Optimization of Construction Resource Management in Civil Pipeline Engineering Based on MOPSO Algorithm

With the acceleration of urbanization, the construction resource management of civil pipeline engineering construction needs to seek a balance between multiple objectives such as resource allocation. In response to this challenge, a construction resource management optimization model based on an improved multi-objective particle swarm optimization algorithm is proposed to improve the efficiency and quality of construction resource management. The new model integrates the explosion mechanism and dual archive mechanism of the fireworks algorithm to improve the efficiency and quality of the solution. To enhance the generalizability of the model across diverse civil pipeline engineering scenarios, synthetic data were employed for pre-training. This approach addresses the challenge of limited real-world data variability by simulating various construction conditions, such as varying geological environments, resource supply fluctuations, and task complexity gradients. The experiment outcomes indicate that the improved multi-objective particle swarm optimization algorithm has a stable solution time and CPU usage of 2.28 seconds and 28.29% at 100 iterations, which is better than the 3.01 seconds and 42.67% of the multi-role and multi-strategy improved particle swarm algorithm. In practical applications, the new model reduces the total cost of pipeline engineering from 176000 dollars to 157211 dollars, a decrease of 10.68%, and shortens the shortest construction period from 255 days to 230 days, a reduction of 10.20%. The pipeline is buried underground for one year without any damage, and the construction quality is guaranteed. This study presents an effective optimization approach for construction resource management in civil pipeline engineering projects, firmly situated within the realm of urban infrastructure development. By implementing this approach, it not only contributes to a reduction in construction costs and an enhancement in engineering quality, but also pioneers a novel research avenue for multi-objective optimization across diverse types of engineering management scenarios. It can be implemented and applied in future research topics.