scholarly journals Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model

2019 ◽  
Vol 4 (2) ◽  
pp. 26 ◽  
Author(s):  
Danial Mohammadzadeh S. ◽  
Seyed-Farzan Kazemi ◽  
Amir Mosavi ◽  
Ehsan Nasseralshariati ◽  
Joseph H. M. Tah
Keyword(s):  
Gene Expression ◽  
Mean Squared Error ◽  
Programming Model ◽  
Gene Expression Programming ◽  
Liquid Limit ◽  
Coefficient Of Determination ◽  
Average Error ◽  
Soil Parameters ◽  
Compression Index ◽  
Fine Grained

In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (Cc) is a key parameter in modeling the settlement of fine-grained soil layers. However, the estimation of this parameter is costly, time-consuming, and requires skilled technicians. To overcome these drawbacks, we aimed to predict Cc through other soil parameters, i.e., the liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Using these parameters is more convenient and requires substantially less time and cost compared to the conventional tests to estimate Cc. This study presents a novel prediction model for the Cc of fine-grained soils using gene expression programming (GEP). A database consisting of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of the developed GEP-based model was evaluated through the coefficient of determination (R2), the root mean squared error (RMSE), and the mean average error (MAE). The proposed model performed better in terms of R2, RMSE, and MAE compared to the other models.

scholarly journals Evolutionary Prediction Model for Fine-Grained Soils Compression Index Using Gene-Expression Programming

2019 ◽  
Author(s):  
Danial Mohammadzadeh ◽  
Seyed-Farzan Kazemi ◽  
Amir Mosavi
Keyword(s):  
Gene Expression ◽  
Prediction Model ◽  
Closed Form ◽  
Data Science ◽  
Gene Expression Programming ◽  
Closed Form Solution ◽  
Average Error ◽  
Soil Parameters ◽  
Compression Index ◽  
Fine Grained

Appropriate estimation of soil settlement is of significant importance since it directly influences the performance of building and infrastructures that are built on soil. In particular, the settlement of fine-grained soils is critical because of low permeability and continuous settlement with time. Coefficient of consolidation (Cc) is a key parameter to estimate settlement of fine-grained soil layers. However, estimation of this parameter is time consuming, needs skilled technicians, and specific equipment. In this study, Cc was estimated using several soil parameters such as liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Estimating such parameters in laboratory is straight forward and needs substantially less time and cost compared to conventional tests to estimate Cc such as oedometer test. This study presents a novel prediction model for Cc of fine-grained soils using gene-expression programming (GEP). GEP is a biologically inspired technique capable of offering closed-form solution for the optimal solution. A database consisted of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of developed GEP-based model was evaluated through coefficient of determination (R2), root mean squared error (RMSE), and mean average error (MAE). High R2 and low error values indicated the descent performance of the model. Furthermore, the model was evaluated using the additional performance measures and met all the suggested criteria. Furthermore, the model had a better performance in terms of R2, RMSE, and MAE compared to most of existing models. It is expected that the developed model will decrease the time and cost associate with determining Cc of fine-grained soils.Keywords: evolutionary model, gene-expression programming (GEP), prediction, soil compression index, estimation, soil engineering, soil informatics, civil engineering, machine learning, data science, big data, soft computing, deep learning, forecasting, subject classification codes, construction informatics, computational intelligence (CI), artificial intelligence (AI), estimation

scholarly journals Modelling Reservoir Turbidity Using Landsat 8 Satellite Imagery by Gene Expression Programming

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1479 ◽  
Author(s):  
Liu ◽  
Wang
Keyword(s):  
Gene Expression ◽  
Performance Evaluation ◽  
Satellite Imagery ◽  
Mean Squared Error ◽  
Accuracy Assessment ◽  
Gene Expression Programming ◽  
Model Performance ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Model Performance Evaluation

