Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

As a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better

Editorial: Big data and machine learning in geoscience and geoengineering : Introduction

202009 bcmaVersion of RecordPublishe

Bearing capacity prediction of inclined loaded strip footing on reinforced sand by ANN

Laboratory model tests have been conducted on a strip foundation resting over multi-layered geogrid-reinforced dense and loose sand subjected to inclined load. Based on the laboratory model test results, a neural network model is developed to estimate the reduction factor for bearing capacity. The reduction factor obtained by ANN can be used to estimate the ultimate bearing capacity of a strip foundation subjected to centric inclined load from the ultimate bearing capacity of the same foundation under centric vertical loading. A thorough sensitivity analysis was carried out to find out the important parameters affecting the reduction factor. Emphasis was given on the construction of neural interpretation diagram, based on the weights developed in the neural network model, to determine the direct or inverse effect of input parameters to the output. An ANN model equation is developed based on trained weights of the neural network model. The results from artificial neural network (ANN) were com-pared with the laboratory model test results and these results are in good agreement

Prediction of ultimate bearing capacity eccentrically loaded rectangular foundations using ANN

Extensive laboratory model tests were conducted on a rectangular embedded foundations resting over homogeneous sand bed and subjected to eccentric load to determine the ultimate bearing capacity. The depth of embedment varies from 0 to B with an increment of 0.5B; where B is the width of foundation and the eccentricity ratio (e/B) was varied from 0 to 0.15 with increments of 0.05. Based on the laboratory model test results, a neural network model has been developed to estimate the reduction factor (RF). The reduction factor can be used to estimate the ultimate bearing capacity of an eccentrically loaded foundation from the ultimate bearing capacity of a centrally loaded foundation. A thorough sensitivity analysis has been carried out to determine the important parameters affecting the reduction factor. Importance was given on the construction of neural interpretation diagram, and based on this diagram, whether direct or inverse relationships exist between the input and output parameters was determined. The results from artificial neural network (ANN) were compared with the laboratory model test results and the agreement is good