Research on Supervision of Construction Process of Smart Urban Management System Based on Artificial Intelligence

Abstract

With the acceleration of the construction of smart cities, traditional urban management methods can no longer meet the needs of process supervision of complex engineering projects. To address the problems of construction delays, cost overruns and insufficient risk prediction in the current construction of smart urban management systems, this paper proposes an artificial intelligence-based method for construction process supervision. By constructing a three-layer system architecture of perception-decision-execution, integrating Bayesian and DS evidence theory to improve the multi-source data processing capability,introducing LSTM neural network to realize the risk prediction of construction period and cost overruns,and using multi-agent collaborative optimization and particle swarm optimization algorithms to improve decision-making efficiency. An empirical study based on the actual data of Shenzhen's smart urban management construction was conducted. The results demonstrate that the proposed model outperforms traditional methods in terms of construction period prediction accuracy, cost optimization rate and risk warning ability, effectively improving the level of intelligent supervision of the construction process and having good engineering application value.