Automation in Construction最新文献_第9页

Deep learning-based Scalable Image-to-3D Facade Parser for generating thermal 3D building models 基于深度学习的可扩展图像到3D立面解析器，用于生成热3D建筑模型

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-15 DOI: 10.1016/j.autcon.2025.106449

Yinan Yu , Alex Gonzalez-Caceres , Samuel Scheidegger , Sanjay Somanath , Alexander Hollberg

{"title":"Deep learning-based Scalable Image-to-3D Facade Parser for generating thermal 3D building models","authors":"Yinan Yu , Alex Gonzalez-Caceres , Samuel Scheidegger , Sanjay Somanath , Alexander Hollberg","doi":"10.1016/j.autcon.2025.106449","DOIUrl":"10.1016/j.autcon.2025.106449","url":null,"abstract":"<div><div>Renovating existing buildings is essential for climate impact. Early-phase renovation planning requires simulations based on thermal 3D models at Level of Detail (LoD) 3, which include features like windows. However, scalable and accurate identification of such features remains a challenge. This paper presents the Scalable Image-to-3D Facade Parser (SI3FP), a pipeline that generates LoD3 thermal models by extracting geometries from images using both computer vision and deep learning. Unlike existing methods relying on segmentation and projection, SI3FP directly models geometric primitives in the orthographic image plane, providing a unified interface while reducing perspective distortions. SI3FP supports both sparse (e.g., Google Street View) and dense (e.g., hand-held camera) data sources. Tested on typical Swedish residential buildings, SI3FP achieved approximately 5% error in window-to-wall ratio estimates, demonstrating sufficient accuracy for early-stage renovation analysis. The pipeline facilitates large-scale energy renovation planning and has broader applications in urban development and planning.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"179 ","pages":"Article 106449"},"PeriodicalIF":11.5,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841299","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 finite element modeling method for steel bridges integrating 3D point clouds and intelligent drawing recognition technology 结合三维点云和智能绘图识别技术的钢桥有限元自动化建模方法

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-14 DOI: 10.1016/j.autcon.2025.106466

Yixuan Chen , Chenhao Gao , Qijing Chen , Jian Zhang

{"title":"Automated finite element modeling method for steel bridges integrating 3D point clouds and intelligent drawing recognition technology","authors":"Yixuan Chen , Chenhao Gao , Qijing Chen , Jian Zhang","doi":"10.1016/j.autcon.2025.106466","DOIUrl":"10.1016/j.autcon.2025.106466","url":null,"abstract":"<div><div>Laser scanning is widely recognized for capturing bridge geometry, yet automation of information extraction and finite element model (FEM) generation remains limited by manual intervention. Therefore, an automated FEM framework for bridges is proposed by integrating point cloud with intelligent recognition techniques. This paper presents three key contributions: (1) A high-precision external dimension extraction algorithm is developed based on bridge-specific features and secondary segmentation, combining projection density, adaptive thresholding, and region-growing RANSAC; (2) An internal drawing extraction framework is established using deep learning-based search, optical character recognition (OCR), and large language models for automated retrieval of structural information; (3) A FEM generation process is implemented by aligning internal and external data through component naming conventions, using a three-step algorithm involving segmentation, element creation, boundary and load assignment. Validations on arch bridge model and pedestrian bridge are conducted. This paper provides an initial exploration toward automated digital modeling in bridge engineering.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"179 ","pages":"Article 106466"},"PeriodicalIF":11.5,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829849","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

Open-source framework for automated generation of building energy performance simulation models and beyond from BIM Data 开源框架，用于自动生成建筑能源性能仿真模型和超越BIM数据

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-14 DOI: 10.1016/j.autcon.2025.106427

David Jansen , Veronika Richter , Laura Maier , Jérôme Frisch , Christoph van Treeck , Dirk Müller

