Real-time Exterior Quality Assessment of Substation Building Structures Integrating Intelligent Inspection Robots

DOI: https://doi.org/10.62517/jes.202602220

Author(s)

Wang Song

Affiliation(s)

School of Urban Construction, Beijing University of Technology, Beijing, China

Abstract

Traditional construction quality assessment (CQA) methods relying on BIM and 3D laser scanning fail to meet the real-time control requirements of dynamic substation construction due to complex data processing and significant feedback delays. This study establishes a real-time quality assessment framework for substation structures by integrating intelligent robotics with image recognition technology. Utilising YOLO detection to identify twenty key performance indicators across four dimensions, it employs an AHP-fuzzy comprehensive evaluation model to quantify quality scores. Weights are determined through AHP and validated via consistency tests, while FCE quantifies membership degrees, enabling systematic construction quality evaluation.

Keywords

Construction Quality Assessment; Intelligent Inspection Robots; Image Recognition; AHP-FCE Mode; Substation

References

[1] Taiping Yuan, Jing-Feng Ou, Juan Wang, et al. Systematic Thinking and Evaluation of Construction Quality Management Standardization of Power Engineering in China[J]. Advances in Civil [2] Wei Guo, Xiaomin Chen, Xiaoning Lin, et al. Application of modular mathematical modeling in intelligent design of 3D digital substation[J]. Applied Mathematics and Nonlinear Sciences, 2023, 8(2): 1765-1772. [3] Shushan Li, Siyuan Li, Jinxin Hu, et al. Intelligent Modeling of Edge Components of Prefabricated Shear Wall Structures Based on BIM[J]. Buildings, 2023, 13(5): 1252. [4] Yu Liu, Chun’an Tang, Pengyu Wang, et al. Study on Disease Mechanism and Theoretical Quantification Method of Tunnel Structure[J]. Advances in Civil Engineering, 2019, 2019(1):. [5] Jingbo Hu, Andrew Ning, Min Jiang, et al. Comprehensive Testing and Evaluation of Metal Structure of Bridge Grab Ship Unloader[J]. 2025. [6] Shanaka Kristombu Baduge, Sadeep Thilakarathna, Jude Shalitha Perera, et al. Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications[J]. Automation in Construction, 2022, 141: 104440. [7] Srijeet Halder, Kereshmeh Afsari, John Serdakowski, et al. Real-Time and Remote Construction Progress Monitoring with a Quadruped Robot Using Augmented Reality[J]. Buildings, 2022, 12(11): 2027. [8] Mohamed Hussein, Abdelrahman E. E. Eltoukhy, Amos Darko, et al. Simulation-Optimization for the Planning of Off-Site Construction Projects: A Comparative Study of Recent Swarm Intelligence Metaheuristics[J]. Sustainability, 2021, 13(24): 13551. [9] Yuchao Li, Qin Zhao, Yunhe Liu, et al. Semiautomatic Generation of Code Ontology Using ifcOWL in Compliance Checking[J]. Advances in Civil Engineering, 2021, 2021(1):. [10] Gangwen Yan, Yinghui Yang, Huizhong Zhang, et al. A Review of Life Cycle Construction Process and Cutting-Edge Technology in Prefabricated MEP Installation Engineering[J]. Buildings, 2024, 14(3): 630. [11] Hojong Kim, Nirjal Lamichhane, Cheol-Sang Kim, et al. Innovations in Building Diagnostics and Condition Monitoring: A Comprehensive Review of Infrared Thermography Applications[J]. Buildings, 2023, 13(11): 2829. [12] Srijeet Halder, Kereshmeh Afsari, et al. Robots in Inspection and Monitoring of Buildings and Infrastructure: A Systematic Review[J]. Applied Sciences, 2023, 13(4): 2304. [13] Josip Tomo Licardo, Mihael Domjan, Tihomir Orehovački, et al. Intelligent Robotics—A Systematic Review of Emerging Technologies and Trends[J]. Electronics, 2024, 13(3): 542. [14] Chulhee Lee, Doa Kim, Dongku Kim, et al. Quality Assessment of High-Speed Motion Blur Images for Mobile Automated Tunnel Inspection[J]. Sensors, 2025, 25(12): 3804. [15] Josip Tomo Licardo, Mihael Domjan, Tihomir Orehovački, et al. Intelligent Robotics—A Systematic Review of Emerging Technologies and Trends[J]. Electronics, 2024, 13(3): 542. [16] J.L. Burati, J.J. Farrington, Construction Industry Institute–Costs of Quality Deviations in Design and Construction, Clemson University, Source Document, 29 (1987). [17] M.-K. Kim, Q. Wang, H. Li, Non-contact sensing based geometric quality assessment of buildings and civil structures: A review, Autom. Constr. 100 (2019) 163–179. [18] M.-K. Kim, H. Sohn, C.-C. Chang, Localization and Quantification of Concrete Spalling Defects Using Terrestrial Laser Scanning, J. Comput. Civil Eng. 29 (2015) 04014086. [19] Christian Koch, Hristina Georgieva, Varun Kasireddy, et al. A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure[J]. Advanced Engineering Informatics, 2015, 29(2): 196-210. [20] 20.Billie F. Spencer, Vedhus Hoskere, Yasutaka Narazaki, et al. Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring[J]. Engineering, 2019, 5(2): 199-222.. [21] Chuan Zhi Dong, F. Necati Çatbaş, et al. A review of computer vision–based structural health monitoring at local and global levels[J]. Structural Health Monitoring, 2020, 20(2): 692-743. [22] Yue Pan, Limao Zhang, et al. Roles of artificial intelligence in construction engineering and management: A critical review and future trends[J]. Automation in Construction, 2020, 122: 103517. [23] Youngjib Ham, Kevin Han, Jacob J. Lin, et al. Visual monitoring of civil infrastructure systems via camera-equipped Unmanned Aerial Vehicles (UAVs): a review of related works[J]. Visualization in Engineering, 2016, 4(1):. [24] Bilal Manzoor, Idris Othman, Serdar Durdyev, et al. Influence of Artificial Intelligence in Civil Engineering toward Sustainable Development—A Systematic Literature Review[J]. Applied System Innovation, 2021, 4(3): 52. [25] MARTINEZ P, AL-HUSSEIN M, AHMAD R. A scientometric analysis and critical review of computer vision applications for construction [J]. AUTOMATION IN CONSTRUCTION, 2019, 107. [26] CHOI S-M, CHA H-S, JIANG S. Hybrid Data Augmentation for Enhanced Crack Detection in Building Construction [J]. BUILDINGS, 2024, 14(7). [27] PANERU S, JEELANI I. Computer vision applications in construction: Current state, opportunities & challenges [J]. AUTOMATION IN CONSTRUCTION, 2021, 132. [28] PANERU S, JEELANI I. Computer vision applications in construction: Current state, opportunities & challenges [J]. AUTOMATION IN CONSTRUCTION, 2021, 132. [29] M. Cabaleiro, R. Lindenbergh, W.F. Gard, P. Arias, J.W.G. van de Kuilen, Algorithm for automatic detection and analysis of cracks in timber beams from LiDAR data, Constr. Build. Mater. 130 (2017) 41–53. [30] I. Anagnostopoulos, V. Patr ˘ ˘aucean, I. Brilakis, P. Vela, Detection of Walls Floors, and Ceilings in Point Cloud Data, Construct. Res. Cong. 2016 (2016) 2302–2311. [31] L. Truong-Hong, D.F. Laefer, T. Hinks, H. Carr, Combining an Angle Criterion with Voxelization and the Flying Voxel Method in Reconstructing Building Models from LiDAR Data, Comput.-Aided Civ. Infrastruct. Eng. 28 (2013) 112–129. [32] LI X, SHANG D, LI F, et al. The climbing performance analysis of a robot for power line inspection with retractable double serial manipulators [J]. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE, 2022, 236(9): 4946-61. [33] GUO J, ZHANG X, LI H, et al. Mechanism Design and Strength Analysis of Key Components of Flight-climbing-slide Robot for High-voltage Transmission Line Inspection [Z]. 2017 IEEE 7TH ANNUAL INTERNATIONAL CONFERENCE ON CYBER TECHNOLOGY IN AUTOMATION, CONTROL, AND INTELLIGENT SYSTEMS (CYBER). 2017: 109-14 [34] Aladayleh K J, Aladaileh M J. Applying Analytical Hierarchy Process (AHP) to BIM-Based Risk Management for Optimal Performance in Construction Projects[J]. Buildings, 2024, 14(11): 3632. [35] Wu Y, He X, Cui T, et al. Decision‐Making Evaluation and Optimization Strategies for Construction EPC Project Developers Utilizing BIM Technology[J]. Advances in Civil Engineering, 2024, 2024(1): 6694580. [36] Ma S, Tian Q, Zou C, et al. Quality Risk Evaluation of Urban Rail Transit Construction Based on AHP–FCE Method[J]. Advances in Civil Engineering, 2023, 2023(1): 2187071. [37] Hou H C, Wang Y, Lan H. Student residential apartment performance evaluation using integrated AHP-FCE method[J]. Journal of Building Engineering, 2023, 67: 106000. [38] Zeng D, He Q, Yu Z, et al. Risk assessment of sustained casing pressure in gas wells based on the fuzzy comprehensive evaluation method[J]. Journal of Natural Gas Science and Engineering, 2017, 46: 756-763. [39] Kilinç Y, Özdemir Ö, Orhan C, et al. Evaluation of technical performance of pipes in water distribution systems by analytic hierarchy process[J]. Sustainable Cities and Society, 2018, 42: 13-21. [40] He S, Xu H, Zhang J, et al. Risk assessment of oil and gas pipelines hot work based on AHP-FCE[J]. Petroleum, 2023, 9(1): 94-100. [41] Sun B, Tang J, Yu D, et al. Ecosystem health assessment: A PSR analysis combining AHP and FCE methods for Jiaozhou Bay, China1[J]. Ocean & Coastal Management, 2019, 168: 41-50. [42] Zhang J, Wang M, Zhao L, et al. Analysis of factors affecting prefabricated building quality based on ISM-BN[J]. Sustainability, 2023, 15(12): 9682. [43] Huo H, Ji Y, Xie K, et al. Design and Application of Intelligent Construction Quality Inspection System Based on BIM‐RFID[J]. Advances in Civil Engineering, 2023, 2023(1): 5638265. [44] Othman S M N B S, Rahim Z A. Construction 4.0 Intervention in Quality Assessment System in Construction’s Implementation[C]//International Conference of Reliable Information and Communication Technology. Cham: Springer International Publishing, 2021: 713-722. [45] GB/T 50375-2016. Construction Engineering Quality Evaluation Standard [S]. Beijing: China Architecture & Building Press, 2016.