Pub Date : 2026-02-01Epub Date: 2025-12-26DOI: 10.1016/j.autcon.2025.106730
Ben Huang , Fei Kang , Xi Liu
Accurate damage detection is critical for ensuring the safety and long-term stability of dams. However, conventional inspection methods often suffer from low automation, high labor intensity, and high costs. To address these limitations, this paper proposes an intelligent detection system based on an enhanced YOLOX framework, designed for real-time identification of multiple damage types in concrete dams using unmanned aerial vehicles (UAVs). The improved model is lightweight, containing only 8.94 million parameters, yet achieves a mAP50 of 0.821 and an F1-score of 0.781. Based on this model, detection software was implemented with the PyQt5 framework, and an integrated UAV-based system was constructed to support high-precision, real-time analysis of both image and video data. This approach provides an automated and intelligent solution for the visual inspection of concrete dam damage, offering significant potential for practical engineering applications and future intelligent monitoring systems.
{"title":"Intelligent UAV-based deep learning system for multi-class concrete dam damage detection","authors":"Ben Huang , Fei Kang , Xi Liu","doi":"10.1016/j.autcon.2025.106730","DOIUrl":"10.1016/j.autcon.2025.106730","url":null,"abstract":"<div><div>Accurate damage detection is critical for ensuring the safety and long-term stability of dams. However, conventional inspection methods often suffer from low automation, high labor intensity, and high costs. To address these limitations, this paper proposes an intelligent detection system based on an enhanced YOLOX framework, designed for real-time identification of multiple damage types in concrete dams using unmanned aerial vehicles (UAVs). The improved model is lightweight, containing only 8.94 million parameters, yet achieves a mAP<sub>50</sub> of 0.821 and an <em>F</em><sub>1</sub>-score of 0.781. Based on this model, detection software was implemented with the PyQt5 framework, and an integrated UAV-based system was constructed to support high-precision, real-time analysis of both image and video data. This approach provides an automated and intelligent solution for the visual inspection of concrete dam damage, offering significant potential for practical engineering applications and future intelligent monitoring systems.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106730"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145837257","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 : 2026-02-01Epub Date: 2026-01-03DOI: 10.1016/j.autcon.2025.106752
Chen Zhang , Dhanada K. Mishra , Matthew M.F. Yuen , Yantao Yu , Jize Zhang
Accurate pixel-level segmentation of concrete spalling has been severely hampered by the prohibitive cost of manual annotation. This paper investigates how accurate pixel-level defect segmentation can be achieved using only low-cost weakly supervised bounding box annotations. A three-stage framework is proposed to generate and refine pseudo-masks from bounding boxes using the Segment Anything Model (SAM), dynamic self-correction, and inference-time fusion. The proposed method outperformed existing techniques by over 10% in F1 score on a large-scale spalling dataset. These findings establish the economic viability of deploying scalable automated inspection systems by drastically reducing data annotation costs, providing a practical and scalable pathway for spalling assessment.
{"title":"Accurate concrete spalling segmentation from bounding box supervision using Segment Anything","authors":"Chen Zhang , Dhanada K. Mishra , Matthew M.F. Yuen , Yantao Yu , Jize Zhang","doi":"10.1016/j.autcon.2025.106752","DOIUrl":"10.1016/j.autcon.2025.106752","url":null,"abstract":"<div><div>Accurate pixel-level segmentation of concrete spalling has been severely hampered by the prohibitive cost of manual annotation. This paper investigates how accurate pixel-level defect segmentation can be achieved using only low-cost weakly supervised bounding box annotations. A three-stage framework is proposed to generate and refine pseudo-masks from bounding boxes using the Segment Anything Model (SAM), dynamic self-correction, and inference-time fusion. The proposed method outperformed existing techniques by over 10% in F1 score on a large-scale spalling dataset. These findings establish the economic viability of deploying scalable automated inspection systems by drastically reducing data annotation costs, providing a practical and scalable pathway for spalling assessment.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106752"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880901","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 : 2026-02-01Epub Date: 2026-01-06DOI: 10.1016/j.autcon.2025.106740
Meihao Zhu , Zhansheng Liu , Weiyi Li , Song Wang , Qingwen Zhang
Task allocation for multi-robot construction systems in unknown environments often has limited adaptability, high computational cost, and inefficient exploratory mapping. To address these issues, this paper presents an Improved Wavefront Frontier Detection–Utility Value (I-WFD-UV) task allocation framework for collaborative environmental exploration. The method integrates: (1) a collision-detection system using a bounding volume hierarchy for multi-category construction obstacle recognition; (2) a centroid-point extraction technique with frontier filtering to reduce computational complexity; and (3) a set of task allocation strategies incorporating discounted information gain, improved movement cost, angle-based attractiveness, and a forced distance maximized distribution to optimize multi-robot distribution. Integrating digital twin technology further enhances the practicality of the solution. Ablation studies validate the effectiveness and efficiency of the presented method across multiple simulation scenarios involving scaled cable-truss structures. This method provides an efficient and reliable solution for collaborative exploration by multi-robot systems in complex construction environments.
