Automation in Construction最新文献_第8页

Concrete 3D printing and digital fabrication technologies for bridge construction 桥梁施工混凝土3D打印和数字制造技术

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-21 DOI: 10.1016/j.autcon.2025.106485

Nan Zhang, Jay Sanjayan

{"title":"Concrete 3D printing and digital fabrication technologies for bridge construction","authors":"Nan Zhang, Jay Sanjayan","doi":"10.1016/j.autcon.2025.106485","DOIUrl":"10.1016/j.autcon.2025.106485","url":null,"abstract":"<div><div>This paper reviews the current state of 3D concrete printing (3DCP) and digital fabrication technologies, with a focus on their applicability to bridge construction. Four major categories, including extrusion-based, particle-based, shotcrete-based 3DCP and digital casting, are analyzed in terms of process integration and limitations. Recent innovations in concrete handling process and advanced nozzle-based mixing technologies are emphasized, which address the long-standing conflict between pumpability and buildability in digital concrete. Representative case studies illustrate the structural diversity, reinforcement strategies, and implementation challenges of 3D-printed bridges. These examples also highlight the transformative potential and the current limitations of 3DCP. Several key challenges are identified, including anisotropic mechanical behavior, material sustainability, on-site assembly and construction process, and the absence of established design standards. With continued advancements in material science, digital workflows, and interdisciplinary collaboration, 3DCP and other digital methods hold the potential to reshape future bridge construction through enhanced efficiency, flexibility, and sustainability.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"179 ","pages":"Article 106485"},"PeriodicalIF":11.5,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144879387","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}

引用次数: 0

AI-driven control algorithm using machine learning and genetic optimization for enhancing visual comfort in adaptive façades 利用机器学习和遗传优化的人工智能驱动控制算法增强自适应街巷的视觉舒适性

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-21 DOI: 10.1016/j.autcon.2025.106474

Mohammad Tabatabaei Manesh , Mohammad Rajaian Hoonejani , Samira Ghafari Gousheh , Alireza Abdolmaleki , Arman Nikkhah Dehnavi , Atefeh Shahrashoob

{"title":"AI-driven control algorithm using machine learning and genetic optimization for enhancing visual comfort in adaptive façades","authors":"Mohammad Tabatabaei Manesh , Mohammad Rajaian Hoonejani , Samira Ghafari Gousheh , Alireza Abdolmaleki , Arman Nikkhah Dehnavi , Atefeh Shahrashoob","doi":"10.1016/j.autcon.2025.106474","DOIUrl":"10.1016/j.autcon.2025.106474","url":null,"abstract":"<div><div>Effective management of daylight and visual comfort in office spaces remains a challenge, as existing shading systems often lack adaptability to changing environmental conditions and occupant needs. This paper presents an AI-driven real-time shading control algorithm that optimizes visual comfort using machine learning-based surrogate models and evolutionary optimization. A non-conventional adaptive façade was simulated using Radiance and Ladybug Tools across nine U.S. climates. Four machine learning models were evaluated for predicting Task Illuminance (<span><math><msub><mrow><mi>E</mi></mrow><mrow><mi>t</mi></mrow></msub></math></span>) and Vertical Eye Illuminance (<span><math><msub><mrow><mi>E</mi></mrow><mrow><mi>v</mi></mrow></msub></math></span>), with Extra Trees achieving the highest accuracy (<span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> = 0.95). A Non-dominated Sorting Genetic Algorithm II (NSGA-II) balances glare reduction and daylight utilization by optimizing façade configurations in real time. In contrast to prior approaches constrained to fixed geometries and single-objective control, this paper introduces a generalizable multi-objective control framework. Results show that AI-driven optimization significantly improves adaptive façade performance, offering a scalable solution for intelligent daylight and comfort management.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"179 ","pages":"Article 106474"},"PeriodicalIF":11.5,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886967","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}

引用次数: 0

Segmentation of surface and subsurface damages in concrete structures through fusion of multi-modal images using vision transformer 基于视觉变换的多模态图像融合对混凝土结构表面和地下损伤进行分割

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-20 DOI: 10.1016/j.autcon.2025.106469

