Critical Analysis of the Use of Digital Image Analysis Technology to Monitor Construction Facilities and Materials

Introduction.

Due to the latest wide-scale adoption of advanced technologies in the construction sector and the pressure to complete projects in the shortest time and at minimal cost, the principle of achieving a high-quality level in project implementation is a daunting task for those working in construction. In this context digital image analysis technology has served as a viable solution to meet the requirements for improving project management efficiency at different stages. This literature review examines the role of digital image processing technologies in monitoring construction materials and structures throughout the project lifecycle in order to improve their quality and efficiency. The aim of the study is to evaluate the efficiency of this technology compared to traditional methods, review the latest developments in digital image processing for monitoring building structures, and identify performance indicators such as time and cost, as well as mention obstacles preventing its wide-scale adoption in engineering.

Materials and Methods.

More than 30 publications (2015-2024) covering AI algorithms (CNN, YOLOv4), 3D modeling (LiDAR, Structure from Motion) and BIM integration were systematically reviewed. Their applicability, scalability, and impact on structural condition monitoring were evaluated.

Research Results.

According to the results, the use of digital image analysis technology as a tool for monitoring structures and quality control of construction materials at different stages of a project lifecycle caused improved project quality, reduced time and costs, and boosted decision-making at different stages of a project cycle. The integration of image processing with artificial intelligence and building information modeling systems proved to be accurate in detecting defects in buildings and building materials with a 25% increase in the project management efficiency.

Discussion and Conclusion.

Digital image processing (DIP) holds a transformational potential, but it is facing some obstacles such as environmental influences, data heterogeneity and lack of standardization. LiDAR integration, development of sustainable machine learning models for multimodal data analysis, and strengthening interdisciplinary collaboration are set forth. In order to overcome the restraints, further research is required to optimize technologies for real-world operating conditions. DIP is revolutionizing design monitoring, but mass adoption is possible only by means of sustainable innovation, industry-wide partnerships, and adaptation to external factors.