Automation in Construction最新文献_第10页

Digital twin–driven temperature field optimization in tunnel freezing restoration using particle swarm optimization 基于粒子群算法的隧道冻结修复温度场数字双驱动优化

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-02-01 Epub Date: 2026-01-02 DOI: 10.1016/j.autcon.2025.106747

Jie Zhou , Chao Ban , Chengjun Liu , Zeyao Li , Huade Zhou , Hsinming Shang

{"title":"Digital twin–driven temperature field optimization in tunnel freezing restoration using particle swarm optimization","authors":"Jie Zhou , Chao Ban , Chengjun Liu , Zeyao Li , Huade Zhou , Hsinming Shang","doi":"10.1016/j.autcon.2025.106747","DOIUrl":"10.1016/j.autcon.2025.106747","url":null,"abstract":"<div><div>The distribution and evolution of temperature field are key concerns in freezing restoration projects, while traditional methods face limitations due to sparse sensor placement and simplified simulation inputs. More effective and accurate methods are needed to determine the temperature field. A PSO-based digital twin model was developed and validated with a tunnel freezing restoration project in Bangkok, Thailand. By integrating real-time field temperature data, the model enables dynamic optimization of parameters, enhancing the accuracy. Single-parameter optimization achieves fast convergence, ideal for early-stage calibration, while multi-parameter optimization improves performance under complex conditions. In these cases, PSO demonstrates better performance compared with GA and DE. When using multiple measurement points, the model may encounter local optima. The hybrid optimization strategy (GA-PSO) provides an effective pathway to mitigate the issue of local optima. This paper demonstrates the model feasibility and effectiveness, offering a practical approach for dynamic temperature management in complex freezing environments.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106747"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880898","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

No-reference image quality assessment via degraded-content inference for sewer inspection images 基于退化内容推理的无参考图像质量评价

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-02-01 Epub Date: 2025-12-17 DOI: 10.1016/j.autcon.2025.106727

Xingyu Chen, Zegen Wang, Jianghai He, Yaowen Ran, Mi Chen, Jiayi Hu, Chaoyue Li

{"title":"No-reference image quality assessment via degraded-content inference for sewer inspection images","authors":"Xingyu Chen, Zegen Wang, Jianghai He, Yaowen Ran, Mi Chen, Jiayi Hu, Chaoyue Li","doi":"10.1016/j.autcon.2025.106727","DOIUrl":"10.1016/j.autcon.2025.106727","url":null,"abstract":"<div><div>Sewer inspection images captured via CCTV often experience significant quality degradation due to complex pipeline environments and the motion of inspection robots. To address the challenges in quality assessment and support subsequent enhancement or detection tasks, this paper proposes DI-IQA, a no-reference image quality assessment model tailored for sewer inspection scenarios. DI-IQA introduces a degraded content inference (DCI) module based on GANs, guided by dark channel prior and luminance consistency losses, and an image quality regression (IQR) module that integrates features from the generator, discriminator, degraded images, and discrepancy images. Besides, two datasets were constructed for training: the Degraded Sewer Inspection Image Dataset (5350 image pairs) for DCI module, and the Sewer Inspection IQA Dataset (1000 images) for IQR module. Experiments show DI-IQA achieves PLCC 0.934 and SROCC 0.931 on the Sewer Inspection IQA Dataset, demonstrating outstanding performance, and up to PLCC 0.976 and SROCC 0.973 on natural image benchmarks.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106727"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784695","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

Scalable road infrastructure monitoring using embedded fiber Bragg grating sensors based on wavelet scattering-long short-term memory autoencoder 基于小波散射-长短期记忆自编码器的嵌入式光纤光栅传感器可扩展道路基础设施监测

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-02-01 Epub Date: 2025-12-18 DOI: 10.1016/j.autcon.2025.106724

Ali Golmohammadi , Vahid Yaghoubi , Navid Hasheminejad , Nasser Ghaderi , Wim Van den bergh , David Hernando

{"title":"Scalable road infrastructure monitoring using embedded fiber Bragg grating sensors based on wavelet scattering-long short-term memory autoencoder","authors":"Ali Golmohammadi , Vahid Yaghoubi , Navid Hasheminejad , Nasser Ghaderi , Wim Van den bergh , David Hernando","doi":"10.1016/j.autcon.2025.106724","DOIUrl":"10.1016/j.autcon.2025.106724","url":null,"abstract":"<div><div>Managing and extracting insights from the large volumes of data generated by optical fiber sensor networks is a major challenge. This paper presents an intelligent, scalable framework for real-time road health monitoring using fiber Bragg grating (FBG) sensor data. The proposed framework reduces reliance on manual data handling and cuts storage needs by over 99 % by constructing a compact health indicator (HI). Data preprocessing and fusion reduce volume and variability, while a wavelet scattering network (WSN) extracts damage-sensitive features that are encoded via a long short-term memory (LSTM) autoencoder to represent the health state. Temperature data is integrated to distinguish structural damage from environmental effects. The approach is evaluated through laboratory fatigue tests and synthetic damage data generated from healthy-state field measurements. Results demonstrate accurate, efficient monitoring with potential for edge deployment, enabling low-cost, real-time, long-term structural health management and representing a significant step toward automated, resource-efficient infrastructure maintenance.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106724"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785105","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

