重载列车提速下弱膨胀土铁路路基状态监测及安全评价案例分析

Case Study on Condition Monitoring and Safety Evaluation of Weakly Expansive Soil Railway Subgrade Under Speed Increase of Heavy-Haul Train

摘要

随着中国国家综合立体交通网建设加速推进,列车重载和提速已经成为发展的必然趋势,但重载列车提速下弱膨胀土铁路路基往往出现病害增多且整修频率增大的问题．本文以某重载铁路路基病害防治工程为例,选取了具有代表性的弱膨胀土路基病害路段K506和K548,开展了沉降、水平位移、土压力和加速度的现场监测,分析了各监测指标的变化规律；利用层次分析法(AHP)和反向传播(BP)神经网络建立了弱膨胀土路基安全评价模型,系统评价了重载列车提速下既有线弱膨胀土铁路路基病害等级．研究结果表明,针对沉降变形,K506、K548断面均呈现出蠕动变形、稳定变形和加速变形3阶段演化特征：在蠕动变形阶段,沉降变形呈现增长缓慢但持续加剧的趋势；进入稳定变形阶段后,K506和K548断面处沉降量分别保持8.57×10^-2 mm/d和5.00×10^-2 mm/d的恒定沉降速率进行增长；在加速变形阶段,沉降量骤增,两断面平均沉降增长速率均突增．对于水平位移,K506 断面演化规律与沉降变化规律类似,同样呈现出3阶段演化特征,但K548断面水平位移并没有呈现3阶段发展趋势,而是随着时间的延长呈现出先增大后稳定的发展趋势．土压力值前期增长速度快,列车动载效应沿路基横断面呈径向衰减趋势．对于路基振动加速度值,两断面在监测150 d时的加速度值均远大于监测30 d时的对应值．基于 AHP,构建重载列车提速下的弱膨胀土铁路路基安全评价指标体系,确定了4个准则层1级指标和20个2级指标,其中列车荷载与降雨强度权重最大且均为0.113 90,排水有效性权重最小为0.008 00．结合BP神经网络方法预测了K506和K548的路基病害等级均为较严重,与专家评价结果保持一致,证明该方法可应用于弱膨胀土铁路路基安全评价.

Abstract

With the accelerated advancement of the construction of national comprehensive multi-dimensional transportation network in China, the heavy haul and speed increase of trains have become an inevitable development trend. However, under the speed increase of heavy-haul trains, the weakly expansive soil railway subgrades often face problems such as a growing number of diseases and a higher frequency of repairs. In this paper, a certain heavy-haul railway subgrade disease prevention and control project was taken as an example. The representative weakly expansive soil subgrade disease sections K506 and K548 were selected, the on-site monitoring of vertical settlement, horizontal displacement, soil pressure and acceleration was conducted, and the variation laws of each monitoring index were analyzed. By using the analytic hierarchy process(AHP) and back propagation(BP) neural network, a safety evaluation model for weakly expansive soil subgrades was established, which systematically evaluated the disease grades of existing weakly expansive soil railway subgrades under the speed increase of heavy-haul trains. Results show that regarding the settlement deformation, both sections exhibited three-stage(i.e., creeping deformation, stable deformation and accelerated deformation) evolution characteristics. At the creeping deformation stage, the settlement deformation showed a trend of slow growth but continuous intensification. At the stable deformation stage, the settlements for sections K506 and K548 increased at constant settlement growth rates of 8.57×10^-2 mm/d and 5.00×10^-2 mm/d, respectively. At the accelerated deformation stage, the settlement increased sharply, and the average settlement growth rates for both sections suddenly rose. As for the horizontal displacement, the evolution law was similar to that of settlement for section K506, showing three-stage evolution characteristics. However, the horizontal displacement for section K548 did not show the three-stage development trend; instead, it showed a trend of first increasing and then stabilizing with the passage of time. The value of soil pressure grew rapidly at the early stage, and the dynamic load effect of trains showed a radial attenuation trend along the subgrade cross-section. As for the subgrade vibration acceleration, the corresponding values for both sections at the end of 150 days of monitoring were much larger than those at the end of 30 days of monitoring. Based on AHP, a safety evaluation index system for weakly expansive soil railway subgrades under the speed increase of heavy-haul trains was constructed, with 4 first-level indexes in the criterion layer and 20 second-level ones. It was found that both the train load and rainfall intensity had the largest weights, i.e., 0.113 90, while the drainage effectiveness had the smallest weight of 0.008 00. Combined with the BP neural network method, it was predicted that the subgrade disease grades of sections K506 and K548 were both relatively serious. The prediction results were consistent with the expert evaluation results, indicating that the proposed method can be applied to the safety evaluation of weakly expansive soft soil railway subgrades.

关键词

重载列车提速 / 安全评价 / 现场监测 / 弱膨胀土 / 反向传播神经网络 / 重载铁路

Key words

speed increase of heavy-haul train / safety evaluation / on-site monitoring / weakly expansive soil / back propagation(BP) neural network / heavy-haul railway

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基金资助

国家自然科学基金资助项目(52378363)

中国国家铁路集团有限公司科技研究开发计划课题(N2023G008)