This study aimed to develop a reliable turbidity model to assess reservoir turbidity based on Landsat-8 satellite imagery. Models were established by multiple linear regression (MLR) and gene-expression programming (GEP) algorithms. Totally 55 and 18 measured turbidity data from Tseng-Wen and Nan-Hwa reservoir paired and screened with satellite imagery. Finally, MLR and GEP were applied to simulated 13 turbid water data for critical turbidity assessment. The coefficient of determination (R2), root mean squared error (RMSE), and relative RMSE (R-RMSE) calculated for model performance evaluation. The result show that, in model development, MLR and GEP shows a similar consequent. However, in model testing, the R2, RMSE, and R-RMSE of MLR and GEP are 0.7277 and 0.8278, 0.7248 NTU and 0.5815 NTU, 22.26% and 17.86%, respectively. Accuracy assessment result shows that GEP is more reasonable than MLR, even in critical turbidity situation, GEP is more convincible. In the model performance evaluation, MLR and GEP are normal and good level, in critical turbidity condition, GEP even belongs to outstanding level. These results exhibit GEP denotes rationality and with relatively good applicability for turbidity simulation. From this study, one can conclude that GEP is suitable for turbidity modeling and is accurate enough for reservoir turbidity estimation.

scholarly journals Prediction of the Void Ratio Parameter in Mineral Tailings Using Gene Expression Programming

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Ali Akbar Heshmati R. ◽  
Hossein Salehzadeh ◽  
Mehdi Shahidi
Keyword(s):  
Gene Expression ◽  
Void Ratio ◽  
Gene Expression Programming ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Physical Parameters ◽  
Experimental Database ◽  
Initial Void Ratio ◽  
Different Sources ◽  
Initial Prediction

Mineral tailing deposits are one of the most important issues in the field of geotechnical engineering. The void ratio of mineral tailings is an essential parameter for investigating the geotechnical behavior of tailings. However, there has not yet been a comprehensive empirical formulation for initial prediction of the void ratio of mineral tailings. In this study, the void ratio of various types of mineral waste is estimated by using gene expression programming (GEP). Therefore, taking into consideration the effective physical parameters that affect the estimation of this parameter, eight different models are presented. A reliable experimental database collected from different sources in the literature was applied to develop the GEP models. The performance of the developed GEP models was measured based on coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). According to the results, the model with effective stress σ ′ , initial void ratio (e0), and parameters of R2 = 0.92, MAE = 0.109, and RMSE = 0.180 performed the best. Finally, a new empirical formulation for the initial prediction of the void ratio parameter is proposed based on the aforementioned analyses.

Modeling Diesel and Hybrid Bus Fuel Consumption with Virginia Tech Comprehensive Power-Based Fuel Consumption Model: Model enhancements and Calibration issues

2015 ◽  
Vol 2533 (1) ◽  
pp. 100-108 ◽  
Author(s):  
William Edwardes ◽  
Hesham Rakha
Keyword(s):  
Fuel Consumption ◽  
Field Data ◽  
Mean Squared Error ◽  
Carbon Dioxide Emission ◽  
Calibration Procedure ◽  
Virginia Tech ◽  
Coefficient Of Determination ◽  
Average Error ◽  
Model Parameters ◽  
Consumption Model

The goal of this paper was to develop a calibration procedure and use it to estimate diesel bus fuel consumption and carbon dioxide emission levels. There are few models for estimating those values. Available models require dynamometer data to calibrate model parameters and produce a bang-bang control system (optimum control entails maximum throttle and braking input). The only diesel fuel consumption model that does not suffer from these deficiencies is the Virginia Tech comprehensive power-based fuel consumption model (VT-CPFM). VT-CPFM can be calibrated with publicly available data from the Altoona Bus Research and Testing Center. However, each bus is slightly different because it is built and tuned for the specific transit agency. Consequently, research presented in this paper enhanced the VT-CPFM for modeling diesel buses and developed a procedure for calibrating bus fuel consumption models by using in-field data. All models produced a good fit to the in-field data with a coefficient of determination ( R2) greater than .936, and the sum of the mean squared error for each quarter of a second was less than 0.002. Validation found an average error of 17.55% in total fuel consumed during the validation portion of the test. However, for tests with air-conditioning on, the average error was 10.82%.