{"title":"Open-source framework for automated generation of building energy performance simulation models and beyond from BIM Data","authors":"David Jansen , Veronika Richter , Laura Maier , Jérôme Frisch , Christoph van Treeck , Dirk Müller","doi":"10.1016/j.autcon.2025.106427","DOIUrl":"10.1016/j.autcon.2025.106427","url":null,"abstract":"<div><div>The manual creation of Building Energy Performance Simulation (BEPS) models is time-consuming and error-prone, hindering the implementation of energy-efficient building designs. This paper explores how to automate the generation of BEPS models from Building Information Modeling (BIM) data while addressing challenges of data imperfection and tool compatibility. <strong><span>bim2sim</span></strong> is presented as an open-source Python framework with a modular plugin architecture that processes Industry Foundation Classes (IFC) files to generate simulation models for various tools, including Modelica/TEASER and EnergyPlusWe present <strong><span>bim2sim</span></strong>, an open-source Python framework with a modular plugin architecture that processes Industry Foundation Classes (IFC) files to generate simulation models for various tools including Modelica/TEASER and EnergyPlus. The application to a non-residential building case study demonstrates the framework’s capability to produce functional simulation models at different levels of detail with significant time savings, reducing the time for generating simulation results from a BIM model to less than one hour. The framework benefits architects, building services engineers, and simulation engineers by streamlining the simulation workflow while maintaining OpenBIM compatibility. Future research should address remaining limitations in geometry processing and material mapping to further facilitate seamless information exchange between building design and simulation domains.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"179 ","pages":"Article 106427"},"PeriodicalIF":11.5,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829850","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

Multi-fidelity structural design for 3D concrete printing with selective paste intrusion 选择性浆料侵入混凝土3D打印的多保真度结构设计

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-13 DOI: 10.1016/j.autcon.2025.106352

Sebastian Dietrich , Philip Schneider , Christiane Richter , Reza Najian Asl , Kathrin Dörfler , Kai-Uwe Bletzinger , Pierluigi D'Acunto

{"title":"Multi-fidelity structural design for 3D concrete printing with selective paste intrusion","authors":"Sebastian Dietrich , Philip Schneider , Christiane Richter , Reza Najian Asl , Kathrin Dörfler , Kai-Uwe Bletzinger , Pierluigi D'Acunto","doi":"10.1016/j.autcon.2025.106352","DOIUrl":"10.1016/j.autcon.2025.106352","url":null,"abstract":"<div><div>This paper presents a method for the structural design of additively manufactured concrete structures using the Selective Paste Intrusion (SPI) technique. The approach addresses the specific constraints of 3D printing while leveraging its unique design potential. The proposed method integrates global geometry generation, segmentation into manufacturable components, detailed structural design, and advanced analysis. A multi-fidelity modeling strategy connects low-fidelity models, such as strut-and-tie networks for force path generation, with high-fidelity models that use continuous geometries and stress fields for precise design refinement. Low-fidelity models, developed through Vector-based Graphic Statics and Combinatorial Equilibrium Modeling, facilitate rapid design exploration during the early design phase, whereas high-fidelity models enable advanced design development through finite element simulations and optimization techniques. Segmentation is guided by force flow to enhance manufacturability and ensure normal stress transmission at joints. The proposed method is demonstrated through a case study of a 3D-printed segmented pedestrian bridge manufactured with the SPI technique, highlighting its effectiveness in optimizing structural performance, ensuring stability, and accommodating printing constraints from the initial design phase.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"179 ","pages":"Article 106352"},"PeriodicalIF":11.5,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829847","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

Advancements and challenges in earthmoving equipment automation using core technologies 土方设备自动化核心技术的进展与挑战

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-13 DOI: 10.1016/j.autcon.2025.106472

Jiyu Feng , Wei Chen , Changyi Liu , Xinhui Liu , Bingwei Cao , Feixiang Xu

{"title":"Advancements and challenges in earthmoving equipment automation using core technologies","authors":"Jiyu Feng , Wei Chen , Changyi Liu , Xinhui Liu , Bingwei Cao , Feixiang Xu","doi":"10.1016/j.autcon.2025.106472","DOIUrl":"10.1016/j.autcon.2025.106472","url":null,"abstract":"<div><div>Earthmoving equipment automation is limited by interactions with complex media, unstructured terrain and highly variable environmental conditions. Although research in this field has continued for nearly thirty years, a truly fully autonomous system remains unrealized. This paper examines automation technologies for earthmoving equipment in typical repetitive short-cycle loading operations and presents a systematic survey of core literature spanning over thirty years. Key techniques are grouped into four categories, environmental perception, path planning and tracking, autonomous loading and unloading and safety and risk assessment. The analysis indicates that current research primarily addresses improvements to individual stages or subsystems while system level optimization of the entire workflow is still lacking. Moreover, data physics hybrid methods show strong potential in earthmoving equipment automation applications by compensating for errors in physical models and improving the generalization of data driven models. Finally, the major challenges are summarized and directions for future research are proposed.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"179 ","pages":"Article 106472"},"PeriodicalIF":11.5,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829846","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-driven lifting-centered construction site layout planning decision approach with BIM 基于BIM的以数据驱动吊装为中心的施工现场布局规划决策方法

