Elsevier

 Journal

 An Artificial Neural Network-based Mathematical Model For The Prediction Of Blast-induced Ground Vibration In Granite Quarries In Ibadan, Oyo State, Nigeria

 Abiodun Ismail LAWAL

 2020

Blast-induced ground vibration is one of the most severe and complex environmental problems associated with blasting operation. The scaled-distance approach is the common method of estimating the magnitude of the blast-induced ground vibration. However, the prediction of this approach is inaccurate as evident in the literature. Therefore, this study proposed an artificial neural network model for the prediction of blasting operations in five granite quarries in Ibadan, Oyo State, Nigeria. The distance from the measuring sta- tion to the blasting point (D) and a charge per delay (Q) were the two input parame- ters into the model while the peak particle velocity (PPV) was the targeted output. 100 datasets were used in developing the model. The datasets were divided into training, test- ing, and validation. The ANN model was trained using backpropagation algorithm with the Levenberg-Marquardt training function. The weights and biases obtained from the trained ANN architecture were extracted and transformed into a simple mathematical equation for the computation of PPV. The obtained results from the ANN model was compared with the prediction of multilinear regression (MLR). The coefficient of determination (R 2 ) of the pro- posed ANN model is 0.988 while that of the MLR model is 0.738. The mean absolute per- centage error (MAPE), root-mean-squared error (RMSE), and variance accounted for (VAF) were also used to further evaluate the performance of the models. The MAPE, RMSE, and VAF of the ANN model are 7.14, 2.90, and 98.74 while that of the MLR model is 40.90, 13.35, and 73.76. Therefore, the proposed ANN model can give a reasonable prediction of the PPV. 

 Taylor And Francis

 Journal

 An Artificial Neural Network-based Mathematical Model For The Prediction Of Blast-induced Ground Vibrations

 Lawal AI, Idris MA

 2019

This paper presents an artificial neural network (ANN) based mathematical model for the prediction of blast-induced ground vibrations using the data obtained from the literature. A feed-forward back-propagation multi-layer perceptron (MLP) was adopted, and the Levenberg–Marquardt algorithm was used in training the network. The powder factor, the maximum charge per delay, and distance from blasting face to monitoring point are the input variables. The peak particle velocity (PPV) is the targeted output variable. The model was then formulated using the weights and biases output from the ANN simulation. Multilinear regression (MLR) analysis was also performed using the same number of datasets, as in the case of ANN. The quality of the proposed ANN-based model was also tested with another 14 datasets outside the one used in developing the models and compared with more classical models. The coefficient of the determination (R2) of the proposed ANN-based model was the highest. 

 Taylor & Francis

 Journal

 Prediction Of Gross Calorific Value Of Solid Fuels From Their Proximate Analysis Using Soft Computing And Regression Analysis

 Moshood Onifade, Abiodun Ismail Lawal, Adeyemi Emman Aladejare, Samson Bada & Musa Adebayo Idris

 2019

The determination of gross calorific value (GCV) of solid fuel is important because GCV is frequently required in the design ofmost combustion and other thermal systems. However, experimental determination of GCV is time-consuming, which necessitated the development of different empirical equations to estimate GCV using the elemental composition of the solid fuels. With the growing popularity of empirical equations for estimation of GCV of solid fuels, there is a need to develop reliable and suitable models for the prediction of GCV of coal from the South African coalfields (SAC). In this study, empirical models were developed to determine the relationship between the proximate analysis of coal with its GCV, using soft computing and regression analyses. A total of 32 coal samples were used to develop three empirical models based on soft computing techniques, namely; adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANN), and regression analysis using multilinear regression (MLR). The performances of the proposed models were evaluated using coefficient of determination (R2), mean absolute percentage error (MAPE), mean squared error (MSE) and variance accounted for (VAF). The R2, MAPE, MSE and VAF for the ANFIS are 99.92%, 2.0395%, 0.0778 and 99.918% while for the ANN, they are 99.71%, 2.863%, 0.2834 and 99.703%. The R2, MAPE, MSE and VAF for the MLR are 99.46%, 3.551%, 0.5127 and 99.460%. From the soft computing and regression analysis studies conducted, the ANFIS was found as the most suitable model for predicting the GCV for these coal samples. 

 Springer

 Journal

 Estimation Of Static Earth Pressures For A Sloping Cohesive Backfill Using Extended Rankine Theory With A Composite Log-spiral Failure Surface

 Shi-Yu Xu, Abiodun Ismail Lawal, Anoosh Shamsabadi, Ertugrul Taciroglu

 2018

The Rankine earth pressure theory is extended herein to an inclined c–/ backfill. An analytical approach is then proposed to compute the static passive and active lateral earth pressures for a sloping cohesive backfill retained by a vertical wall, with the presence of wall–soil interface adhesion. The proposed method is based on a limit equilibrium analysis coupled with the method of slices wherein the assumed profile of the backfill failure surface is a composite of log-spiral and linear segments. The geometry of the failure surface is determined using the stress states of the soil at the two boundaries of the mobilized soil mass. The resultant lateral earth thrust, the point of application, and the induced moment on the wall are computed considering global and local equilibrium of forces and moments. Results of the proposed approach are compared with those predicted by a number of analytical models currently adopted in the design practice for various combinations of soil’s frictional angles, wall–soil interface frictional angles, inclined angles of backfill and soil cohesions. The predicted results are also verified against those obtained from finite element analyses for several scenarios under the passive condition. It is found that the magnitude of earth thrust increases with the backfill inclination angle under both the passive and active conditions.