No abstract

The liquid limit (LL) is considered the most fundamental parameter in soil mechanics for the design and analysis of geotechnical systems. According to the literature, the LL is governed by different particle sizes such as sand content (S), clay content (C), and silt content (M). However, conventional methods do not incorporate the effect of all the influencing factors because traditional methods utilize material passing through a # 40 sieve for LL determination (LL40), which may contain a substantial number of coarse particles. Therefore, recent advancements suggest that the LL must be determined using material passing from a # 200 sieve. However, determining the liquid limit using # 200 sieve material, referred to as LL200 in the laboratory, is a time-consuming and difficult task. In this regard, artificial-intelligence-based techniques are considered the most reliable and robust solutions to such issues. Previous studies have adopted experimental routes to determine LL200 and no such attempt has been made to propose empirical correlation for LL200 determination based on influencing factors such as S, C, M, and LL40. Therefore, this study presents a novel prediction model for the liquid limit based on soil particle sizes smaller than 0.075 mm (# 200 sieve) using gene expression programming (GEP). Laboratory experimental data were utilized to develop a prediction model. The results indicate that the proposed model satisfies all the acceptance requirements of artificial-intelligence-based prediction models in terms of statistical checks such as the correlation coefficient (R2), root-mean-square error (RMSE), mean absolute error (MAE), and relatively squared error (RSE) with minimal error. Sensitivity and parametric studies were also conducted to assess the importance of the individual parameters involved in developing the model. It was observed that LL40 is the most significant parameter, followed by C, M, and S, with sensitivity values of 0.99, 0.93, 0.88, and 0.78, respectively. The model can be utilized in the field with more robustness and has practical applications due to its simple and deterministic nature.

No abstract

A number of measures are being taken to protect the rapidly depleted energy resources around the world. The trend towards sustainable energy resources is increasing, especially in order to improve energy efficiency in transportation vehicles. In this study, the total energy consumption amounts of the railway vehicles were examined based on the line length, number of passengers and the amount of cargo in the last 43 years in our country. For 5 different models created by the artificial neural networks method, the amount of consumed energy and estimated energy amounts were compared using the correlation coefficients, R 2 , absolute error and absolute relative error criteria using Levenberg-Marquardt and Conjugate Gradient Descent algorithms. In the model 3, where the number of passengers and the amount of cargo were used as inputs, accuracy values and error criteria were better. According to the results obtained in the study, it was revealed that the amount of energy consumption is mostly related to the amount of load and then the number of passengers, and the change in line length and years is less effective. With the data obtained in this study, it will be possible to determine the amount of energy that can be spent by using the number of passengers planned to be transported on the railways in the future

No abstract