{"title":"Improved wavefront frontier detection-utility value task allocation for multi-robot collaborative environmental exploration","authors":"Meihao Zhu , Zhansheng Liu , Weiyi Li , Song Wang , Qingwen Zhang","doi":"10.1016/j.autcon.2025.106740","DOIUrl":"10.1016/j.autcon.2025.106740","url":null,"abstract":"<div><div>Task allocation for multi-robot construction systems in unknown environments often has limited adaptability, high computational cost, and inefficient exploratory mapping. To address these issues, this paper presents an Improved Wavefront Frontier Detection–Utility Value (I-WFD-UV) task allocation framework for collaborative environmental exploration. The method integrates: (1) a collision-detection system using a bounding volume hierarchy for multi-category construction obstacle recognition; (2) a centroid-point extraction technique with frontier filtering to reduce computational complexity; and (3) a set of task allocation strategies incorporating discounted information gain, improved movement cost, angle-based attractiveness, and a forced distance maximized distribution to optimize multi-robot distribution. Integrating digital twin technology further enhances the practicality of the solution. Ablation studies validate the effectiveness and efficiency of the presented method across multiple simulation scenarios involving scaled cable-truss structures. This method provides an efficient and reliable solution for collaborative exploration by multi-robot systems in complex construction environments.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106740"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145903159","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 : 2026-02-01Epub Date: 2026-01-05DOI: 10.1016/j.autcon.2025.106758
Zhao Zhang , Fengyang He , Zhonghao Chen , Lei Yuan , Hong Guan , Zengxi Pan , Huijun Li
As civil engineering advances toward next-generation construction, the integration of robotics, automation, and sustainable manufacturing is becoming increasingly critical. Robotic Wire Arc Additive Manufacturing (WAAM) provides a promising pathway through flexible deposition control and efficient material utilisation in steel structures. This review focuses on WAAM-fabricated steels and synthesises current developments in process, material behaviour, structural applications and future research directions. Relationships between WAAM parameters and deposition strategies are examined to clarify their influence on the performance of WAAM-fabricated steels. Reported material behaviours, including tensile, fatigue, corrosion, and high temperature behaviour, are systematically assessed. Structural applications relevant to direct fabrication, hybrid construction, and repair-related interventions are evaluated to illustrate practical pathways for WAAM in civil engineering. By linking WAAM process with both material and structural performance, this review establishes knowledge and guidance for advancing WAAM toward reliable and efficient adoption in both academic research and industrial practice within civil engineering.
{"title":"Comprehensive review of robotic wire arc additive manufacturing for steel structures: Process, material behaviour, structural applications and pathways to automated construction","authors":"Zhao Zhang , Fengyang He , Zhonghao Chen , Lei Yuan , Hong Guan , Zengxi Pan , Huijun Li","doi":"10.1016/j.autcon.2025.106758","DOIUrl":"10.1016/j.autcon.2025.106758","url":null,"abstract":"<div><div>As civil engineering advances toward next-generation construction, the integration of robotics, automation, and sustainable manufacturing is becoming increasingly critical. Robotic Wire Arc Additive Manufacturing (WAAM) provides a promising pathway through flexible deposition control and efficient material utilisation in steel structures. This review focuses on WAAM-fabricated steels and synthesises current developments in process, material behaviour, structural applications and future research directions. Relationships between WAAM parameters and deposition strategies are examined to clarify their influence on the performance of WAAM-fabricated steels. Reported material behaviours, including tensile, fatigue, corrosion, and high temperature behaviour, are systematically assessed. Structural applications relevant to direct fabrication, hybrid construction, and repair-related interventions are evaluated to illustrate practical pathways for WAAM in civil engineering. By linking WAAM process with both material and structural performance, this review establishes knowledge and guidance for advancing WAAM toward reliable and efficient adoption in both academic research and industrial practice within civil engineering.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106758"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145903173","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}
Edge computing mitigates the latency of traditional “on-site acquisition–off-site processing” workflows, enabling real-time structural safety assessment. This paper develops an edge-computing-based safety assessment system for shield tunnel linings, integrating sensors, inspection technologies, wireless sensor networks, and edge gateways. Coordinated gateway–sensor communication enables on-site data fusion and standardized processing for variable-weight fuzzy assessment. A lookup-table-based membership function achieved a 79× acceleration, 74 % lower memory use, and 34 % lower energy consumption. A lightweight multithreaded architecture improved image pre-processing by 60 %, while an optimized Kalman filter reduced latency and energy by 20 % and 36 %, respectively. A simplified Seasonal-Trend decomposition using Loess (STL)-ARIMA model enhanced forecasting efficiency by 14 %. Validated on Shanghai Metro Line 2 in China, the system enabled zone-level assessments within 243 s. By integrating edge-compatible algorithms with domain-specific structural knowledge, the framework provides a scalable, energy-efficient, and adaptive solution for long-term intelligent maintenance of shield tunnels and similar infrastructure systems.
