Intelligent City Construction Resource Optimization and Scheduling Algorithm Based on T-S Fuzzy Neural Network and Deep Data Mining

The challenges of cost control in construction and installation projects have perennially been a significant concern for entities in the construction sector. The intricate interplay of various equipment, personnel, and tasks during the installation process often leads to an extension in the construction timeline, thereby escalating the overall cost of construction. To address this issue, this study introduces a heuristic two-phase optimization deployment approach. Furthermore, to facilitate efficient and intelligent resource management for urban construction projects, a resource optimization algorithm predicated on the T-S Fuzzy Neural Network (T-S FNN) and deep data mining is proposed. This algorithm employs the T-S FNN to conduct an in-depth analysis and selection of resource data, followed by the application of the Particle Swarm Optimization algorithm to enhance the network. Experimental outcomes demonstrate that the proposed algorithm significantly curtails the overall construction duration and labor cost when juxtaposed with multiple standard allocation strategies. This underscores its efficacy and efficiency in reducing the time and cost associated with project construction.