Abstract: With the maturity of the construction technology, the number of extra-long tunnels built in China is gradually increasing. Extra-long tunnels are usually the key activities in a single project due to their long duration, and their duration has a direct impact on the single project. At the same time, the construction cost of long tunnels is high, and balancing the duration and cost is an important goal in the preparation of the construction schedule of long tunnels. In the current situation, the preparation of extra-long tunnel construction schedule is still based on manual experience, and the duration and cost are not yet finely and intelligently managed, which is a pain point that needs to be solved at this time of rapid construction of extra-long tunnels.
The extra-long tunnel is usually divided into several sections for parallel construction to shorten the construction period, and the parallel construction requires additional working surfaces by excavating auxiliary tunnels outside the tunnel. Firstly, the number of working faces directly affects the duration and cost of the project. Secondly, when the number of working faces is constant, where to excavate auxiliary tunnels and where to increase working faces will also have an impact on the construction period and cost. Extra-long tunnels are usually regarded as key activities, and their construction period cannot be delayed. In this case, how to save costs while meeting the construction period requirements is a problem worthy of attention.
When the tunnel realizes multi-face construction by excavating auxiliary tunnels, the sequence of each construction unit is not fixed but can be adjusted. The logical sequence of construction is soft logic, and the construction sequence will affect the duration and cost of the project. The trade-off model of duration and cost is constructed to minimize the project cost under the premise of meeting the duration. The discrete time-cost trade-off problem is NP-hard, and the addition of soft logic increases the difficulty of solving it. Therefore, the operator of genetic algorithm is improved to increase its solution speed and accuracy. Improvements include: (1)Designing adaptive crossover, mutation probability and improved catastrophe operator to avoid the algorithm from falling into local optimum; (2)Optimizing the mutation strategy to enhance the local search ability of the algorithm. Through investigation and research, data of a railroad extra-long tunnel projects are collected, including length, construction time and cost, etc.
Based on the example, the model and algorithm are verified to be effective for the optimization problem. In addition, to test the superiority of the improved algorithm, nine calculation examples with different scales and constraints are designed to compare the proposed algorithm with other algorithms horizontally. For each calculation example, the three comparison algorithms are run 20 times respectively. On the one hand, the average and minimum values of the solution results of each algorithm are counted to compare the stability and optimization ability of the algorithm operation. On the other hand, the average running time of 20 times for each algorithm is counted, and the difference in solution speed is analyzed.
The experimental results show that: (1)The proposed model and algorithm can be effectively applied to the extra-long tunnel construction schedule optimization problem. The compiler only needs to input the information of construction unit length, construction speed and cost, as well as the time and cost of auxiliary tunnels, and then can get the number and locations of auxiliary tunnels, the construction mode of working faces, and the total duration and cost and so on. (2)By comparing the proposed improved genetic algorithm with the standard genetic algorithm and particle swarm algorithm, it can be found that the other two algorithms can easily fall into local optimum when solving the problem as the size of the problem increases. Moreover, the solution obtained by the particle swarm algorithm is more volatile, while the solution obtained by the improved genetic algorithm is superior and more stable. In terms of running speed, the improved genetic algorithm is faster than the standard genetic algorithm. This is due to the fact that the improved genetic algorithm optimizes the search mechanism. The above results demonstrate that the improved genetic algorithm has better global search capability, stability and faster running speed when dealing with large-scale tunnel schedule optimization problems.
This study can help enterprises to realize the intelligent compilation of extra-long tunnel construction schedule, assist in enterprises to make scientific decisions on the number and location of auxiliary tunnels. It helps to reduce unfavorable decisions due to human experience, and helps to control the project duration and cost. This study is applicable to the case where the auxiliary tunnel is an inclined shaft, vertical shaft or horizontal cavern. When the auxiliary tunnel is in other forms, the model needs to be constructed according to its construction characteristics, which will be improved through further research.

Key words: scheduling, tunnel engineering, multiple working surfaces, soft logic, genetic algorithm

摘要： 特长隧道通常采用多工作面施工,解决带软逻辑的施工进度计划优化问题是特长隧道施工组织策划的重要难题。基于软逻辑关系,结合隧道工程施工特性,构建了截止工期约束下施工成本最小化的特长隧道施工进度计划优化模型;针对问题求解的复杂性,对遗传算法进行改进,设计了自适应的交叉、变异概率和改进的灾变算子避免算法陷入局部最优,同时优化了变异策略以增强局部搜索能力;采用多个特长隧道算例对模型及算法进行验证,实验结果表明:所提模型对于特长隧道施工进度计划优化具有可行性和有效性;改进的遗传算法在应对大规模问题时仍能表现出较好的全局寻优能力和稳定性。研究成果能够为施工单位编制特长隧道施工进度计划、决策辅助坑道数量和位置等提供有益参考。

关键词: 进度计划, 隧道工程, 多工作面, 软逻辑, 遗传算法