{"title":"Real-time on-site structural safety assessment of metro tunnel linings via WSN and edge computing","authors":"Dongming Zhang , Jianhui Yu , Mingliang Zhou , Hongwei Huang , Linghan Ouyang","doi":"10.1016/j.autcon.2025.106711","DOIUrl":"10.1016/j.autcon.2025.106711","url":null,"abstract":"<div><div>Edge computing mitigates the latency of traditional “on-site acquisition–off-site processing” workflows, enabling real-time structural safety assessment. This paper develops an edge-computing-based safety assessment system for shield tunnel linings, integrating sensors, inspection technologies, wireless sensor networks, and edge gateways. Coordinated gateway–sensor communication enables on-site data fusion and standardized processing for variable-weight fuzzy assessment. A lookup-table-based membership function achieved a 79× acceleration, 74 % lower memory use, and 34 % lower energy consumption. A lightweight multithreaded architecture improved image pre-processing by 60 %, while an optimized Kalman filter reduced latency and energy by 20 % and 36 %, respectively. A simplified Seasonal-Trend decomposition using Loess (STL)-ARIMA model enhanced forecasting efficiency by 14 %. Validated on Shanghai Metro Line 2 in China, the system enabled zone-level assessments within 243 s. By integrating edge-compatible algorithms with domain-specific structural knowledge, the framework provides a scalable, energy-efficient, and adaptive solution for long-term intelligent maintenance of shield tunnels and similar infrastructure systems.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106711"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145734741","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 : 2026-02-01Epub Date: 2026-01-13DOI: 10.1016/j.autcon.2025.106759
Wenshang Yan , Hongnan Li
Improving the accuracy and robustness of deep-learning-based crack-segmentation models remains a significant challenge, primarily because of the insufficient quantity and diversity of the available pixel-level annotated data. To address this issue, this paper proposes a controllable Crack Reference-based Diffusion Model (CRDM). The proposed model can accurately synthesize realistic cracks on crack-free background images by leveraging predefined masks and reference images. Notably, it effectively transfers crack features from reference images to generated images, while maintaining high semantic accuracy. Extensive experiments are performed to demonstrate the advantages of CRDM in producing high-quality, diverse, crack images with precise controllability. The dataset augmented with the CRDM-generated images improves the performance of crack-segmentation models by ∼1 % IoU, across various scenarios. Further performance gains are achieved through our refined label-filtering strategy. The proposed CRDM exhibits strong potential for crack-segmentation tasks, effectively reducing the time and cost of data annotation and acquisition.
{"title":"Controllable reference-based semantic crack-image generation using diffusion model for intelligent infrastructure inspection","authors":"Wenshang Yan , Hongnan Li","doi":"10.1016/j.autcon.2025.106759","DOIUrl":"10.1016/j.autcon.2025.106759","url":null,"abstract":"<div><div>Improving the accuracy and robustness of deep-learning-based crack-segmentation models remains a significant challenge, primarily because of the insufficient quantity and diversity of the available pixel-level annotated data. To address this issue, this paper proposes a controllable Crack Reference-based Diffusion Model (CRDM). The proposed model can accurately synthesize realistic cracks on crack-free background images by leveraging predefined masks and reference images. Notably, it effectively transfers crack features from reference images to generated images, while maintaining high semantic accuracy. Extensive experiments are performed to demonstrate the advantages of CRDM in producing high-quality, diverse, crack images with precise controllability. The dataset augmented with the CRDM-generated images improves the performance of crack-segmentation models by ∼1 % IoU, across various scenarios. Further performance gains are achieved through our refined label-filtering strategy. The proposed CRDM exhibits strong potential for crack-segmentation tasks, effectively reducing the time and cost of data annotation and acquisition.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106759"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962001","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 : 2026-02-01Epub Date: 2026-01-13DOI: 10.1016/j.autcon.2026.106767
Pedro Meira-Rodríguez , Vicente López-Chao
Generative artificial intelligence (AI) is increasingly incorporated into architecture, engineering, and construction (AEC) workflows, yet its adoption has advanced faster than the development of robust communication frameworks that ensure reproducibility, controllability, and methodological transparency. Academic research often emphasizes exploratory prototypes or technical advances, whereas professional practice depends on empirically tested input combinations that seldom follow systematic documentation. This review examines 190 academic publications (2000–2025) and 812 practitioner cases to identify the core human–AI communication variables structuring image-based generative workflows across platforms such as Midjourney, DALL-E, and Stable Diffusion. By synthesizing these variables into a cross-platform taxonomy, the paper reframes them as design levers and reproducible parameters for AEC design at an early stage. In doing so, the paper advances the goals of automation, standardization, and traceability in AEC workflows by demonstrating that reproducibility in generative design depends not only on model performance but on the communicability and documentation of user–model interactions.
