Automation in Construction最新文献

Expression of Concern: “Integrating deep learning and multi-attention for joint extraction of entities and relationships in engineering consulting texts” [Automation in Construction, Volume 168PA, 1 Dec 2024, 105739] 关注的表达：“在工程咨询文本中整合深度学习和多关注的实体和关系的联合提取”[自动化建设，168PA卷，1 Dec 2024, 105739]

IF 10.3 1区工程技术

Automation in Construction Pub Date : 2026-04-13 DOI: 10.1016/j.autcon.2026.106956

引用次数: 0

Prefabricated Buildings: Cross-Database CiteSpace Bibliometric Analysis of Research Trends and Hotspots 装配式建筑：跨数据库CiteSpace文献计量学研究趋势与热点分析

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-04-01 Epub Date: 2026-02-12 DOI: 10.1016/j.autcon.2026.106837

Chao Zou , Jiwei Zhu , Tingyu Duan , Siyu Tong

{"title":"Prefabricated Buildings: Cross-Database CiteSpace Bibliometric Analysis of Research Trends and Hotspots","authors":"Chao Zou , Jiwei Zhu , Tingyu Duan , Siyu Tong","doi":"10.1016/j.autcon.2026.106837","DOIUrl":"10.1016/j.autcon.2026.106837","url":null,"abstract":"<div><div>Prefabricated buildings (PBs) are reshaping contemporary construction practices and provide an important pathway for improving efficiency and quality in the building industry. Using data from CNKI and Wanfang (C&W) and from Web of Science and Scopus (W&S), this paper reviews 8564 relevant articles and systematically analyzes the current research status, development trajectory, and emerging trends of PBs using the CiteSpace bibliometric method. The results show that: (1) the number of PB studies in C&W has grown steadily and appears to have entered a relatively stable stage, while W&S studies continue to increase, with China, Australia, and the United States contributing many influential publications; (2) W&S and C&W display distinct yet complementary research emphases; (3) institutional characteristics are similar in both datasets, although collaboration is generally closer in W&S, and institutional clustering in China remains limited; (4) safety, environmental protection, energy conservation, and sustainability are becoming prominent future research directions.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"184 ","pages":"Article 106837"},"PeriodicalIF":11.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146161830","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

Zero-shot depth map restoration from sparse infrastructure point clouds using diffusion models and prompted segmentation 基于扩散模型和提示分割的稀疏基础设施点云零镜头深度图恢复

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-04-01 Epub Date: 2026-02-12 DOI: 10.1016/j.autcon.2026.106839

Yixiong Jing , Cheng Zhang , Haibing Wu , Guangming Wang , Olaf Wysocki , Brian Sheil

{"title":"Zero-shot depth map restoration from sparse infrastructure point clouds using diffusion models and prompted segmentation","authors":"Yixiong Jing , Cheng Zhang , Haibing Wu , Guangming Wang , Olaf Wysocki , Brian Sheil","doi":"10.1016/j.autcon.2026.106839","DOIUrl":"10.1016/j.autcon.2026.106839","url":null,"abstract":"<div><div>Point clouds are essential for infrastructure monitoring, but are often sparse and noisy, limiting fine-grained segmentation required for downstream tasks such as defect detection. Existing studies focus on semantic segmentation of large components or brick-level segmentation from RGB images, which are impractical in low-light environments such as masonry tunnels. This paper presents InfraDiffusion, a zero-shot framework that projects masonry point clouds into depth maps and restores them using an adapted Denoising Diffusion Null-space Model (DDNM). Without task-specific training, InfraDiffusion enhances the visual quality of depth maps, enabling downstream analysis on sparse data. Experiments on masonry bridge and tunnel datasets demonstrate significant improvements in brick-level segmentation: the mean Intersection over Union (mIoU) using the Segment Anything Model (SAM) increased from below 0.1 to over 0.7 for bridges and from below 0.4 to above 0.7 for tunnels. These results highlight InfraDiffusion’s potential for automated inspection of masonry assets under challenging sensing conditions.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"184 ","pages":"Article 106839"},"PeriodicalIF":11.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146160227","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

Automating dense GPR simulations for C-scan imaging of subsurface infrastructure 用于地下基础设施c扫描成像的密集GPR模拟自动化

