A Data-Driven Rutting Depth Short-Time Prediction Model With Metaheuristic Optimization for Asphalt Pavements Based on RIOHTrack

Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This study attempts to develop a robust artificial intelligence model to estimate different asphalt pavements’ rutting depth clips, temperature, and load axes as primary characteristics. The experiment data were obtained from19 asphalt pavements with different crude oil sources on a 2.038km long full-scale field accelerated pavement test track(Road Track Institute, RIOHTrack) in Tongzhou, Beijing. In addition,this paper also proposes to build complex networks with different pavement rutting depths through complex network methods and the Louvain algorithm for community detection. The most critical structural elements can be selected from different asphalt pavement rutting data, and similar structural elements can be found. An extreme learning machine algorithm with residual correction(RELM) is designed and optimized using an independent adaptive particle swarm algorithm. The experimental results of the proposed method are compared with several classical machine learning algorithms, with predictions of average root mean squared error(MSE), average mean absolute error(MAE), and a verage mean absolute percentage error(MAPE) for 19 asphalt pavements reaching 1.742, 1.363, and 1.94% respectively. The experiments demonstrate that the RELM algorithm has an advantage over classical machine learning methods in dealing with non-linear problems in road engineering. Notably, the method ensures the adaptation of the simulated environment to different levels of abstraction through the cognitive analysis of the production environment parameters. It is a promising alternative method that facilitates the rapid assessment of pavement conditions and could be applied in the future to production processes in the oil and gas industry.

Optimization of confined aquifer dewatering for long-deep excavation using simulation–optimization method

The confined aquifer dewatering for long-deep excavations usually encounters challenges due to complicated geotechnical conditions,large excavation sizes,and high hydraulic pressures.To propose the most efficient scheme of confined aquifer dewatering for long-deep excavations,dewatering optimizations were performed using the simulation–optimization method.An open cut tunnel of the Jiangyin-Jingjiang Yangtze River Tunnel Project was taken as an example.The methods of finite element and linear programming(LP)were combined to optimize the dewatering process.A three-dimensional finite element model was developed.After simulating the pumping tests,hydraulic conductivity was inverted.Then,necessary parameters in the LP method were determined by simulating dewatering with each pumping well,and various LP models were developed based on some important influence factors such as dewatering sequence,considered pumping wells,and pumping rate limitation.Finally,the optimal pumping rates were solved and applied to the numerical model,with induced drawdown and ground settlement computed for comparison.The results indicate that the optimization can significantly reduce the required wells in the original design.Dewatering in the deepest zone exhibits the highest efficiency for long-deep excavations with gradually varying depths.For the dewatering sequence from the shallowest to the deepest zone,more pumping wells are required but less energy is consumed.Higher quantity and more advantageous locations of pumping wells in the LP model usually result in lower total pumping rate,drawdown,and ground settlement.If more pumping wells are considered in the deepest zone,pumping rate limitation of single well will only slightly increase the total pumping rate,number of required pumping wells,drawdown,and ground settlement.