Pub Date : 2026-01-12DOI: 10.1016/j.autcon.2026.106763
Wei Tu , Yu Gu , Ruizhe Chen , Xing Zhang , Jiasong Zhu , Chisheng Wang , Qingquan Li
Urban sewer pipelines are prone to diverse faults, such as cracks, erosion, and root intrusion. Effective and efficient inspection methods are essential for large-scale urban sewer pipe networks. This paper presented a collaborative inspection approach to inspect urban sewer pipes, which integrates robotic pipe capsules (RPCs) with lightweight deep learning and spatial optimization. A bi-level network is built to represent diverse movements of workers and the RPCs and their collaboration. A specialized lightweight deep neural network is designed to identify faults with images captured by PRC in real time. The worker and RPC routes are spatially optimized with hybrid meta-heuristics. An experiment in Shenzhen, China, demonstrated that it achieves a balanced accuracy of 83.43% with 7.64 frames per second, which outperforms baseline methods. The presented method provides an alternative approach for large-scale urban sewer pipe networks.
{"title":"Collaborative inspection for large-scale urban sewer pipe networks by coupling multiple robotic pipe capsules and spatial optimization","authors":"Wei Tu , Yu Gu , Ruizhe Chen , Xing Zhang , Jiasong Zhu , Chisheng Wang , Qingquan Li","doi":"10.1016/j.autcon.2026.106763","DOIUrl":"10.1016/j.autcon.2026.106763","url":null,"abstract":"<div><div>Urban sewer pipelines are prone to diverse faults, such as cracks, erosion, and root intrusion. Effective and efficient inspection methods are essential for large-scale urban sewer pipe networks. This paper presented a collaborative inspection approach to inspect urban sewer pipes, which integrates robotic pipe capsules (RPCs) with lightweight deep learning and spatial optimization. A bi-level network is built to represent diverse movements of workers and the RPCs and their collaboration. A specialized lightweight deep neural network is designed to identify faults with images captured by PRC in real time. The worker and RPC routes are spatially optimized with hybrid meta-heuristics. An experiment in Shenzhen, China, demonstrated that it achieves a balanced accuracy of 83.43% with 7.64 frames per second, which outperforms baseline methods. The presented method provides an alternative approach for large-scale urban sewer pipe networks.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106763"},"PeriodicalIF":11.5,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956563","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 : 2026-01-12DOI: 10.1016/j.autcon.2026.106774
Jinghuan Zhang , Wang Chen , Jian Zhang
Crack width is an indicator of durability loss and serviceability in concrete bridges. Although UAV-based inspection is adopted, variable standoff distance and oblique imaging hinder valid, millimeter-level quantification. This paper presents a framework for crack identification and measurement. (1) A UAV-mounted four-point laser ranging device establishes a scale for each frame. Combined with homography and a Jacobian-based local length metric, the pixel-to-physical factor becomes a function of position and direction, which reduces scale drift across viewpoints. (2) CrackMamba-Net is designed to couple state space modeling with boundary sensitive representations, enhancing crack edge continuity and boundary clarity under fine and low contrast conditions. (3) Topology-preserving skeleton refinement with PCA-guided, distance-weighted linear correction estimates the local orientation; width is then measured along the refined normal and converted to physical units. Field and on-bridge experiments show linear agreement with references and low bias, supporting traceable, engineering-consistent crack quantification at the millimeter scale.