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-04-01 Epub Date: 2026-02-13 DOI: 10.1016/j.autcon.2026.106828

Huamei Zhu , Yimin Zhou , Feng Xiao , Jelena Ninic , Wallace Lai , Qian-bing Zhang

{"title":"Automating dense GPR simulations for C-scan imaging of subsurface infrastructure","authors":"Huamei Zhu , Yimin Zhou , Feng Xiao , Jelena Ninic , Wallace Lai , Qian-bing Zhang","doi":"10.1016/j.autcon.2026.106828","DOIUrl":"10.1016/j.autcon.2026.106828","url":null,"abstract":"<div><div>Civil infrastructure requires continuous assessments to address aging, deterioration, and climate change impacts. Subsurface assets present particular challenges due to their invisibilities and the highly uncertain ground conditions. Ground Penetrating Radar (GPR) is widely employed for infrastructure inspection while its interpretation often demands significant expert knowledge. This paper presents an integrated framework for efficiently simulating dense GPR B-scans to support C-scan imaging and data-driven applications. Using pipeline leakage detection as a demonstration, the framework couples digital modelling, hydromechanical (HM) simulation, and finite-difference time-domain (FDTD) electromagnetic (EM) simulation. Automated data sharing between digital models and multi-physics solvers eliminates manual model setup. Simulated B-scans and C-scans capturing water-induced changes are validated against field experiments, with reality gap sources analysed. The framework enables scalable generation of physically informed synthetic GPR datasets for complex scenarios requiring geospatially registered inputs, supporting efficient C-scan imaging and data-driven interpretation.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"184 ","pages":"Article 106828"},"PeriodicalIF":11.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146161843","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

Hierarchical fusion network for joint segmentation and VQA in road pavement inspection 分层融合网络在路面检测中的联合分割与VQA

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-04-01 Epub Date: 2026-02-12 DOI: 10.1016/j.autcon.2026.106829

Zhekai Xia , Shuyuan Xu , Jun Wang , Tuan Ngo , Junbo Sun , Wenchi Shou

{"title":"Hierarchical fusion network for joint segmentation and VQA in road pavement inspection","authors":"Zhekai Xia , Shuyuan Xu , Jun Wang , Tuan Ngo , Junbo Sun , Wenchi Shou","doi":"10.1016/j.autcon.2026.106829","DOIUrl":"10.1016/j.autcon.2026.106829","url":null,"abstract":"<div><div>Conventional road pavement inspection methods typically rely on a single data modality, attempting to address defect segmentation and semantic assessment separately. Moreover, existing methods struggle with accurate crack delineation under complex field conditions. To address these challenges, this paper proposes a unified architecture, named HFSV-Net (Hierarchical Fusion Network for Joint Segmentation and Visual Question Answering). The proposed method adopts a multi-scale, multi-stage feature fusion strategy for cross-model feature representations. A Feature Pyramid Network-style single-head segmentation decoder equipped with a stripe attention mechanism is introduced to further enhance segmentation performance. Experiments on three benchmark datasets demonstrate that HFSV-Net outperforms the best competing methods by 2.32%, 1.10%, and 2.71% in mIoU, respectively. Ablation studies with feature visualization analysis further validate the effectiveness of the proposed modules. Overall, the work establishes a unified multimodal fusion paradigm for joint segmentation and VQA in road pavement inspection, achieving superior crack delineation performance under challenging conditions.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"184 ","pages":"Article 106829"},"PeriodicalIF":11.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146160221","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

Virtual reality-based experimental analysis of personality and cognitive traits on task performance and safety in novice tower crane operators 基于虚拟现实的塔机新手任务绩效与安全的人格与认知特征实验分析

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-03-01 Epub Date: 2026-01-14 DOI: 10.1016/j.autcon.2026.106776

Seungkeun Yeom , Juui Kim , Seungwon Seo , Seongkyun Ahn , Choongwan Koo , Taehoon Hong