Lokeswari Malepati , Vedhus Hoskere , Nagarajan Ganapathy , S. Suriya Prakash

{"title":"Segmentation of surface and subsurface damages in concrete structures through fusion of multi-modal images using vision transformer","authors":"Lokeswari Malepati , Vedhus Hoskere , Nagarajan Ganapathy , S. Suriya Prakash","doi":"10.1016/j.autcon.2025.106469","DOIUrl":"10.1016/j.autcon.2025.106469","url":null,"abstract":"<div><div>Semantic segmentation of multimodal images combining visible and infrared spectra enables quantification of both surface and subsurface damage in concrete structures. High-quality segmentation, however, hinges on precise cross-modal registration and an effective fusion strategy. Sparse feature similarity across these modalities typically observed in real-world infrastructure images, limits the effectiveness and generalizability of existing registration algorithms. To overcome this limitation, this paper proposes a new multi-modal image registration algorithm that narrows the search space leveraging epipolar constraint and employs a modified multi-scale mutual-information metric for robust feature matching. Tests on a purpose-built dataset show the method surpasses state-of-the-art registration algorithms. The paper also evaluates how fusion schemes and loss functions affect segmentation performance, revealing that a combined loss function (i.e., OHEM cross entropy and Generalized Dice Loss) paired with an early-fusion strategy yields the highest mean Intersection-over-Union. These contributions advance a comprehensive framework for automated damage segmentation in multimodal imagery.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"179 ","pages":"Article 106469"},"PeriodicalIF":11.5,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144879386","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}

引用次数: 0

Sewer pipeline condition assessment and defect detection using computer vision 基于计算机视觉的污水管道状态评估与缺陷检测

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-20 DOI: 10.1016/j.autcon.2025.106479

C. Long Nguyen , Andy Nguyen , Jason Brown , L. Minh Dang

{"title":"Sewer pipeline condition assessment and defect detection using computer vision","authors":"C. Long Nguyen , Andy Nguyen , Jason Brown , L. Minh Dang","doi":"10.1016/j.autcon.2025.106479","DOIUrl":"10.1016/j.autcon.2025.106479","url":null,"abstract":"<div><div>The structural integrity and operability of sewer pipeline systems are crucial for society's health, urban environment, and economic stability. Advancements in computer vision (CV) have revolutionized sewer defect inspection, offering unprecedented accuracy and efficiency in identifying and assessing pipeline failures. While prior reviews exist, they often lack systematic comparisons of models, detailed dataset analyses, or comprehensive severity assessment frameworks. This paper presents a comprehensive review of CV implementations for sewer defect detection, location, and characterization. It thoroughly evaluates main inspection techniques, diverse datasets, and key performance metrics. State-of-the-art CV models and their applications are critically reviewed, alongside defect severity assessments and their link to maintenance strategies. Key challenges and limitations are identified, leading to recommendations for enhancing inspection efficiency and accuracy. The paper consolidates findings on methodological trends, data analysis advancements, algorithm performance variations, and improved severity assessment approaches.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"179 ","pages":"Article 106479"},"PeriodicalIF":11.5,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144879388","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}

引用次数: 0

Data-centric enhancement of site-specific automated construction equipment detection in wide-angle site images 广角工地图像中特定工地自动化施工设备检测的数据中心增强

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-20 DOI: 10.1016/j.autcon.2025.106483

Sangkil Song, Juwon Hong, Jaewon Jeoung, Junkuk Ahn, Taehoon Hong

{"title":"Data-centric enhancement of site-specific automated construction equipment detection in wide-angle site images","authors":"Sangkil Song, Juwon Hong, Jaewon Jeoung, Junkuk Ahn, Taehoon Hong","doi":"10.1016/j.autcon.2025.106483","DOIUrl":"10.1016/j.autcon.2025.106483","url":null,"abstract":"<div><div>Construction equipment detection in wide-angle site images is limited by data scarcity, site-specific variability, and adaptability of existing models. This paper presents a site-specific automated framework from a data-centric perspective to enhance detection performance. Site-specific datasets are generated using zero-shot instance segmentation and depth estimation to create synthetic images that reflect actual site conditions. Object detection models are trained on these datasets, and a slicing-based inference pipeline is integrated to further improve detection performance. Four model configurations are compared: combining two equipment image types (bounding-box and segmented objects) and two synthetic methods (scale-agnostic and scale-aware). The framework improves detection performance by up to 13.72 % over the control group. Requiring minimal human intervention, it offers a reproducible and scalable approach for developing site-specific object detection models, supporting downstream applications such as productivity analysis and safety monitoring.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"179 ","pages":"Article 106483"},"PeriodicalIF":11.5,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144879389","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}

引用次数: 0

Augmented Carpentry: Computer vision-assisted framework for manual fabrication 增强木工：用于手工制造的计算机视觉辅助框架

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-20 DOI: 10.1016/j.autcon.2025.106433

Andrea Settimi , Julien Gamerro , Yves Weinand

引用次数: 0

Fourier-Mixture of Experts YOLO for concrete crack segmentation with visual interpretability 具有视觉可解释性的混凝土裂缝分割中的fourier -混合专家YOLO

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-20 DOI: 10.1016/j.autcon.2025.106452