{"title":"Human–AI communication parameters for reproducible text-to-image workflows in AEC design across academia and practice","authors":"Pedro Meira-Rodríguez , Vicente López-Chao","doi":"10.1016/j.autcon.2026.106767","DOIUrl":"10.1016/j.autcon.2026.106767","url":null,"abstract":"<div><div>Generative artificial intelligence (AI) is increasingly incorporated into architecture, engineering, and construction (AEC) workflows, yet its adoption has advanced faster than the development of robust communication frameworks that ensure reproducibility, controllability, and methodological transparency. Academic research often emphasizes exploratory prototypes or technical advances, whereas professional practice depends on empirically tested input combinations that seldom follow systematic documentation. This review examines 190 academic publications (2000–2025) and 812 practitioner cases to identify the core human–AI communication variables structuring image-based generative workflows across platforms such as Midjourney, DALL-E, and Stable Diffusion. By synthesizing these variables into a cross-platform taxonomy, the paper reframes them as design levers and reproducible parameters for AEC design at an early stage. In doing so, the paper advances the goals of automation, standardization, and traceability in AEC workflows by demonstrating that reproducibility in generative design depends not only on model performance but on the communicability and documentation of user–model interactions.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106767"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962616","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 : 2026-02-01Epub Date: 2025-12-24DOI: 10.1016/j.autcon.2025.106735
Mingpeng Liu , Dechun Lu , Franz Tschuchnigg , Fengwen Lai , Xin Zhou , Feng Chen
To enable intelligent prediction and control of excavation-induced deformations, including wall deflection, ground surface settlement, and nearby tunnel displacements, this paper proposes an integrated approach combining in-situ test-based numerical modelling, Bayesian-optimised deep neural networks (BO-DNNs), and a DNN-based Newton-Raphson (DNN-NR) algorithm. The proposed framework serves as a decision-support tool for pre-construction planning of a deep excavation adjacent to existing tunnels. Specifically, the verified numerical models generate the training dataset for the BO-DNN model, which achieves high predictive accuracy for maximum deformations under varying servo-force combinations and excavation geometries. The BO-DNN analysis reveals that servo forces significantly influence deformation patterns and can even alter the direction of wall deflection and ground settlement. Leveraging this surrogate model, the DNN-NR algorithm efficiently identifies optimal servo forces to minimise deformations. The applications demonstrate that the DNN-NR-derived forces effectively restrict deformations within allowable limits. Furthermore, the algorithm quantifies the relative importance of each servo strut in deformation control and provides allowable axial force thresholds, facilitating adaptive force adjustments during the excavation.