{"title":"Virtual reality-based experimental analysis of personality and cognitive traits on task performance and safety in novice tower crane operators","authors":"Seungkeun Yeom , Juui Kim , Seungwon Seo , Seongkyun Ahn , Choongwan Koo , Taehoon Hong","doi":"10.1016/j.autcon.2026.106776","DOIUrl":"10.1016/j.autcon.2026.106776","url":null,"abstract":"<div><div>This paper investigates how personality traits and psychological-cognitive states influence task performance, safety, and physiological responses of novice tower crane operators through a virtual reality (VR) simulation integrated with continuous biometric monitoring. Fifty participants completed object lifting, obstacle navigation, and precision placement tasks while personality profiles and biosignals (ECG, EDA) were collected and analyzed using principal component analysis,cluster-based classification, and additional statistical methods. High extraversion and situational awareness enhanced speed and accuracy, whereas high openness, stress sensitivity, and acrophobia led to longer durations and reduced accuracy. High conscientiousness shortened task times by 19.12% but increased collisions by approximately threefold, revealing a trade-off between efficiency and safety. By integrating behavioral, cognitive, and physiological data, this work advances technology-enabled, data-driven safety management. The proposed approach enables automated operator risk profiling, intelligent task allocation, and proactive safety interventions for high-rise construction projects involving crane operations.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"183 ","pages":"Article 106776"},"PeriodicalIF":11.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957799","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

Addressing data scarcity in construction safety monitoring using low-rank adaptation (LoRA)-tuned domain-specific image generation 使用低秩自适应（LoRA）调优的特定领域图像生成解决建筑安全监测中的数据稀缺问题

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-03-01 Epub Date: 2026-01-21 DOI: 10.1016/j.autcon.2026.106786

Insoo Jeong , Junghoon Kim , Seungmo Lim , Jeongbin Hwang , Seokho Chi

{"title":"Addressing data scarcity in construction safety monitoring using low-rank adaptation (LoRA)-tuned domain-specific image generation","authors":"Insoo Jeong , Junghoon Kim , Seungmo Lim , Jeongbin Hwang , Seokho Chi","doi":"10.1016/j.autcon.2026.106786","DOIUrl":"10.1016/j.autcon.2026.106786","url":null,"abstract":"<div><div>This paper proposes a lightweight domain adaptation framework for construction safety monitoring by fine-tuning a pretrained text-to-image diffusion model using Low-Rank Adaptation (LoRA). To simulate high-risk construction environments underrepresented in training data, the model was adapted to environmental features and specific hazards, focusing on visually dominant scenarios including falls, struck-by, and caught-in incidents. To address data scarcity, Multi-LoRA fine-tuning was conducted using 20 images per hazard type (totaling 60 across three hazards) and 30 background images, enabling both contextual and hazard-specific adaptation. The generated images achieved the highest semantic consistency, yielding the top mean Contrastive Language-Image Pre-training (CLIP) scores with minimal variance, and improved visual realism by reducing the Fréchet Inception Distance (FID) by 86.72 points. Furthermore, a YOLOv8 model trained exclusively on these synthetic images achieved a mean average precision ([email protected]:0.95) of 94.1% on real-world frames, comparable to a baseline model trained on real data.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"183 ","pages":"Article 106786"},"PeriodicalIF":11.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014826","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

Uncertainty-aware risk mapping with passive WiFi and modified Zonal Safety Analysis (mZSA) in BIM for construction 基于被动WiFi和改进的区域安全分析（mZSA）的BIM中的不确定性风险映射

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-03-01 Epub Date: 2026-01-21 DOI: 10.1016/j.autcon.2026.106779

Mohamed Elrifaee , Tarek Zayed , Ahmed Mansour , Eslam Ali

{"title":"Uncertainty-aware risk mapping with passive WiFi and modified Zonal Safety Analysis (mZSA) in BIM for construction","authors":"Mohamed Elrifaee , Tarek Zayed , Ahmed Mansour , Eslam Ali","doi":"10.1016/j.autcon.2026.106779","DOIUrl":"10.1016/j.autcon.2026.106779","url":null,"abstract":"<div><div>Construction sites remain among the most hazardous work environments, where the lack of non-intrusive, worker-independent monitoring systems limits proactive safety management. Compared to existing approaches that rely heavily on wearables, RFID tags, or bespoke infrastructure, this paper presents a passive and non-intrusive framework leveraging WiFi probe request tracking for safety monitoring in semi-open areas with static hazards. Using low-cost TP-Link routers, the proposed system localizes workers without requiring active participation or additional equipment. To improve robustness beyond conventional fingerprinting models, a joint Autoencoder–Transformer architecture is employed to capture latent dependencies among access points, significantly reducing localization uncertainty. The resulting position estimates are integrated into a modified Zonal Safety Analysis (mZSA) framework adapted for semi-open construction zones. Unlike deterministic approaches that overlook error variability, the proposed method incorporates distribution-specific error modeling, enabling confidence-aware risk buffers. The framework provides a scalable, uncertainty-aware pathway for real-time risk detection in semi-open construction environments.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"183 ","pages":"Article 106779"},"PeriodicalIF":11.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014921","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