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-13 DOI: 10.1016/j.autcon.2025.106467

Rongyan Li , Hung-Lin CHI , Zhiqi Hu , Du Li , Wen Yi , Ioannis Brilakis

{"title":"Data-driven lifting-centered construction site layout planning decision approach with BIM","authors":"Rongyan Li , Hung-Lin CHI , Zhiqi Hu , Du Li , Wen Yi , Ioannis Brilakis","doi":"10.1016/j.autcon.2025.106467","DOIUrl":"10.1016/j.autcon.2025.106467","url":null,"abstract":"<div><div>Construction site layout planning (CSLP) is essential for optimizing the placement of temporary facilities (TFs), yet it inadequately integrates tower crane characteristics, causing inefficient material transportation and safety risk. Current decision-making relies on labor-intensive data extraction, complex mathematical models, and fragmented workflows incompatible with specialized software. This paper proposes an automated data-driven lifting-centered CSLP decision approach with building information modeling (BIM) and AI to enhance TF placement efficiency. The approach incorporates three stages: automated data extraction from the BIM model with users' promotion, development of data-driven lifting-based multi-objectives CSLP decision engines, and evaluation of generated TFs placement through BIM-based simulations. Validation indicates that over 92 % of AI-generated CSLP outcomes outperform traditional methods (genetic algorithm (GA)). Experiments on a real-world project demonstrate that this approach reduces processing time to 7.93 % of GA and lowers functional costs by 11.60 %. This method assists designers in expediting the CSLP decision-making process with BIM models.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"179 ","pages":"Article 106467"},"PeriodicalIF":11.5,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829848","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

Optimization and performance evaluation of machine learning classifiers for predicting construction quality and schedule 用于预测施工质量和进度的机器学习分类器的优化和性能评估

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-13 DOI: 10.1016/j.autcon.2025.106470

Ching-Lung Fan

{"title":"Optimization and performance evaluation of machine learning classifiers for predicting construction quality and schedule","authors":"Ching-Lung Fan","doi":"10.1016/j.autcon.2025.106470","DOIUrl":"10.1016/j.autcon.2025.106470","url":null,"abstract":"<div><div>In recent decades, numerous predictive machine learning (ML) models have been developed within the field of construction management. However, comprehensive evaluations of supervised, data-driven methods that can address the complexity inherent in construction project data remain limited. To bridge this gap, this paper utilized a large-scale, publicly available dataset from the Public Construction Intelligence Cloud (PCIC), comprising 1015 projects characterized by 499 distinct defect categories. Nine supervised ML classifiers were evaluated on two distinct prediction tasks: (i) classifying construction quality into four categorical clusters, and (ii) predicting construction schedule status as either ahead or behind schedule. Each ML model underwent hyperparameter tuning during training to determine optimal parameter combinations, resulting in highly optimized predictive models. Among them, the Multilayer Perceptron (MLP) achieved the highest accuracy, 94.1 % (F1 score: 0.902) for quality prediction and 98.4 % (F1 score: 0.984) for schedule prediction, demonstrating its effectiveness in construction data analysis.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"179 ","pages":"Article 106470"},"PeriodicalIF":11.5,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829961","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

Synchronous control of multiple hydraulic cylinders in aerial building machine using improved deep reinforcement learning 基于改进深度强化学习的航机多液压缸同步控制

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-12 DOI: 10.1016/j.autcon.2025.106448