UAV-based quantitative crack measurement for bridges integrating four-point laser metric calibration and mamba segmentation 基于无人机的桥梁裂缝定量测量，集成四点激光度量校准和曼巴分割

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-02-01 Epub Date: 2026-01-12 DOI: 10.1016/j.autcon.2026.106774

Jinghuan Zhang , Wang Chen , Jian Zhang

{"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":"2026-02-01","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}

引用次数: 0

AI-powered real-time system for automated concrete slump prediction via video analysis 人工智能驱动的实时系统，通过视频分析自动预测混凝土坍落度

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-02-01 Epub Date: 2026-01-13 DOI: 10.1016/j.autcon.2026.106777

Youngmin Kim , Giyeong Oh , Kwangsoo Youm , Youngjae Yu

{"title":"AI-powered real-time system for automated concrete slump prediction via video analysis","authors":"Youngmin Kim , Giyeong Oh , Kwangsoo Youm , Youngjae Yu","doi":"10.1016/j.autcon.2026.106777","DOIUrl":"10.1016/j.autcon.2026.106777","url":null,"abstract":"<div><div>Concrete workability is essential to construction quality, and the slump test remains the most widely used on-site method for its assessment. However, traditional slump testing is manual, time-consuming, and highly operator-dependent, limiting its suitability for continuous or real-time monitoring during placement. SlumpGuard is an AI-powered vision system that analyzes the natural discharge flow from a mixer-truck chute using a single fixed camera. The system performs automatic chute detection, pouring-event identification, and video-based slump classification, enabling quality monitoring without sensors, hardware installation, or manual intervention. The system design is presented, along with a site-replicated dataset comprising over 6000 video clips, and extensive evaluations demonstrating reliable chute localization, accurate pouring detection, and robust slump prediction under diverse field conditions. An expert study further reveals substantial disagreement in human visual estimates, underscoring the need for automated assessment. Demonstration videos are available at <span><span>this URL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106777"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962644","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

Generalizable deep sequence models for 4D trajectory prediction of tower crane loads 塔机载荷四维轨迹预测的广义深度序列模型

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-02-01 Epub Date: 2025-12-10 DOI: 10.1016/j.autcon.2025.106696

Mohammad Hossein Kazemi , Yuqing Hu , Yi Wu , John I. Messner , Scarlett R. Miller

{"title":"Generalizable deep sequence models for 4D trajectory prediction of tower crane loads","authors":"Mohammad Hossein Kazemi , Yuqing Hu , Yi Wu , John I. Messner , Scarlett R. Miller","doi":"10.1016/j.autcon.2025.106696","DOIUrl":"10.1016/j.autcon.2025.106696","url":null,"abstract":"<div><div>Predicting the trajectory of suspended loads is essential for proactive collision avoidance and improving situational awareness during tower crane operations. Existing approaches suffer from limited generalizability, simplified motion assumptions, and lack of real-time deployment. This paper presents a data-driven framework for 4D (3D + time) trajectory prediction using deep sequence models trained on realistic crane-operation data. A Unity-based simulation environment is developed to emulate rotary and linear encoders, and 29 participants operate the crane across randomized pick-and-place tasks, producing diverse motion trajectories. Six architectures — including LSTM-based Seq2Seq models with different attention mechanisms, ConvLSTM networks, and Temporal Convolutional Networks — under varying prediction horizons, temporal context, sampling rates, and sensor noise are evaluated. The Seq2Seq model with Temporal Attention achieves the best performance, with a mean 3D displacement error of 0.45 m on unseen logistic scenarios. A high-performing model is integrated into a real-time digital twin to provide feedback for operator training.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106696"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145730976","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 conceptual optimization of building façade layouts using a fuzzy-logic-based morphological index 基于模糊逻辑形态指标的人工智能驱动的建筑立面布局概念优化

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-02-01 Epub Date: 2026-01-07 DOI: 10.1016/j.autcon.2025.106750

Carlotta Pia Contiguglia , Giuseppe Quaranta , Cristoforo Demartino , Billie F. Spencer Jr

引用次数: 0

Depth-aware detection of hanging objects for state reasoning in construction sites 建筑工地悬吊物状态推理的深度感知检测

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-02-01 Epub Date: 2026-01-06 DOI: 10.1016/j.autcon.2025.106757