{"title":"UAV-based quantitative crack measurement for bridges integrating four-point laser metric calibration and mamba segmentation","authors":"Jinghuan Zhang , Wang Chen , Jian Zhang","doi":"10.1016/j.autcon.2026.106774","DOIUrl":"10.1016/j.autcon.2026.106774","url":null,"abstract":"<div><div>Crack width is an indicator of durability loss and serviceability in concrete bridges. Although UAV-based inspection is adopted, variable standoff distance and oblique imaging hinder valid, millimeter-level quantification. This paper presents a framework for crack identification and measurement. (1) A UAV-mounted four-point laser ranging device establishes a scale for each frame. Combined with homography and a Jacobian-based local length metric, the pixel-to-physical factor becomes a function of position and direction, which reduces scale drift across viewpoints. (2) CrackMamba-Net is designed to couple state space modeling with boundary sensitive representations, enhancing crack edge continuity and boundary clarity under fine and low contrast conditions. (3) Topology-preserving skeleton refinement with PCA-guided, distance-weighted linear correction estimates the local orientation; width is then measured along the refined normal and converted to physical units. Field and on-bridge experiments show linear agreement with references and low bias, supporting traceable, engineering-consistent crack quantification at the millimeter scale.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106774"},"PeriodicalIF":11.5,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962621","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 : 2026-01-10DOI: 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":"2026-01-10","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}
Pub Date : 2026-01-10DOI: 10.1016/j.autcon.2026.106769
Yunpeng Yue , Hai Liu , Marco Donà , Xiaoyu Liu , Elisa Saler , Jie Cui , Francesca da Porto
Cultural heritage (CH) buildings are vulnerable to damage due to aging and environmental factors, necessitating timely detection and maintenance. This paper proposes a lightweight dual-backbone segmentation model for damage detection in CH structures. The architecture integrates a Swin Transformer branch to capture global contextual information and a YOLOv8-Ghost branch to preserve fine-grained local details, with a Content-Guided Attention (CGA) fusion mechanism employed to enhance inter-channel feature interactions. A five-class Roman amphitheater damage dataset with 2010 images was constructed for training and evaluation. The proposed model is applied to damage detection in the Arena, Verona, Italy, which experienced local collapse accident on January 23, 2023. Experimental results demonstrate that the model achieves robust segmentation performance under challenging conditions such as low lighting, occlusions, and heterogeneous surface textures. The inspection results of both the exterior and interior facades of the Arena confirm the effectiveness and efficiency of the proposed dual-backbone fusion strategy.
{"title":"Dual-backbone fusion network for damage segmentation in cultural heritage buildings","authors":"Yunpeng Yue , Hai Liu , Marco Donà , Xiaoyu Liu , Elisa Saler , Jie Cui , Francesca da Porto","doi":"10.1016/j.autcon.2026.106769","DOIUrl":"10.1016/j.autcon.2026.106769","url":null,"abstract":"<div><div>Cultural heritage (CH) buildings are vulnerable to damage due to aging and environmental factors, necessitating timely detection and maintenance. This paper proposes a lightweight dual-backbone segmentation model for damage detection in CH structures. The architecture integrates a Swin Transformer branch to capture global contextual information and a YOLOv8-Ghost branch to preserve fine-grained local details, with a Content-Guided Attention (CGA) fusion mechanism employed to enhance inter-channel feature interactions. A five-class Roman amphitheater damage dataset with 2010 images was constructed for training and evaluation. The proposed model is applied to damage detection in the Arena, Verona, Italy, which experienced local collapse accident on January 23, 2023. Experimental results demonstrate that the model achieves robust segmentation performance under challenging conditions such as low lighting, occlusions, and heterogeneous surface textures. The inspection results of both the exterior and interior facades of the Arena confirm the effectiveness and efficiency of the proposed dual-backbone fusion strategy.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106769"},"PeriodicalIF":11.5,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956564","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 : 2026-01-10DOI: 10.1016/j.autcon.2026.106771
Tianyu Ren, Houtan Jebelli
Drones are increasingly used in construction for inspection and material transport, but their deployment in close-range collaboration with workers remains limited due to safety concerns and the difficulty of motion planning in dynamic environments. This paper introduces a predictive, risk-aware control framework integrating motion forecasting, probabilistic risk modeling, and hybrid planning to enable safe, efficient drone–worker interaction. Worker motion is captured with RGB-D input and forecasted 1.5 s ahead using PoseCastNet, a transformer-based network that outputs joint-wise 3D trajectories and confidence. Predictions are fused into a Bayesian-updated probabilistic safety map that informs global grid-based pathfinding and local actor-critic control with risk-sensitive rewards. Evaluations in simulation with occlusion and human motion yield a 96.5% success rate, over 40% improvement in minimum clearance, over 20% boost in task efficiency, and 8% reduction in joint prediction error compared to reactive and partially predictive baselines, demonstrating its effectiveness in enabling proactive, collaborative UAV operations.
