efficiency. This paper proposes a streamlined unmanned ground vehicle-based inspection platform, integrating only LiDAR and a low-cost monocular camera. Operated via the Robot Operating System, the platform deploys efficient instance segmentation, Simultaneous Localization and Mapping, and fusion algorithms, eliminating complex tuning across environments. A self-attention-enhanced YOLOv7 algorithm is proposed for accurate damage segmentation with limited datasets, while an enhanced KISS-ICP (Keep It Small and Simple-Iterative Closest Point) algorithm is developed to optimize point cloud odometry for efficient mapping and localization. By introducing camera-LiDAR information fusion, the proposed platform achieves structural mapping, damage localization, quantification, and 3D visualization. Laboratory and full-scale bridge tests demonstrated its high accuracy, efficiency, and robustness.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"31 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918070","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}
Real-time prefabricated construction faces challenges in robot planning using Building Information Modeling (BIM) due to the need for temporary structure stability. Existing static analysis methods fail to account for dynamic changes and uncertainties. This paper introduces a framework to streamline construction planning, focusing on real-time stability checks for prefabricated structures. Construction procedures are defined in BIM, with structural information automatically transferred to the Finite Element Method (FEM) domain via the BIM-2-FEM converter for stability analysis based on Eurocode standards. Upon successful stability checks, construction is implemented using Robot Operating System (ROS). A case study validates the framework, showcasing improved decision-making, efficiency, and structural integrity through procedures like component assembly and selection optimization. By integrating BIM, FEM, and ROS, this framework enables efficient planning and real-time structural analysis for prefabricated and temporary structures, ultimately enhancing construction productivity and safety.
{"title":"FEM-based real-time task planning for robotic construction simulation","authors":"Qingfeng Xu, Aiyu Zhu, Gangyan Xu, Zimu Shao, Junjun Zhang, Hong Zhang","doi":"10.1016/j.autcon.2024.105935","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105935","url":null,"abstract":"Real-time prefabricated construction faces challenges in robot planning using Building Information Modeling (BIM) due to the need for temporary structure stability. Existing static analysis methods fail to account for dynamic changes and uncertainties. This paper introduces a framework to streamline construction planning, focusing on real-time stability checks for prefabricated structures. Construction procedures are defined in BIM, with structural information automatically transferred to the Finite Element Method (FEM) domain via the BIM-2-FEM converter for stability analysis based on Eurocode standards. Upon successful stability checks, construction is implemented using Robot Operating System (ROS). A case study validates the framework, showcasing improved decision-making, efficiency, and structural integrity through procedures like component assembly and selection optimization. By integrating BIM, FEM, and ROS, this framework enables efficient planning and real-time structural analysis for prefabricated and temporary structures, ultimately enhancing construction productivity and safety.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"25 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888276","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 : 2024-12-24DOI: 10.1016/j.autcon.2024.105943
Hongyu Chen, Jun Liu, Geoffrey Qiping Shen, Zongbao Feng
A hybrid intelligent framework is proposed in this paper to reduce the existing tunnel deformation caused by shield adjacent undercrossing construction (SAUC). A Bayesian optimization natural gradient boosting (BO-NGBoost) model for existing tunnel deformation prediction is developed, and the Shapley additive explanations (SHAP) approach is used to analyze the interpretability of the prediction model. The multiobjective evolutionary algorithm based on decomposition (MOEA/D) is used to optimize the construction parameters. The applicability and validity of the proposed method are tested in a case study from the Wuhan Metro. The results indicate that (1) the established BO-NGBoost existing tunnel deformation prediction model shows high accuracy. (2) Through SHAP analysis, the importance of each input parameter to the existing tunnel deformation is identified, and the key shield optimization parameters are defined. (3) By using the developed BO-NGBoost-MOEA/D algorithm to optimize the key parameters, the existing tunnel deformation is effectively controlled.
{"title":"Control of existing tunnel deformation caused by shield adjacent undercrossing construction using interpretable machine learning and multiobjective optimization","authors":"Hongyu Chen, Jun Liu, Geoffrey Qiping Shen, Zongbao Feng","doi":"10.1016/j.autcon.2024.105943","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105943","url":null,"abstract":"A hybrid intelligent framework is proposed in this paper to reduce the existing tunnel deformation caused by shield adjacent undercrossing construction (SAUC). A Bayesian optimization natural gradient boosting (BO-NGBoost) model for existing tunnel deformation prediction is developed, and the Shapley additive explanations (SHAP) approach is used to analyze the interpretability of the prediction model. The multiobjective evolutionary algorithm based on decomposition (MOEA/D) is used to optimize the construction parameters. The applicability and validity of the proposed method are tested in a case study from the Wuhan Metro. The results indicate that (1) the established BO-NGBoost existing tunnel deformation prediction model shows high accuracy. (2) Through SHAP analysis, the importance of each input parameter to the existing tunnel deformation is identified, and the key shield optimization parameters are defined. (3) By using the developed BO-NGBoost-MOEA/D algorithm to optimize the key parameters, the existing tunnel deformation is effectively controlled.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"42 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888983","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 : 2024-12-24DOI: 10.1016/j.autcon.2024.105949
Difeng Hu, Vincent J.L. Gan
Maintaining a comfortable indoor environment is essential for enhancing occupant well-being. However, traditional inspection methods rely on manual input of precise coordinates for target objects, limiting efficiency. This paper proposes a semantic navigation approach to improve robotic inspection intelligence and efficiency. A revised RandLA-Net and KNN algorithm construct a semantic map rich in detailed object information, supporting semantic navigation. Subsequently, an object instance reasoning algorithm automatically identifies and extracts target object coordinates from the semantic map using human-like language commands. Given the position information, a semantics-aware A* algorithm calculates safer, more efficient navigation paths through enhanced robot-environment interaction. Experiments demonstrate a position accuracy of ∼0.08 m for objects in the semantic map and effective coordinate extraction by the reasoning algorithm. The semantics-aware A* algorithm generates paths farther from obstacles and cluttered areas with less computational time, indicating its superior performance in terms of the robot's safety and inspection efficiency.
{"title":"Semantic navigation for automated robotic inspection and indoor environment quality monitoring","authors":"Difeng Hu, Vincent J.L. Gan","doi":"10.1016/j.autcon.2024.105949","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105949","url":null,"abstract":"Maintaining a comfortable indoor environment is essential for enhancing occupant well-being. However, traditional inspection methods rely on manual input of precise coordinates for target objects, limiting efficiency. This paper proposes a semantic navigation approach to improve robotic inspection intelligence and efficiency. A revised RandLA-Net and KNN algorithm construct a semantic map rich in detailed object information, supporting semantic navigation. Subsequently, an object instance reasoning algorithm automatically identifies and extracts target object coordinates from the semantic map using human-like language commands. Given the position information, a semantics-aware A* algorithm calculates safer, more efficient navigation paths through enhanced robot-environment interaction. Experiments demonstrate a position accuracy of ∼0.08 m for objects in the semantic map and effective coordinate extraction by the reasoning algorithm. The semantics-aware A* algorithm generates paths farther from obstacles and cluttered areas with less computational time, indicating its superior performance in terms of the robot's safety and inspection efficiency.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"19 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888278","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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