Estimation of flexible pavement structural capacity using machine learning techniques

The most common index for representing structural condition of the pavement is the structural number.The current procedure for determining structural numbers involves utilizing falling weight deflectometer and ground-penetrating radar tests,recording pavement surface deflections,and analyzing recorded deflections by back-calculation manners.This procedure has two drawbacks:falling weight deflectometer and ground-penetrating radar are expensive tests;back-calculation ways has some inherent shortcomings compared to exact methods as they adopt a trial and error approach.In this study,three machine learning methods entitled Gaussian process regression,M5P model tree,and random forest used for the prediction of structural numbers in flexible pavements.Dataset of this paper is related to 759 flexible pavement sections at Semnan and Khuzestan provinces in Iran and includes“structural number”as output and“surface deflections and surface temperature”as inputs.The accuracy of results was examined based on three criteria of R,MAE,and RMSE.Among the methods employed in this paper,random forest is the most accurate as it yields the best values for above criteria(R=0.841,MAE=0.592,and RMSE=0.760).The proposed method does not require to use ground penetrating radar test,which in turn reduce costs and work difficulty.Using machine learning methods instead of back-calculation improves the calculation process quality and accuracy.

Prediction of vertical displacement for a buried pipeline subjected to normal fault using a hybrid FEM-ANN approach

Fault movement during earthquakes is a geotechnical phenomenon threatening buried pipelines and with the potential to cause severe damage to critical infrastructures.Therefore,effective prediction of pipe displacement is crucial for preventive management strategies.This study aims to develop a fast,hybrid model for predicting vertical displacement of pipe networks when they experience faulting.In this study,the complex behavior of soil and a buried pipeline system subjected to a normal fault is analyzed by using an artificial neural network(ANN)to generate predictions the behavior of the soil when different parameters of it are changed.For this purpose,a finite element model is developed for a pipeline subjected to normal fault displacements.The data bank used for training the ANN includes all the critical soil parameters(cohesion,internal friction angle,Young’s modulus,and faulting).Furthermore,a mathematical formula is presented,based on biases and weights of the ANN model.Experimental results show that the maximum error of the presented formula is 2.03%,which makes the proposed technique efficiently predict the vertical displacement of buried pipelines and hence,helps to optimize the upcoming pipeline projects.