Comparing the efficiency of ordinary kriging and cokriging to estimate the Atterberg limits spatially using some soil physical properties

Clay Minerals ◽  
2009 ◽  
Vol 44 (2) ◽  
pp. 181-193 ◽  
Author(s):  
O. Baskan ◽  
G. Erpul ◽  
O. Dengiz
Keyword(s):  
Ordinary Kriging ◽  
Mean Squared Error ◽  
Field Capacity ◽  
Liquid Limit ◽  
Atterberg Limits ◽  
Engineering Properties ◽  
Soil Parameters ◽  
Regression Equations ◽  
Soil System ◽  
The Mean

AbstractThe spatial distribution of the Atterberg limits can be used to distinguish the consistency and behaviour of a soil and its engineering properties, which strongly depends on the water content of the soil and types of silts and clays in the soil. By spatial modeling, and comparing the results of ordinary kriging with the cokriging approach, this study aims to find correlations between the Atterberg limits and the selected physical soil parameters in order to examine how effective they are in generating an understanding of the dynamics of a physical soil system.In 156 soil samples, the Atterberg limits and soil moisture conditions were determined, and auxiliary functions were selected by application of cokriging using correlation analysis and regression equations obtained by the residual maximum likelihood (REML). These techniques were evaluated by the results of the mean absolute error (MAE) and the mean squared error (MSE). Cokriging analysis was found to be more effective at estimating the liquid limit (WLL) and the plastic limit (WPL) than kriging analysis and with smaller error values. On the other hand, the kriging approach, which had smaller MAE and MSE values, was more effective at estimating the plasticity index (WPI) values than the cokriging method. Unlike the REML regression equations, the field capacity (FC) value was the more suitable parameter for the cokriging estimates. When the necessary labour and time were considered for determining the Atterberg limits, both kriging and cokriging were found to be applicable for estimation of these limits.

scholarly journals Prediction of the depth of local scouring at a bridge pier using a gene expression programming method

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Waqed H. Hassan ◽  
Halah K. Jalal
Keyword(s):  
Gene Expression ◽  
Mean Squared Error ◽  
Human Life ◽  
Gene Expression Programming ◽  
Absolute Error ◽  
Width Ratio ◽  
Statistical Parameters ◽  
Data Set ◽  
Total Data ◽  
Local Depth

AbstractLocal scouring around the piers of a bridge is the one of the major reasons for bridge failure, potentially resulting in heavy losses in terms of both the economy and human life. Prediction of accurate depth of local scouring is a difficult task due to the many factors that contribute to this process, however. The main aim of this study is thus to offer a new formula for the prediction the local depth of scouring around the pier of a bridge using a modern fine computing modelling technique known as gene expression programming (GEP), with data obtained from numerical simulations used to compare GEP performance with that of a standard non-linear regression (NLR) model. The best technique for prediction of the local scouring depth is then determined based on three statistical parameters: the determination coefficient (R2), mean absolute error (MAE), and root mean squared error (RMSE). A total data set of 243 measurements, obtained by numerical simulation in Flow-3D, for intensity of flow, ratio of pier width, ratio of flow depth, pier Froude number, and pier shape factor is divided into training and validation (testing) datasets to achieve this. The results suggest that the formula from the GEP model provides better performance for predicting the local depth of scouring as compared with conventional regression with the NLR model, with R2 = 0.901, MAE = 0.111, and RMSE = 0.142. The sensitivity analysis results further suggest that the ratio of the depth of flow has the greatest impact on the prediction of local scour depth as compared to the other input parameters. The formula obtained from the GEP model gives the best predictor of depth of scouring, and, in addition, GEP offers the special feature of providing both explicit and compressed arithmetical terms to allow calculation of such depth of scouring.

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