Wyniki wyszukiwania - BazTech

1

Application of M5P Model Tree and Artificial Neural Networks for Traffic Noise Prediction on Highways of India

Mann Suman, Singh Gyanendra

Civil and Environmental Engineering Reports

|

2024

|

Vol. 34, no. 2

45--62

EN

Traffic noise prediction is the fastest growing development that reflects the rising concern of noise as environmental pollution. Prediction of noise exposure levels can help policy makers and government authorities to make early decisions and plan effective measures to mitigate noise pollution and protect human health. This study examines the application of M5P model tree and Artificial Neural Network (ANN) for prediction of traffic noise on Highways of Delhi. In total 865 data sets collected from 36 sampling stations were used for development of model. Effects of 13 independent variables were considered for prediction. Model selection criteria like determination coefficient (R2 ), root mean square error (RMSE), Mean absolute error (MSE) are used to judge the suitability of developed models. The work shows that both the models can predict traffic noise accurately, with R2 values of 0.922(M5P), 0.942(ANN) and RMSE of 2.17(M5P) ,1.95(ANN). The results indicate that machine learning approach provides better performance in complex areas, with heterogenous traffic patterns. M5p Model tree gives linear equations which are easy to comprehend and provides better insight, indicating that M5P model trees can be effectively used as an alternative to ANN for predicting traffic noise.

2

Soft computing based prediction of friction angle of clay

Dutta R. K., Gnananandarao T., Ladol S.

Archives of Materials Science and Engineering

|

2020

|

Vol. 104, nr 2

58--68

EN

Purpose: This article uses soft computing-based techniques to elaborate a study on the prediction of the friction angle of clay. Design/methodology/approach: A total of 30 data points were collected from the literature to predict the friction angle of the clay. To achieve the friction angle, the independent parameters sand content, silt content, plastic limit and liquid limit were used in the soft computing techniques such as artificial neural networks, M5P model tree and multi regression analysis. Findings: The major findings from this study are that the artificial neural networks are predicting the friction angle of the clay accurately than the M5P model and multi regression analysis. The sensitivity analysis reveals that the clay content is the major influencing independent parameter to predict the friction angle of the clay followed by sand content, liquid limit and plastic limit. Research limitations/implications: The proposed expressions can used to predict the friction angle of the clay accurately but can be further improved using large data for a wider range of applications. Practical implications: The proposed equations can be used to calculate the friction angle of the clay based on sand content, silt content, plastic limit and liquid limit. Originality/value: There is no such expression available in the literature based on soft computing techniques to calculate the friction angle of the clay.

3

Prediction of bearing capacity of H shaped skirted footings on sand using soft computing techniques

Gnananandarao, Khatri V. N., Dutta R. K.

Archives of Materials Science and Engineering

|

2020

|

Vol. 103, nr 2

62--74

EN

Purpose: The present study aims to apply soft computing techniques, Artificial Neural Network (ANN) and M5P model tree, to predict the ultimate bearing capacity of the H plan shaped skirted footing on the sand Design/methodology/approach: A total of 162 laboratory test data for the regular plan shaped (square, circular, rectangular, and strip (up to L/B = 2.5) skirted footing were collected from the literature to develop the soft computing-based models. These models were later modified for the H Plan shaped skirted footing with the introduction of the multiplication factor. The input variables chosen for the regular plan shaped footings were skirt depth to width of the footing ratio (Ds/B), friction angle of the sand (o), the ratio of the interface friction angle-to-friction angle of sand (5/o), and length-to-width (L/B) ratio of the footing. The output is the bearing capacity ratio (BCR, a ratio of the bearing capacity of the skirted footing to the bearing capacity of un-skirted footing). Findings: Sensitivity analysis was carried out to see the impact of the individual variable on the BCR). The sensitivity results reveal that the skirt depth to width of the footing ratio is the primary variable affecting the BCR. Finally, the performance of the developed soft computing models was assessed using six statistical parameters. The results from the statistical parameters reveal that model developed using ANN was performing superior to the one prepared using M5P model tree technique for the prediction of the ultimate bearing capacity of H plan shaped skirted footing on sand. Research limitations/implications: The model equations are developed with experimental laboratory data. Hence, these equations need further improvement by using field data. However, until now there no field data have been available to include in the present data set. Practical implications: These proposed model equations can be used to predict the bearing capacity of the H-shaped footing with the help of Ds/B, o, S/o and L/B without performing the laboratory experiments. Originality/value: There is no such model equation that was developed so far for the H-shaped skirted footings. Hence, an attempt was made in this article to predict the bearing capacity of the H-shaped footing by using available experimental data with the help of soft computing techniques.