{"title":"Intelligent prediction and control of deformation induced by a servo-strutted deep excavation adjacent to existing tunnels","authors":"Mingpeng Liu , Dechun Lu , Franz Tschuchnigg , Fengwen Lai , Xin Zhou , Feng Chen","doi":"10.1016/j.autcon.2025.106735","DOIUrl":"10.1016/j.autcon.2025.106735","url":null,"abstract":"<div><div>To enable intelligent prediction and control of excavation-induced deformations, including wall deflection, ground surface settlement, and nearby tunnel displacements, this paper proposes an integrated approach combining in-situ test-based numerical modelling, Bayesian-optimised deep neural networks (BO-DNNs), and a DNN-based Newton-Raphson (DNN-NR) algorithm. The proposed framework serves as a decision-support tool for pre-construction planning of a deep excavation adjacent to existing tunnels. Specifically, the verified numerical models generate the training dataset for the BO-DNN model, which achieves high predictive accuracy for maximum deformations under varying servo-force combinations and excavation geometries. The BO-DNN analysis reveals that servo forces significantly influence deformation patterns and can even alter the direction of wall deflection and ground settlement. Leveraging this surrogate model, the DNN-NR algorithm efficiently identifies optimal servo forces to minimise deformations. The applications demonstrate that the DNN-NR-derived forces effectively restrict deformations within allowable limits. Furthermore, the algorithm quantifies the relative importance of each servo strut in deformation control and provides allowable axial force thresholds, facilitating adaptive force adjustments during the excavation.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106735"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823223","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 : 2026-02-01Epub Date: 2025-12-22DOI: 10.1016/j.autcon.2025.106733
Yafei Sun , Xuesong Shen , Sisi Zlatanova , Khalegh Barati , James Linke
Automatic construction of knowledge graphs (ACKG) from text enables intelligent operations management of road infrastructure (OMRI). The specialized nature of OMRI text hinders direct adoption of general ACKG methods and necessitates domain-specific approaches. The rapid evolution of OMRI-specific ACKG renders a review necessary. This paper aims to summarize the latest progress and to guide future ACKG research for OMRI applications. 41 articles from seven databases (2020–August 2025) are analyzed systematically. The review provides an in-depth analysis of design motivations and underlying mechanisms of the methods involved, maps the approaches to challenges from textual characteristics, and proposes a domain-tailored process architecture. Key findings include: (1) adoption of advanced technologies, particularly machine learning, addresses domain challenges and facilitates automation; (2) the “extraction-generation-refinement” workflow forms a reusable roadmap; (3) four key aspects reflect ACKG methods' effectiveness; and (4) remaining challenges include technology coverage, and promising directions include transfer learning.
{"title":"Text-based automatic knowledge graph construction for road infrastructure operations management","authors":"Yafei Sun , Xuesong Shen , Sisi Zlatanova , Khalegh Barati , James Linke","doi":"10.1016/j.autcon.2025.106733","DOIUrl":"10.1016/j.autcon.2025.106733","url":null,"abstract":"<div><div>Automatic construction of knowledge graphs (ACKG) from text enables intelligent operations management of road infrastructure (OMRI). The specialized nature of OMRI text hinders direct adoption of general ACKG methods and necessitates domain-specific approaches. The rapid evolution of OMRI-specific ACKG renders a review necessary. This paper aims to summarize the latest progress and to guide future ACKG research for OMRI applications. 41 articles from seven databases (2020–August 2025) are analyzed systematically. The review provides an in-depth analysis of design motivations and underlying mechanisms of the methods involved, maps the approaches to challenges from textual characteristics, and proposes a domain-tailored process architecture. Key findings include: (1) adoption of advanced technologies, particularly machine learning, addresses domain challenges and facilitates automation; (2) the “extraction-generation-refinement” workflow forms a reusable roadmap; (3) four key aspects reflect ACKG methods' effectiveness; and (4) remaining challenges include technology coverage, and promising directions include transfer learning.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106733"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145813788","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 : 2026-02-01Epub Date: 2026-01-10DOI: 10.1016/j.autcon.2025.106756
Danrui Li , Yichao Shi , Mathew Schwartz , Mubbasir Kapadia
Early-stage architectural design relies heavily on precedent cases and domain knowledge, yet existing assistance methods struggle with the dominance of visual data and the linguistic diversity of design descriptions. In this paper, a retrieval-augmented generation framework with a custom knowledge graph tailored to architecture is proposed. The approach features: (1) a lightweight graph structure representing design logic; (2) a knowledge extraction pipeline for visual and textual data; and (3) aggregation and question answering methods that consolidate precedent knowledge for design support. Experiments show improved retrieval accuracy, more comprehensive precedent recommendations, and enhanced user experience, advancing precedent-based assistance for early design.
{"title":"Early-stage architecture design assistance by LLMs and knowledge graphs","authors":"Danrui Li , Yichao Shi , Mathew Schwartz , Mubbasir Kapadia","doi":"10.1016/j.autcon.2025.106756","DOIUrl":"10.1016/j.autcon.2025.106756","url":null,"abstract":"<div><div>Early-stage architectural design relies heavily on precedent cases and domain knowledge, yet existing assistance methods struggle with the dominance of visual data and the linguistic diversity of design descriptions. In this paper, a retrieval-augmented generation framework with a custom knowledge graph tailored to architecture is proposed. The approach features: (1) a lightweight graph structure representing design logic; (2) a knowledge extraction pipeline for visual and textual data; and (3) aggregation and question answering methods that consolidate precedent knowledge for design support. Experiments show improved retrieval accuracy, more comprehensive precedent recommendations, and enhanced user experience, advancing precedent-based assistance for early design.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106756"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145920964","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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