Few-shot GAN adaptation for high-fidelity and diverse crack image generation in dam damage detection 基于GAN的大坝损伤检测中高保真、多样化裂纹图像生成方法

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-03-01 Epub Date: 2026-01-20 DOI: 10.1016/j.autcon.2026.106789

Mingchao Li , Zuguang Zhang , Qiubing Ren , Yantao Yu , Jingyue Yuan , Jiamei Ma

{"title":"Few-shot GAN adaptation for high-fidelity and diverse crack image generation in dam damage detection","authors":"Mingchao Li , Zuguang Zhang , Qiubing Ren , Yantao Yu , Jingyue Yuan , Jiamei Ma","doi":"10.1016/j.autcon.2026.106789","DOIUrl":"10.1016/j.autcon.2026.106789","url":null,"abstract":"<div><div>Substantial crack imagery is hard to acquire in dam structural inspection due to high costs and risks. Crack image generation, as a crucial yet challenging visual task, still struggles with the quality-diversity trade-off under data scarcity. This paper thus presents CrackFSGAN, a few-shot Generative Adversarial Network (GAN) adaptation method for generating realistic, diverse dam crack images from limited samples. It incorporates the Cross-Scale Channel Interaction (CSCI) module to ensure robust gradient flow across network weights for efficient training, and the Self-Supervised Discriminator (SSDr), a redesigned feature-encoder with an additional decoder, to learn more discriminative, region-extensive feature maps. Extensive experiments on multiple damage datasets against state-of-the-art GANs validate CrackFSGAN's superiority in few-shot image synthesis quality and diversity, and its effectiveness in data augmentation for downstream crack detection tasks. Notably, it supports high-resolution (1024 × 1024 pixel<sup>2</sup>) crack image generation, offering a promising solution to data scarcity and advancing intelligent structural damage detection.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"183 ","pages":"Article 106789"},"PeriodicalIF":11.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014902","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

Noise-robust self-supervised learning with frequency-bias decomposition for TBM muck particle size distribution prediction 基于频率偏差分解的噪声鲁棒自监督学习TBM渣土粒径分布预测

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2026-03-01 Epub Date: 2026-01-27 DOI: 10.1016/j.autcon.2026.106802

Guoqiang Huang , Chengjin Qin , Jie Lu , Pengcheng Xia , Haodi Wang , Chengliang Liu

{"title":"Noise-robust self-supervised learning with frequency-bias decomposition for TBM muck particle size distribution prediction","authors":"Guoqiang Huang , Chengjin Qin , Jie Lu , Pengcheng Xia , Haodi Wang , Chengliang Liu","doi":"10.1016/j.autcon.2026.106802","DOIUrl":"10.1016/j.autcon.2026.106802","url":null,"abstract":"<div><div>Accurately predicting muck particle size distribution (PSD) of Tunnel Boring Machine (TBM) is constrained by the cumbersome process of manual annotation and environmental noise. This paper investigates robust prediction of muck PSD curve under noisy TBM operation conditions, while reducing reliance on manual annotations. A noise-robust self-supervised learning method with frequency-bias decomposition is proposed, which integrates contrastive pre-training based on noise augmentation, frequency-domain bias decomposition, and hybrid edge-aware loss function. The experiments show that with only 10% annotation, it achieves performance comparable to existing models trained on 90% annotation, with a maximum particle size MAPE of 6.7% and Rosin-Rammler parameter errors between 10 and 20%. These results demonstrate a low-cost, accurate, and noise-robust approach for muck monitoring, substantially reducing the need for manual annotation and improving prediction reliability. Future work will combine muck PSD with multi-modal TBM excavation data to support intelligent tunneling decision-making.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"183 ","pages":"Article 106802"},"PeriodicalIF":11.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071736","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