Advanced informatic technologies for intelligent construction: A review

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 137, С. 109104 - 109104

Опубликована: Авг. 29, 2024

Язык: Английский

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Deep reinforcement learning for multi-objective optimization in BIM-based green building design

Automation in Construction, Год журнала: 2024, Номер 166, С. 105598 - 105598

Опубликована: Июль 4, 2024

Язык: Английский

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A computational method for real‐time roof defect segmentation in robotic inspection

Computer-Aided Civil and Infrastructure Engineering, Год журнала: 2025, Номер unknown

Опубликована: Апрель 3, 2025

Abstract Roof inspections are crucial but perilous, necessitating safer and more cost‐effective solutions. While robots offer promising solutions to reduce fall risks, robotic vision systems face efficiency limitations due computational constraints scarce specialized data. This study presents real‐time roof defect segmentation network (RRD‐SegNet), a deep learning framework optimized for mobile platforms. The architecture features mobile‐efficient backbone lightweight processing, defect‐specific feature extraction module improved accuracy, regressive detection classification head precise localization. Trained on the multi‐type dataset of 1350 annotated images across six categories, RRD‐SegNet integrates with damage identification tracking. system surpasses state‐of‐the‐art models 85.2% precision 76.8% recall while requiring minimal resources. Field testing confirms its effectiveness F1‐scores 0.720–0.945 types at processing speeds 1.62 ms/frame. work advances automated inspection in civil engineering by enabling efficient, safe, accurate assessments via

Язык: Английский

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Intelligent Transportation Systems

IGI Global eBooks, Год журнала: 2025, Номер unknown, С. 315 - 326

Опубликована: Апрель 4, 2025

The rise of IoT devices and big data generation pose challenges to Intelligent Transportation Systems (ITS), particularly in remote areas. Unmanned aerial vehicles (UAVs) have shown potential enhancing computation communication, but there is a lack literature on well-developed model that combines UAVs with multi-hop, fog-cloud collaborative architecture. This work proposes an optimized multi-hop offloading architecture uses improve job resource allocation ITS. approach fog cloud computing UAV-enabled collaboration address the limitations processing large amounts time-sensitive tasks. problem formulated as mixed integer nonlinear programming (MINLP) constraints user association, UAV positioning, task offloading, allocation. proposed algorithm minimizes energy consumption, latency, optimizes prioritization across layers.

Язык: Английский

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Optimizing BIM drawing element placement through reinforcement learning

Automation in Construction, Год журнала: 2025, Номер 175, С. 106242 - 106242

Опубликована: Май 1, 2025

Язык: Английский

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An automated workflow based on UAV imagery and deep learning methods for monitoring excavation area work

Integrated Computer-Aided Engineering, Год журнала: 2025, Номер unknown

Опубликована: Май 21, 2025

The rapid advancement of Artificial Intelligence (AI) is transforming the construction sector, particularly in site monitoring and safety management. Real-time enables automatic detection work progress issues, anomalies, hazardous situations. However, no existing Deep Learning (DL)-based system specifically designed to utilize Unmanned Aerial Vehicles (UAVs) for excavation area monitoring. This study presents an automated workflow that integrates UAV imagery with DL architectures, featuring a 1D Convolutional Neural Network (1D-CNN) classifying phases VGG16 network detecting fences. These technologies are incorporated into Decision Support System (DSS), which automates report generation enhances decision-making by providing structured, data-driven insights. was validated real-world case involving oil gas company, demonstrating its ability streamline management tasks improve oversight. Compared traditional methods, our approach leverages technology methodologies provide higher accuracy, efficiency, scalability contribution supports digital transformation management, offering practical innovative solution real-time tracking compliance verification.

Язык: Английский

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A surface electromyography–based deep learning model for guiding semi‐autonomous drones in road infrastructure inspection

Computer-Aided Civil and Infrastructure Engineering, Год журнала: 2025, Номер unknown

Опубликована: Май 28, 2025

Abstract While semi‐autonomous drones are increasingly used for road infrastructure inspection, their insufficient ability to independently handle complex scenarios beyond initial job planning hinders full potential. To address this, the paper proposes a human–drone collaborative inspection approach leveraging flexible surface electromyography (sEMG) conveying inspectors' speech guidance intelligent drones. Specifically, this contributes new data set, s EMG C ommands P iloting D rones (sCPD), and an EMG‐based Cross ‐subject lassification Net work (sXCNet), both command keyword recognition inspector identification. sXCNet acquires desired functions performance through synergetic effort of sEMG signal processing, spatial‐temporal‐frequency deep feature extraction, multitasking‐enabled cross‐subject representation learning. The design permits deploying one unified model across all authorized inspectors, eliminating need subject‐dependent models tailored individual users. achieves notable classification accuracies 98.1% on sCPD set 86.1% public Ninapro db1 demonstrating strong potential advancing sEMG‐enabled collaboration in inspection.

Язык: Английский

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Research on Cooperative Arrival and Energy Consumption Optimization Strategies of UAV Formations

Drones, Год журнала: 2024, Номер 8(12), С. 722 - 722

Опубликована: Ноя. 30, 2024

The formation operation of unmanned aerial vehicles (UAVs) is a current research hotspot, particularly in specific mission scenarios where UAV formations are required to cooperatively arrive at designated task areas meet the needs coordinated operations. This paper investigates issues cooperative arrival and energy consumption optimization for such scenarios. First, focusing on rotorcraft UAVs, flight model derived constructed. Next, address challenges solving these models, multi-objective non-convex functions transformed into single-objective continuous functions, thereby reducing computational complexity. Furthermore, an interior-point-method-based strategy designed by estimating initial values parameters. Finally, simulation experiments validate feasibility effectiveness proposed method. experimental results show that when optimizing five algorithm converges just 16 iterations, demonstrating its suitability practical applications.

Язык: Английский

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A trustworthy intelligent offshore wind turbine fatigue crack propagation prediction framework from the probabilistic perspective

Ocean Engineering, Год журнала: 2024, Номер 314, С. 119739 - 119739

Опубликована: Ноя. 15, 2024

Язык: Английский

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