Gilsu Jeong , Joonseok Lee , Moonseo Park , Changbum R. Ahn

{"title":"Depth-aware detection of hanging objects for state reasoning in construction sites","authors":"Gilsu Jeong , Joonseok Lee , Moonseo Park , Changbum R. Ahn","doi":"10.1016/j.autcon.2025.106757","DOIUrl":"10.1016/j.autcon.2025.106757","url":null,"abstract":"<div><div>Hanging objects, referring to materials or components lifted and transported by tower cranes, require continuous monitoring, as undetected suspended loads can cause severe accidents and disrupt construction workflows. However, conventional vision-based detection models struggle to recognize the hanging state due to visual ambiguity and reliance on appearance without spatial reasoning. To address this challenge, this paper proposes a framework that leverages monocular depth information to infer the hanging state more effectively. The approach incorporates a depth-aware feature module, which captures depth differences and spatial context, and a segmentation-guided depth preprocessing that refines object boundaries. Integrated into baseline detectors, the proposed method significantly improves detection accuracy and reduces false positives in complex scenes. Experimental results demonstrate the value of depth-aware modeling and establish a foundation for reliable, state-aware detection of hanging objects, enabling automated monitoring and supporting more efficient management of lifting operations and site workflows in construction environments.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106757"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921014","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

Unified data synthesis for automated 3D Visual Inspection and digital twinning of bridges 统一数据合成，实现桥梁自动三维视觉检测和数字孪生

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-02-01 Epub Date: 2025-12-29 DOI: 10.1016/j.autcon.2025.106741

Wang Wang , Mingjing Xu , Zhen Cao , Jingzi Guo , Chong Liu , Haowei Zhang , Xiaoling Zhang

{"title":"Unified data synthesis for automated 3D Visual Inspection and digital twinning of bridges","authors":"Wang Wang , Mingjing Xu , Zhen Cao , Jingzi Guo , Chong Liu , Haowei Zhang , Xiaoling Zhang","doi":"10.1016/j.autcon.2025.106741","DOIUrl":"10.1016/j.autcon.2025.106741","url":null,"abstract":"<div><div>The automation of 3D bridge inspection is critically limited by scarce annotated data and a fundamental lack of understanding regarding which intrinsic point cloud features drive Sim-to-Real (S2R) success. The paper proposes a unified procedural synthesis framework to overcome this data bottleneck. The core contributions are twofold: (1) Dual-output generation, which yields segmented ground truth and the first bridge component-level point cloud completion dataset via physical simulation. (2) Systematic feature ablation, establishing a definitive S2R importance hierarchy: Surface Normals <span><math><mo>≫</mo></math></span> Geometry <span><math><mo>></mo></math></span> RGB. This finding offers critical guidance for efficient sensor deployment and data synthesis. A model trained exclusively on synthetic data achieved a satisfactory 84.2% mIoU on a real-world benchmark, validating direct S2R transfer and proving synthetic data can substitute manual annotation. The validated methodology provides the foundation to seamlessly integrate procedural damage models, extending automation from component identification to defect detection for analysis-ready digital twins.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106741"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880899","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

Retrieval optimization for construction documents in low-resource languages using contrastive sentence generation and matryoshka representation learning 基于对比句生成和套娃表示学习的低资源语言建筑文档检索优化

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-02-01 Epub Date: 2026-01-06 DOI: 10.1016/j.autcon.2025.106749

Kichang Choi , Minwoo Jeong , Younga Shin , Jong won Ma , Kinam Kim , Hongjo Kim

{"title":"Retrieval optimization for construction documents in low-resource languages using contrastive sentence generation and matryoshka representation learning","authors":"Kichang Choi , Minwoo Jeong , Younga Shin , Jong won Ma , Kinam Kim , Hongjo Kim","doi":"10.1016/j.autcon.2025.106749","DOIUrl":"10.1016/j.autcon.2025.106749","url":null,"abstract":"<div><div>The Retrieval-Augmented Generation (RAG) framework struggles in low resource languages like Korean, particularly in specialized domains such as construction. This paper proposes RAGO-CONSTRUCT, a retrieval optimization methodology for existing RAG systems that integrate Contrastive Sentence Generation (CSG) and Sentence Block Embedding (SBE) with Matryoshka Representation Learning (MRL) to improve retrieval accuracy in Korean construction documents. CSG enables automated dataset generation using local LLMs, while SBE optimizes document chunking strategies to align with embedding model strengths. An 8986-pair training dataset was generated using local LLMs without requiring manual annotation, enabling fine-tuning with Multiple Negative Ranking Loss and Matryoshka Representation Learning. RAGO-CONSTRUCT demonstrated model-agnostic effectiveness across different embedding architectures, with multilingual-e5-large achieving 53.7 % overall accuracy, outperforming OpenAI's text-embedding-3-large by 12.35 % point. The methodology showed consistent performance improvements regardless of the base embedding model used. This approach addresses critical challenges in domain-specific RAG applications for low-resource languages.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106749"},"PeriodicalIF":11.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145920967","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