Journal of Computing in Civil Engineering

Placing embedded parts (EPs), e.g., junction boxes or plastic cable ducts, in a precast concrete (PC) component is a fundamental and repetitive trade in its manufacturing. Yet, such trade is far from being automated to enhance PC component manufacturing productivity. This study presents an intelligent robotic system for automated part placement for PC component manufacturing by using target detection and path planning. The proposed system consists of an Aubo-i5 robotic arm, a Robotiq 2F-85 clamping claw, and an Intel Realsense D435i depth camera. An improved YOLOv5 target detection algorithm is proposed to automatically detect EPs with high precision, and a two-way two-threaded informed RRT* path planning algorithm is developed to optimize the robot movement. Using junction box placement as an experiment, performance of the system was evaluated by examining EP detection, clamping, path planning, and placement. The visual detection model achieved a mAP value of 99.5%. The efficiency of the path planning algorithm was improved by 37.7% compared with Bidirectional RRT* with close pathfinding quality. The final success rate of EP placement reached 99.8%. The research contributes to the field of PC component production by providing an automated system for EPs placement.

Abstract

This study introduces a transformative artificial intelligence of things (AIoT) framework that advances bridge maintenance by incorporating advanced inspection techniques. A central innovation is the Pilgrimage Walk Optimization (PWO)-Lite algorithm, which fine-tunes the hyperparameters of the You Only Look Once (YOLO)v7-tiny deep learning model. This model, integrated with the Deep Simple Online and Realtime Tracking (DeepSORT) algorithm, enables real-time detection and significantly enhances the system’s ability to detect deteriorations in concrete beneath bridge decks swiftly and accurately. The PWO-Lite algorithm draws inspiration from the traditional Matsu pilgrimage, an important Taiwanese folk religious event. It reflects this influence in its search behavior, miming devotees’ gathering and movement patterns. This unique approach to algorithmic design incorporates cultural customs into computational strategies. An embedded system has been configured to efficiently process visual data from unmanned aerial vehicles (UAVs), providing actionable insights directly at the inspection site. This configuration reduces the reliance on heavy computational equipment and complex setups, streamlining bridge inspections and minimizing dependence on extensive infrastructure. The practical integration of this technology into UAVs allows engineers and field professionals to obtain precise, real-time data, enhancing maintenance planning and resource management. The broader implications of this research include the potential to significantly improve standard practices in infrastructure maintenance, offering a scalable solution that could revolutionize the field. This study bridges the gap between traditional AI applications and civil engineering. It introduces a culturally inspired optimization technique to structural health monitoring, benefiting both theoretical and practical aspects of infrastructure maintenance.

Practical Applications

This research introduces a cutting-edge system that combines artificial intelligence with advanced drone-mounted cameras to inspect the condition of concrete beneath bridge decks more efficiently. Utilizing the newly developed Pilgrimage Walk Optimization (PWO)-Lite algorithm, this system quickly identifies and analyzes deterioration, such as cracks or spalling, without requiring large-scale computer systems or intensive manual labor. By integrating this technology into drones, field experts and engineers can swiftly transform images of bridge deterioration into actionable insights, expediting assessments and reducing maintenance costs. This approach enhances maintenance scheduling and budget allocation and significantly shortens the time required for bridge inspections. Our system enables real-time data collection and analysis, which is particularly beneficial in remote or difficult-to-access areas. These innovations provide practical, scalable solutions for infrastructure management, significantly improving the safety and longevity of bridge structures through timely maintenance and precise damage assessment.