INTELLIGENT MONITORING OF THE TECHNICAL CONDITION OF BUILDING STRUCTURES

Abstract

Intelligent monitoring of the technical condition of building structures is one of the most rapidly developing areas of modern civil engineering. Traditional technical inspection systems, based primarily on periodic visual inspections, instrumental measurements, and local non-destructive testing, are gradually being supplemented by continuous data collection, automated diagnostics, and forecasting of structural changes. The current stage of structural monitoring development is characterized by the integration of sensor systems, the Internet of Things, computer vision, machine and deep learning methods, digital twins, and physically informed neural networks. The purpose of this article is to analytically examine modern approaches to intelligent monitoring of building structures and identify the most promising system architecture that integrates data on the actual condition of the structure, artificial intelligence algorithms, and engineering computational models. This article analyzes current research in the fields of Structural Health Monitoring (SHM), intelligent vibration data analysis, computer vision, IoT monitoring, digital twins, self-diagnosing cementitious composites, and physics-informed machine learning. It is shown that the transition from simple damage detection to intelligent condition assessment requires integrating multiple sources of information and taking into account the physical nature of the structure's operation. The scientific novelty of this article lies in the proposed conceptual model of intelligent monitoring. The new model comprises six interconnected levels: digital object context generation, multichannel data collection, preliminary data processing and synchronization, intelligent anomaly and damage detection, assessment of the current technical condition, and forecasting of its changes. Particular attention is paid to the need to maintain the engineering interpretability of artificial intelligence results. It is concluded that the most promising approach is a hybrid model in which machine learning methods do not replace computational and expert methods, but are integrated with them into a unified system for supporting engineering decisions.