Limao Zhang , Jiaqi Wang

{"title":"Synchronous control of multiple hydraulic cylinders in aerial building machine using improved deep reinforcement learning","authors":"Limao Zhang , Jiaqi Wang","doi":"10.1016/j.autcon.2025.106448","DOIUrl":"10.1016/j.autcon.2025.106448","url":null,"abstract":"<div><div>Aerial building machine (ABM) represents an advanced integrated construction system designed for high-rise structures, where the jacking phase constitutes a critical safety determinant. Current research on multi-cylinder synchronous control during ABM jacking operations remains scarce. To address this gap, this study proposes a Lyapunov-constrained twin delayed deep deterministic Policy Gradient (TD3) framework integrated with hindsight experience replay (HER). A physics-based multi-cylinder interaction environment is established to facilitate agent training. Deep reinforcement learning is utilized to train the controller for adaptive synchronous control of the multi-cylinder system in ABM. Validation through a case study of a Chinese ABM project demonstrates the following outcomes: (1) Synchronization error is reduced to 0.46 mm, contrasting sharply with 30 mm observed under uncontrolled conditions. (2) Structural stress decreases by 29.28 % compared to conventional control methods. (3) The Lyapunov constraint theoretically ensures control stability of the algorithm, and the HER strategy facilitates faster convergence of the model. These results underscore the robustness and generalizability of the reinforcement learning controller in uncertain operational scenarios, highlighting its potential for hydraulic system applications and contributions to control theory advancement.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"179 ","pages":"Article 106448"},"PeriodicalIF":11.5,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144814058","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

Task planning for human-robot collaboration in structure assembly 结构装配中人机协作的任务规划

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-11 DOI: 10.1016/j.autcon.2025.106464

Yizhe Wang, Yihai Fang, Yu Bai

{"title":"Task planning for human-robot collaboration in structure assembly","authors":"Yizhe Wang, Yihai Fang, Yu Bai","doi":"10.1016/j.autcon.2025.106464","DOIUrl":"10.1016/j.autcon.2025.106464","url":null,"abstract":"<div><div>Integrating robotics in construction works has the potential to address persistent challenges such as labor shortages, health risks, and low productivity. This paper proposes a systematic task planning approach for human-robot collaboration (HRC) in structure assembly, comprising a robotic potential scoring-based task classification system and a constraint programming-based task allocation and sequencing optimization model. Based on key factors such as component properties, connection methods, robotic payload capacities, material storage layouts, and workspace safety, the task classification system provides a systematic approach to classifying construction tasks. To obtain optimal task allocation and sequencing, an extended flexible job shop scheduling problem (FJSSP) based optimization model integrates human fatigue into the objective function to balance operational efficiency and worker well-being. A timber frame assembly case study with different HRC configurations and storage arrangements was conducted to evaluate the performance in enhancing makespan and reducing human fatigue. This work establishes a robust foundation for optimizing HRC in structure assembly, paving the way for more effective and human-centric practices in the industry.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"179 ","pages":"Article 106464"},"PeriodicalIF":11.5,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144814057","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

Long context window-based zero-shot legal interpretation of building codes and regulations 基于长语境窗口的建筑法规零射击法律解释

IF 11.5 1区工程技术

Automation in Construction Pub Date : 2025-08-11 DOI: 10.1016/j.autcon.2025.106450

Jaekun Lee , Ghang Lee

{"title":"Long context window-based zero-shot legal interpretation of building codes and regulations","authors":"Jaekun Lee , Ghang Lee","doi":"10.1016/j.autcon.2025.106450","DOIUrl":"10.1016/j.autcon.2025.106450","url":null,"abstract":"<div><div>South Korean authorities handle over 2000 inquiries daily about building code violations. Interpreting these complex, frequently updated codes is challenging, even for legal experts. Prior studies using large language models (LLMs) with retrieval-augmented generation (RAG) have struggled with context loss due to data segmentation. This paper proposes three automated building code interpreter (ABCI) models—Original, Inferred, and Filtered—that leverage long-context window (LCW) LLMs as the base model. On 171 challenging legal interpretative question-answering (LIQA) cases, ABCI-Filtered achieved 63.2 % accuracy, outperforming the RAG baseline approach (56.1 %), state-of-the-art LLMs like Claude 3.7 (60.2 %), as well as ABCI-Inferred (60.8 %) and ABCI-Original (56.7 %). Notably, unlike prior methods that require fine-tuning, ABCI-Filtered outperformed previous methods using only zero-shot reasoning. In an additional experiment using a relatively straightforward building code QA dataset, ABCI-Filtered and ABCI-Inferred outperformed the other methods (79.6 % and 80.0 %, respectively), confirming the difficulty of the initial task using the LIQA dataset.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"179 ","pages":"Article 106450"},"PeriodicalIF":11.5,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144809810","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