Automation in Construction最新文献_第5页

Monitoring concrete pouring progress using knowledge graph-enhanced computer vision

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-18 DOI: 10.1016/j.autcon.2025.106117

Fabian Pfitzner , Songbo Hu , Alexander Braun , André Borrmann , Yihai Fang

{"title":"Monitoring concrete pouring progress using knowledge graph-enhanced computer vision","authors":"Fabian Pfitzner , Songbo Hu , Alexander Braun , André Borrmann , Yihai Fang","doi":"10.1016/j.autcon.2025.106117","DOIUrl":"10.1016/j.autcon.2025.106117","url":null,"abstract":"<div><div>Accurate progress measurement in concrete pouring is essential to prevent project delays and material waste. This paper introduces a knowledge graph (KG)-enhanced computer vision (CV) method to improve the accuracy and generalizability of traditional methods used in concrete pouring monitoring, which often struggle to integrate contextual data. By combining object detection and extracting information from BIM models, the method creates a KG to represent spatial–temporal relationships among building components and pouring-related resources (<em>e.g</em>., concrete mixer, bucket, hose, workers). Rule-based interpretation and Graph Neural Networks (GNN) classify pouring states and cycles, achieving 80.3% accuracy with the rule-based system and 89.2% with GNN in conducted experiments on ten samples across two construction sites. These findings demonstrate that the KG-enhanced CV method provides generalizability, offering data-driven support for site managers to efficiently coordinate processes. This approach lays the foundation for detailed process-oriented digital twinning of construction projects, enabling deeper insights and better decision-making.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106117"},"PeriodicalIF":9.6,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance-based payment mechanism for common data environment (CDE) adoption in construction projects

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-15 DOI: 10.1016/j.autcon.2025.106089

Ron C.W. Ng, Jack C.P. Cheng, George C.W. Cheng, Ka Hang Fung, Chun Wai Fong

引用次数: 0

Reference architecture and ontology framework for digital twin construction

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-15 DOI: 10.1016/j.autcon.2025.106111

Jonas Schlenger , Kacper Pluta , Alwyn Mathew , Timson Yeung , Rafael Sacks , André Borrmann

{"title":"Reference architecture and ontology framework for digital twin construction","authors":"Jonas Schlenger , Kacper Pluta , Alwyn Mathew , Timson Yeung , Rafael Sacks , André Borrmann","doi":"10.1016/j.autcon.2025.106111","DOIUrl":"10.1016/j.autcon.2025.106111","url":null,"abstract":"<div><div>The application of digital twins in building construction faces challenges due to limited guidance on the necessary data management layers. This paper addresses this gap by investigating how the reference architecture for Digital Twin Construction (DTC) should be structured to manage planning information, raw monitoring data, and derived knowledge, as well as its data schema to compare project plans with status. By defining platform requirements and using Design Science Research Methodology, a solution was implemented and validated using a case study based on the ConSLAM dataset. A plugin-based DTC reference architecture employing multiple RDF graphs linked to specialized databases and the DTC Ontology as the internal data schema are introduced. This architecture guides construction companies in data-driven decision-making during execution. It establishes a foundation for managing digital twins and fosters the development of domain-specific services that benefit from clear data structures, supporting a holistic digital twin adaptable to project-specific needs.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106111"},"PeriodicalIF":9.6,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning applications in designing cementitious materials

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-14 DOI: 10.1016/j.autcon.2025.106125

Shichen Dang , Hu Fang , Yao Yao

引用次数: 0

Text-to-structure interpretation of user requests in BIM interaction

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-14 DOI: 10.1016/j.autcon.2025.106119

Yinyi Wei , Xiao Li , Frank Petzold

{"title":"Text-to-structure interpretation of user requests in BIM interaction","authors":"Yinyi Wei , Xiao Li , Frank Petzold","doi":"10.1016/j.autcon.2025.106119","DOIUrl":"10.1016/j.autcon.2025.106119","url":null,"abstract":"<div><div>Numerous efforts have been devoted to utilizing a natural language-based interface for BIM interaction. These interfaces require extracting user's intent (i.e., the operation type) and slots (i.e., the targeted elements and properties). However, there is a lack of a fine-grained approach for extracting intent and slot information simultaneously. This paper introduces a text-to-structure approach based on language models to interpret user requests for BIM interaction (T2S4BIM). It proposed a synthetic data generation method and a curated dataset as data support. Employing Transformer-based models, T2S4BIM converts unstructured user requests into a structured format with intent and slot information. Experiments demonstrated that T2S4BIM outperformed existing approaches, with encoder-decoder models like T5 and FLAN-T5 achieving performance comparable to larger, decoder-only models such as Llama3.1-8B and Qwen2.5-7B, while improving efficiency. The practical applicability of T2S4BIM was illustrated through a Revit plug-in that interprets user requests and executes corresponding actions (e.g., manipulating object properties).</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106119"},"PeriodicalIF":9.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621394","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

Graph-based change detection of pavement cracks

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-14 DOI: 10.1016/j.autcon.2025.106110