{"title":"Safety-aware predictive motion planning for close-range human-UAV collaboration in construction","authors":"Tianyu Ren, Houtan Jebelli","doi":"10.1016/j.autcon.2026.106771","DOIUrl":"10.1016/j.autcon.2026.106771","url":null,"abstract":"<div><div>Drones are increasingly used in construction for inspection and material transport, but their deployment in close-range collaboration with workers remains limited due to safety concerns and the difficulty of motion planning in dynamic environments. This paper introduces a predictive, risk-aware control framework integrating motion forecasting, probabilistic risk modeling, and hybrid planning to enable safe, efficient drone–worker interaction. Worker motion is captured with RGB-D input and forecasted 1.5 s ahead using PoseCastNet, a transformer-based network that outputs joint-wise 3D trajectories and confidence. Predictions are fused into a Bayesian-updated probabilistic safety map that informs global grid-based pathfinding and local actor-critic control with risk-sensitive rewards. Evaluations in simulation with occlusion and human motion yield a 96.5% success rate, over 40% improvement in minimum clearance, over 20% boost in task efficiency, and 8% reduction in joint prediction error compared to reactive and partially predictive baselines, demonstrating its effectiveness in enabling proactive, collaborative UAV operations.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106771"},"PeriodicalIF":11.5,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956586","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 : 2026-01-09DOI: 10.1016/j.autcon.2026.106761
Jaehwan Seong, Hyung-soo Kim, Hyung-Jo Jung
This paper introduces Cross Hard Negative Mining (Cross-HNM), which reuses cross-site false positives as hard negatives for domain-generalizable construction-site object detection. By training per-site sub-models to extract false positives from other sites, Cross-HNM exploits cross-site structure to suppress dataset-specific noise. Evaluations across 11 sites and 5 unseen test sites show that a single Cross-HNM model achieves 57.58 % mAP, matching performance of 6-fold ensemble method without the inference overhead. Theoretical analysis using Ben-David bounds formalizes how cross-site negatives reduce domain divergence and the upper bound on generalization error. Optimal thresholds are selected via 2-D sensitivity analysis and an LS-CC plateau. Performance gains transfer across architectures, including YOLOv11, Faster R-CNN, and DETR. Since mining and LS-CC are one-off, offline steps, the final detector preserves baseline runtime. Cross-HNM thus provides a practical, scalable solution for intelligent construction safety monitoring in diverse, unseen environments.
{"title":"Improving cross-site generalization in construction object detection via hard negative mining","authors":"Jaehwan Seong, Hyung-soo Kim, Hyung-Jo Jung","doi":"10.1016/j.autcon.2026.106761","DOIUrl":"10.1016/j.autcon.2026.106761","url":null,"abstract":"<div><div>This paper introduces Cross Hard Negative Mining (Cross-HNM), which reuses cross-site false positives as hard negatives for domain-generalizable construction-site object detection. By training per-site sub-models to extract false positives from other sites, Cross-HNM exploits cross-site structure to suppress dataset-specific noise. Evaluations across 11 sites and 5 unseen test sites show that a single Cross-HNM model achieves 57.58 % mAP, matching performance of 6-fold ensemble method without the inference overhead. Theoretical analysis using Ben-David bounds formalizes how cross-site negatives reduce domain divergence and the upper bound on generalization error. Optimal thresholds are selected via 2-D sensitivity analysis and an LS-CC plateau. Performance gains transfer across architectures, including YOLOv11, Faster R-CNN, and DETR. Since mining and LS-CC are one-off, offline steps, the final detector preserves baseline runtime. Cross-HNM thus provides a practical, scalable solution for intelligent construction safety monitoring in diverse, unseen environments.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106761"},"PeriodicalIF":11.5,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145920963","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 : 2026-01-08DOI: 10.1016/j.autcon.2025.106745
Xinping Hu , Yang Miang Goh , Juliana Tay
Construction project management programmes struggle to provide timely and personalised feedback at scale. This paper developed and evaluated an AI feedback system that combines a large language model (LLM) with retrieval-augmented generation (RAG) to deliver personalised messages. A design-based study trialled the feature in two settings, an in-person workshop and an online course, with 81 participants. Mixed methods were used through a perception questionnaire, interviews, and focus groups. Ratings were positive across constructs, with no significant differences between delivery modes. Regression analysis revealed that engagement and perceived fairness independently predicted the intention to continue using the tool. Thematic analysis identified five design considerations: clarity to reduce cognitive load, deeper diagnosis with actionable guidance, role-relevant personalisation, a motivational tone with reflective prompts, and transparency to sustain trust. This paper presents a practical LLM-RAG pipeline, provides evidence of acceptance, and offers practical guidance for practitioners on AI-generated feedback in construction management.