Haochen Chang , David Bassir , Anicet Barrios , Gongfa Chen

{"title":"Fourier-Mixture of Experts YOLO for concrete crack segmentation with visual interpretability","authors":"Haochen Chang , David Bassir , Anicet Barrios , Gongfa Chen","doi":"10.1016/j.autcon.2025.106452","DOIUrl":"10.1016/j.autcon.2025.106452","url":null,"abstract":"<div><div>Accurate crack segmentation is essential for structural health monitoring, yet most deep-learning studies treat the task as binary and struggle with varied morphologies. This paper introduces FMOE-YOLO, a Fourier-enhanced Mixture-of-Experts extension of YOLO that integrates a Multi-branched Auxiliary Feature Pyramid Network (MAFPN) and an SPPF_LSKA large-kernel attention head. The Fourier expert captures high-frequency crack cues, while MAFPN with LSKA supplies rich multiscale context. Experiments on three datasets of rising difficulty — Individual, Single-Crack (four classes), and Complex (six classes) — show consistent gains over standard YOLOv8. On the Single-Crack set the model attains 86.2% [email protected], improving performance by 4.7 percentage points. t-SNE and UMAP embeddings reveal tighter, better separated clusters, and Grad-CAM maps confirm sharper crack localization, demonstrating enhanced interpretability. The proposed approach offers strong potential for real-world monitoring, effectively handling diverse crack morphologies and challenging geometric conditions.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"179 ","pages":"Article 106452"},"PeriodicalIF":11.5,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144864836","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}

引用次数: 0

Domain-continual learning for expanding the design space of deep generative modelling in nonlinear analysis of masonry 扩展砌体非线性分析中深度生成建模设计空间的领域持续学习

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-20 DOI: 10.1016/j.autcon.2025.106435

Ahmad Adaileh , Bahman Ghiassi , Riccardo Briganti

{"title":"Domain-continual learning for expanding the design space of deep generative modelling in nonlinear analysis of masonry","authors":"Ahmad Adaileh , Bahman Ghiassi , Riccardo Briganti","doi":"10.1016/j.autcon.2025.106435","DOIUrl":"10.1016/j.autcon.2025.106435","url":null,"abstract":"<div><div>Application of conditional generative adversarial network (cGAN) offers a promising approach for predicting the nonlinear behaviour of masonry. However, the large variability in masonry’s mechanical properties makes developing comprehensive models highly time- and resource-intensive. This paper presents a continual learning (CL) approach to expand the predictive capabilities of a pre-trained cGAN model, designed to predict full mechanical response fields of masonry panels, to new domains of unseen material property combinations. Elastic weight consolidation (EWC) regularisation is adopted to mitigate catastrophic forgetting in the initial training domain. The effects of fine-tuning hyperparameters, trainable blocks, and fine-tuning subset configurations, are investigated to optimise fine-tuning performance. The fine-tuned model demonstrates excellent capability in predicting the strain maps and reaction forces and capturing extreme strain values within the expanded domain, while avoiding catastrophic forgetting. This approach outperforms costly full re-training from scratch, demonstrating a viable and computationally efficient solution for extending the generalisation capabilities of data-driven models.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"179 ","pages":"Article 106435"},"PeriodicalIF":11.5,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144864826","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}

引用次数: 0

Ensuring information security resilience in Digital-enabled Construction Projects (DCP) through quantum security technologies 通过量子安全技术确保数字化建设项目（DCP）的信息安全弹性

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-20 DOI: 10.1016/j.autcon.2025.106480

Karen Banahene Blay , Amos Darko , Seongha Hwang , Ioannis Brilakis , Fraser Foster , Ran Wei

{"title":"Ensuring information security resilience in Digital-enabled Construction Projects (DCP) through quantum security technologies","authors":"Karen Banahene Blay , Amos Darko , Seongha Hwang , Ioannis Brilakis , Fraser Foster , Ran Wei","doi":"10.1016/j.autcon.2025.106480","DOIUrl":"10.1016/j.autcon.2025.106480","url":null,"abstract":"<div><div>Ensuring information security resilience in Digital-enabled Construction Projects (DCPs) is challenging. This is due to the presence of various protocols, the participation of multiple organizations, and the extensive exchange of dynamic and static information across complex networks. Traditional security measures are often reactive, static, and fragmented, and insufficient. This paper explores quantum security technologies to proactively address information confidentiality, integrity, and availability (CIA) challenges in DCPs. A mixed-method review combining quantitative scientometric analysis and qualitative literature review was conducted, with findings validated through expert interviews and a focus group. The research proposes the Adaptive Quantum-Classical Security (AQCS) framework, demonstrating how quantum cryptography, communication, networks, and optimization can enhance proactive security in DCPs. AQCS facilitates gradual adoption, improving information resilience while minimizing workflow disruption, financial losses, and operational risks. This paper applies quantum security technologies to construction, introducing the AQCS framework for integrating quantum advancements into DCPs.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"179 ","pages":"Article 106480"},"PeriodicalIF":11.5,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144864827","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}

引用次数: 0

Spatial analysis and complexity evaluation for predicting rebar installation duration 钢筋安装工期预测的空间分析与复杂性评价

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-19 DOI: 10.1016/j.autcon.2025.106462

Fabian Pfitzner , Alexander Braun , André Borrmann , Frédéric Bosché

引用次数: 0