MLLBC: A Machine Learning Toolbox for Modeling the Loss Rate of the Lining Bearing Capacity

Abstract

Testing the health of tunnels, as a branch of highway operation, has an extremely important application in public property and even life safety. Among them, there are many factors that cause the tunnel to deform or collapse. The conventional methods use the finite element method (FEM) which are to simulate the bearing capacity loss rate of the lining by using the mechanical method. However, it takes a long time to calculate the stress-strain-situation of the lining model under each condition. This paper explores the machine learning to calculate the loss rate of the lining bearing capacity under more conditions based on FEM simulation data. Here, we establish a machine learning toolbox for modeling the loss rate of the lining bearing capacity named ''MLLBC'', which contains three main components: 1) data loading; 2) machine learning model deployment; 3) performance evaluation. To ensure the fairness of model evaluation, ten machine learning models use a unified code library. We also conduct experiments on our new dataset which is the loss rate of the lining bearing capacity with different data amounts, as well as experiments on the goodness of model fitting under different ranges of various variables. INDEX TERMS Toolbox, the loss rate of the lining bearing capacity, machine learning, tunnel health.

Monthly Probability Theoretical Line Loss Calculation Method of Low Voltage Distribution Network Based on Simultaneous Power and Electricity

“…In order to prove the effectiveness of the method proposed in this paper, the previous works, i.e. the line loss calculation method using daily electricity [17] (called method 1 in this paper) and the line loss calculation method using hourly electricity [18] (called method 2 in this paper) are compared in the simulation.…”

“…The probability analysis method of TLL is an emerging method for evaluating TLL in recent years [17] . This method can consider the impact of random changes in load, power source, and other factors on line loss rate, thus can accurately evaluating the distribution range of line loss rate in a specific power grid in the long, medium, and short term [18] .…”

“…The measurement data used in this paper is compared with the previous works, i.e. the line loss calculation method using daily electricity [17] (called method 1 in this paper) and the line loss calculation method using hourly electricity [18] (called method 2 in this paper), as shown in Table 1. By using the power and electricity data in the hour level time period, the time interval of theory line loss calculation is shortened compared with the previous line loss calculation method based on daily electricity and hourly electricity, thus can improve the accuracy of TLL calculation.…”

Combining the fractional order derivative and machine learning for leaf water content estimation of spring wheat using hyper-spectral indices

“…Support vector regression (SVR) is a type of support vector machine (SVM) that is used for regression tasks. It tries to find a function that best predicts the continuous output value for a given input value [ 61 , 62 ]. The basic steps for building an SVR model are as follows: (1) Data preparation: Collect and preprocess the training data, including feature selection, data cleaning, and normalization; (2) Feature scaling: Scale the input features to ensure they have similar ranges and magnitudes.…”

“…Support vector regression (SVR) is a type of support vector machine (SVM) that is used for regression tasks. It tries to find a function that best predicts the continuous output value for a given input value [61,62]…”