{"title":"Mixed-methods evaluation of automated personalised feedback in construction management training using RAG and LLMs","authors":"Xinping Hu , Yang Miang Goh , Juliana Tay","doi":"10.1016/j.autcon.2025.106745","DOIUrl":"10.1016/j.autcon.2025.106745","url":null,"abstract":"<div><div>Construction project management programmes struggle to provide timely and personalised feedback at scale. This paper developed and evaluated an AI feedback system that combines a large language model (LLM) with retrieval-augmented generation (RAG) to deliver personalised messages. A design-based study trialled the feature in two settings, an in-person workshop and an online course, with 81 participants. Mixed methods were used through a perception questionnaire, interviews, and focus groups. Ratings were positive across constructs, with no significant differences between delivery modes. Regression analysis revealed that engagement and perceived fairness independently predicted the intention to continue using the tool. Thematic analysis identified five design considerations: clarity to reduce cognitive load, deeper diagnosis with actionable guidance, role-relevant personalisation, a motivational tone with reflective prompts, and transparency to sustain trust. This paper presents a practical LLM-RAG pipeline, provides evidence of acceptance, and offers practical guidance for practitioners on AI-generated feedback in construction management.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106745"},"PeriodicalIF":11.5,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921090","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 : 2026-01-07DOI: 10.1016/j.autcon.2026.106760
Luis F. Verduzco , Jack C.P. Cheng , Mingkai Li , Lufeng Wang
Constructability-based optimization design of reinforcing bar (rebar) in Reinforced Concrete (RC) structures is crucial for more sustainable practices in the construction industry. To make the process time-efficient, the use of surrogate models is necessary, especially with Graph Neural Networks (GNNs). However, the adoption of GNNs alone can be limited for large RC buildings, due to their characterization as data-hungry models. In this context, Physics-Informed Neural Networks become relevant. Their implementation, however, remains unexploited for this task, where constructability constraints are as preponderant as the physics behind. This paper presents a Constructability-Aware Physics-Informed Graph Neural Network (PIGNN) for surrogate-assisted optimization design of rebar in concrete beams (CPyRO-GraphNet-Beams). Its testing and application for fixed-end supported beams is presented, as a comparison with Plain GNNs. It is demonstrated that CPyRO-GraphNet-Beams outperforms Plain GNNs, highlighting its greater capability to learn constructable features from datasets, enhancing, in turn, more practical and sustainable optimum rebar designs.
{"title":"Constructability-aware Physics-Informed Graph Neural Networks for surrogate-assisted optimization design of rebar in concrete beams","authors":"Luis F. Verduzco , Jack C.P. Cheng , Mingkai Li , Lufeng Wang","doi":"10.1016/j.autcon.2026.106760","DOIUrl":"10.1016/j.autcon.2026.106760","url":null,"abstract":"<div><div>Constructability-based optimization design of reinforcing bar (rebar) in Reinforced Concrete (RC) structures is crucial for more sustainable practices in the construction industry. To make the process time-efficient, the use of surrogate models is necessary, especially with Graph Neural Networks (GNNs). However, the adoption of GNNs alone can be limited for large RC buildings, due to their characterization as data-hungry models. In this context, Physics-Informed Neural Networks become relevant. Their implementation, however, remains unexploited for this task, where constructability constraints are as preponderant as the physics behind. This paper presents a Constructability-Aware Physics-Informed Graph Neural Network (PIGNN) for surrogate-assisted optimization design of rebar in concrete beams (CPyRO-GraphNet-Beams). Its testing and application for fixed-end supported beams is presented, as a comparison with Plain GNNs. It is demonstrated that CPyRO-GraphNet-Beams outperforms Plain GNNs, highlighting its greater capability to learn constructable features from datasets, enhancing, in turn, more practical and sustainable optimum rebar designs.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106760"},"PeriodicalIF":11.5,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145920965","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 : 2026-01-07DOI: 10.1016/j.autcon.2025.106746
Martin Urbieta , Matias Urbieta , Guillermo Burriel
BIM solutions require a digital model as a foundation to optimize processes such as maintenance, infrastructure renovation, or demolition. However, a vast number of analog building plans are archived by public entities managing urban development, and manually converting these plans into digital models, which is prohibitively expensive. To address this gap, the paper introduces an approach for organizations who need to convert large datasets of legacy electrical floorplans into a BIM. The approach leverages a Machine Learning model for instance segmentation to detect electrical features, and the line-segment detection model DeepLSD for extracting cable traces. To support model training, a new dataset, referred as IPVBA-ELEC, is provided. The approach assembles circuits by establishing semantic relationships between circuit components and wires, and store them in an IFC file. Case studies were evaluated using quantitative and qualitative techniques yielding promising results and encouraging further research of additional MEP domains.