Yibo Zhou , Yuchun Huang , Qi Chen , Dongchen Yang

{"title":"Graph-based change detection of pavement cracks","authors":"Yibo Zhou , Yuchun Huang , Qi Chen , Dongchen Yang","doi":"10.1016/j.autcon.2025.106110","DOIUrl":"10.1016/j.autcon.2025.106110","url":null,"abstract":"<div><div>Pavement crack deterioration threatens road safety, but current maintenance strategies rely on composite indicators that lack crack location and attribute changes, failing to accurately track deterioration. Traditional feature-point-based methods struggle with temporal crack correspondence due to noise and shape variability. However, local structural features like intersections and inflection points are significant and extractable, providing a reliable basis for crack correspondence. This paper proposes an unsupervised graph-based framework for crack change detection. First, a curvature-first distance-optimized algorithm extracts stable keypoints to construct crack graphs, representing structural information. Second, a graph matching strategy combines Bezier curve similarity and Monte Carlo Tree Search to resolve structural correspondences, enabling accurate change detection. To address data scarcity, a Voronoi-based simulator models crack propagation through controlled stress fields. Experiments on synthetic and real-world datasets achieved crack change detection accuracies of 97.95% and 90.18%, respectively, demonstrating high accuracy without relying on learning-based components.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106110"},"PeriodicalIF":9.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621395","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 knowledge-driven image captioning for bridge damage description generation

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-14 DOI: 10.1016/j.autcon.2025.106116

Chengzhang Chai , Yan Gao , Guanyu Xiong, Jiucai Liu, Haijiang Li

{"title":"Domain knowledge-driven image captioning for bridge damage description generation","authors":"Chengzhang Chai , Yan Gao , Guanyu Xiong, Jiucai Liu, Haijiang Li","doi":"10.1016/j.autcon.2025.106116","DOIUrl":"10.1016/j.autcon.2025.106116","url":null,"abstract":"<div><div>Deep learning-based bridge visual inspection often produces limited outputs, lacking the accurate descriptions required for practical assessments. Researchers have explored multimodal approaches to generate damage descriptions, but existing models are prone to hallucination and face challenges related to feature representation sufficiency, attention mechanism flexibility, and domain-specific knowledge integration. This paper develops an image captioning framework driven by domain knowledge to address these issues. It incorporates a multi-level feature fusion module that adaptively integrates Faster R-CNN trained weights (domain knowledge) with a CNN architecture. Additionally, it introduces a correlation-aware attention mechanism to dynamically capture interdependencies between image regions and optimise the attentional focus during LSTM decoding. Experimental results show that the proposed framework achieves higher BLEU scores and improves image-text alignment as verified through attention heatmaps. While the framework enhances inspection efficiency and quality, further dataset expansion and broader domain validation are required to assess its generalisation ability.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106116"},"PeriodicalIF":9.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621397","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

Automated measurement of asphalt pavement rut depth using smartphone imaging

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-13 DOI: 10.1016/j.autcon.2025.106124

Zhenqiang Han , Jiaqi Tang , Liqun Hu , Wei Jiang , Aimin Sha

{"title":"Automated measurement of asphalt pavement rut depth using smartphone imaging","authors":"Zhenqiang Han , Jiaqi Tang , Liqun Hu , Wei Jiang , Aimin Sha","doi":"10.1016/j.autcon.2025.106124","DOIUrl":"10.1016/j.autcon.2025.106124","url":null,"abstract":"<div><div>Fast and accurate rutting distress detection is essential for driving safety and advancing pavement maintenance automation. However, existing Rut Depth (RD) measurement methods are often inefficient or costly due to complex pavement conditions and expensive equipment. This paper introduces a method to identify and measure RD using smartphone photography and neural networks. Accelerated Pavement Tests (APTs) were conducted to capture rutting evolution, combining measured and photographed data. Grayscale images were analyzed via Fourier transform, identifying the rut-related frequency range (0–0.02 Hz). Grayscale rut curves corresponding to actual rut cross-sections were extracted, and seven feature points were used to calculate grayscale RD. A backpropagation neural network model was trained and validated, demonstrating RD detection within 5–45 mm, with an average absolute error of 1.29 mm. This method provides an alternative for efficient RD measurement in APT and field pavement condition evaluations, offering potentials for improving pavement maintenance practices.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106124"},"PeriodicalIF":9.6,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609936","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

Dynamic robotic bricklaying force-position control considering mortar dynamics for enhanced consistency

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-13 DOI: 10.1016/j.autcon.2025.106090

Yi Zhou , Bowen Huang , Boge Dong , Yi Wen , Molong Duan

{"title":"Dynamic robotic bricklaying force-position control considering mortar dynamics for enhanced consistency","authors":"Yi Zhou , Bowen Huang , Boge Dong , Yi Wen , Molong Duan","doi":"10.1016/j.autcon.2025.106090","DOIUrl":"10.1016/j.autcon.2025.106090","url":null,"abstract":"<div><div>With growing construction automation needs and aging workforce, robotic bricklaying technologies offer promising solutions by replacing labor-intensive manual wall construction. Despite advances in mechatronics, trajectory generation, and adhesive bonding, bricklaying process consistency still remains a major issue, challenged by nonlinear mortar dynamics, variable thickness, and lack of force-position control. This paper enhances consistency by adjusting the robot trajectory to achieve the desirable maximum contact force and brick placement accuracy. The feedforward bricklaying trajectory is generated by solving an optimal control problem with unspecified terminal time to approach the desirable maximum force while maintaining placement accuracy, considering the identified mortar dynamics. To ensure precise positioning and robust desired maximum contact force, robotic bricklaying force-position feedback control is proposed to dynamically control the velocity utilizing real-time force feedback. Simulations and experiments validated the proposed approach, including shear tests, demonstrating enhanced consistency and the relationship between maximum contact force and bond strength.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106090"},"PeriodicalIF":9.6,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610036","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

Corrigendum to “Neural radiance fields for construction site scene representation and progress evaluation with BIM” [Automation in Construction, Volume 172 (2025) 106013]

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-12 DOI: 10.1016/j.autcon.2025.106121

Yuntae Jeon , Dai Quoc Tran , Khoa Tran Dang Vo , Jaehyun Jeon , Minsoo Park , Seunghee Park

引用次数: 0