{"title":"AI-driven extraction of electrical circuits from floorplans for BIM","authors":"Martin Urbieta , Matias Urbieta , Guillermo Burriel","doi":"10.1016/j.autcon.2025.106746","DOIUrl":"10.1016/j.autcon.2025.106746","url":null,"abstract":"<div><div>BIM solutions require a digital model as a foundation to optimize processes such as maintenance, infrastructure renovation, or demolition. However, a vast number of analog building plans are archived by public entities managing urban development, and manually converting these plans into digital models, which is prohibitively expensive. To address this gap, the paper introduces an approach for organizations who need to convert large datasets of legacy electrical floorplans into a BIM. The approach leverages a Machine Learning model for instance segmentation to detect electrical features, and the line-segment detection model DeepLSD for extracting cable traces. To support model training, a new dataset, referred as IPVBA-ELEC, is provided. The approach assembles circuits by establishing semantic relationships between circuit components and wires, and store them in an IFC file. Case studies were evaluated using quantitative and qualitative techniques yielding promising results and encouraging further research of additional MEP domains.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106746"},"PeriodicalIF":11.5,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921015","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 : 2026-01-07DOI: 10.1016/j.autcon.2025.106750
Carlotta Pia Contiguglia , Giuseppe Quaranta , Cristoforo Demartino , Billie F. Spencer Jr
This paper introduces a computational framework that bridges the gap between qualitatively driven architectural intent and quantitatively grounded engineering optimization in the context of building façade design. At the core of the framework is a Morphological Index () based on fuzzy inference, which synthesizes measurable attributes of the façade layout into a single, interpretable score. This index, in turn, serves as the objective of an optimization algorithm tasked with shaping the façade’s morphology according to designers’ preferences. A series of numerical investigations illustrates the framework’s adaptability to diverse morphological design goals. Ultimately, the conversion of optimized layouts into expressive representations via artificial-intelligence-powered visualizations confirms the framework’s applicability to automated conceptual design of building façades.
{"title":"AI-driven conceptual optimization of building façade layouts using a fuzzy-logic-based morphological index","authors":"Carlotta Pia Contiguglia , Giuseppe Quaranta , Cristoforo Demartino , Billie F. Spencer Jr","doi":"10.1016/j.autcon.2025.106750","DOIUrl":"10.1016/j.autcon.2025.106750","url":null,"abstract":"<div><div>This paper introduces a computational framework that bridges the gap between qualitatively driven architectural intent and quantitatively grounded engineering optimization in the context of building façade design. At the core of the framework is a Morphological Index (<span><math><mrow><mi>M</mi><mi>I</mi></mrow></math></span>) based on fuzzy inference, which synthesizes measurable attributes of the façade layout into a single, interpretable score. This index, in turn, serves as the objective of an optimization algorithm tasked with shaping the façade’s morphology according to designers’ preferences. A series of numerical investigations illustrates the framework’s adaptability to diverse morphological design goals. Ultimately, the conversion of optimized layouts into expressive representations via artificial-intelligence-powered visualizations confirms the framework’s applicability to automated conceptual design of building façades.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106750"},"PeriodicalIF":11.5,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145920966","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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