Application of multilinear regression analysis in modeling of soil properties for geotechnical civil engineering works in Calabar South

Egbe, Jerome; Ewa, DE; Ubi, Stanley Emmanuel; Ikwa, G.B.; Tumenayo, O.O.

doi:10.4314/njt.v36i4.10

Cited by 23 publications

(10 citation statements)

References 2 publications

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“…In addition, the use of classical MVR to predict the bearing capacity of pile was also applied. Multi-variable regression technique is commonly used in several studies, such as Egbe et al [70] and Silva et al [71] to predict the properties and chemical composition of soil. The regression coefficient results are shown in Table 8.…”

Section: Comparison With Empirical Equations and Multi-variable Regrementioning

confidence: 99%

Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest

Pham

Tran

et al. 2020

Applied Sciences

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Axial bearing capacity of piles is the most important parameter in pile foundation design. In this paper, artificial neural network (ANN) and random forest (RF) algorithms were utilized to predict the ultimate axial bearing capacity of driven piles. An unprecedented database containing 2314 driven pile static load test reports were gathered, including the pile diameter, length of pile segments, natural ground elevation, pile top elevation, guide pile segment stop driving elevation, pile tip elevation, average standard penetration test (SPT) value along the embedded length of pile, and average SPT blow counts at the tip of pile as input variables, whereas the ultimate load on pile top was considered as output variable. The dataset was divided into the training (70%) and testing (30%) parts for the construction and validation phases, respectively. Various error criteria, namely mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination (R2) were used to evaluate the performance of RF and ANN algorithms. In addition, the predicted results of pile load tests were compared with five empirical equations derived from the literature and with classical multi-variable regression. The results showed that RF outperformed ANN and other methods. Sensitivity analysis was conducted to reveal that the average SPT value and pile tip elevation were the most important factors in predicting the axial bearing capacity of piles.

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Section: Comparison With Empirical Equations and Multi-variable Regrementioning

confidence: 99%

Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest

Pham

Tran

et al. 2020

Applied Sciences

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“…In addition, the multivariate regression (MVR) model is also used to compare with the prediction results of the FNN model. This is one of the most popular models in statistical probability and has proven successful in many different fields [ 25 , 35 , 36 ]. The general formula of MVR has the following form:

where β j are the coefficients of j th input and β 0 is the intercept.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, the multivariate regression (MVR) model is also used to compare with the prediction results of the FNN model. is is one of the most popular models in statistical probability and has proven successful in many different fields [25,35,36].…”

Section: Comparing With Empirical Formulas and Other Modelsmentioning

confidence: 99%

Application of Feedforward Neural Network and SPT Results in the Estimation of Seismic Soil Liquefaction Triggering

Pham

2021

Computational Intelligence and Neuroscience

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Soil liquefaction is a dangerous phenomenon for structures that lose their shear strength and soil resistance, occurring during seismic shocks such as earthquakes or sudden stress conditions. Determining the liquefaction and nonliquefaction capacity of soil is a difficult but necessary job when constructing structures in earthquake zones. Usually, the possibility of soil liquefaction is determined by laboratory tests on soil samples subjected to dynamic loads, and this is time-consuming and costly. Therefore, this study focuses on the development of a machine learning model called a Forward Neural Network (FNN) to estimate the activation of soil liquefaction under seismic condition. The database is collected from the published literature, including 270 liquefaction cases and 216 nonliquefaction case histories under different geological conditions and earthquakes used for construction and confirming the model. The model is built and optimized for hyperparameters based on a technique known as random search (RS). Then, the L2 regularization technique is used to solve the overfitting problem of the model. The analysis results are compared with a series of empirical formulas as well as some popular machine learning (ML) models. The results show that the RS-L2-FNN model successfully predicts soil liquefaction with an accuracy of 90.33% on the entire dataset and an average accuracy of 88.4% after 300 simulations which takes into account the random split of the datasets. Compared with the empirical formulas as well as other machine learning models, the RS-L2-FNN model shows superior performance and solves the overfitting problem of the model. In addition, the global sensitivity analysis technique is used to detect the most important input characteristics affecting the activation prediction of liquefied soils. The results show that the corrected SPT resistance (N1)60 is the most important input variable, affecting the determination of the liquefaction capacity of the soil. This study provides a powerful tool that allows rapid and accurate prediction of liquefaction based on several basic soil properties.

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“…Yongwei et al, [12], identified the effectiveness of a particular set of important management programs in improving construction labor productivity. While Egbe et al, [13], applied multi-linear regression analysis model for predicting soil properties in Calabar South to offer a technical guide and solution in foundation designs problems in the area, Mohammad et al, [14] described the development of a multi-linear regression model to predict the effect of building shape on total energy consumption in two different climate regions (i.e. colddry and warm-marine).…”

Section: Fig 1 Labor Productivity Through Methods Studymentioning

confidence: 99%

Multiple Linear Regression Approach for Strategic Decisions on Industrial Productivity under Limited Available Budget

Ojo

Oladapo

Ajayeoba

et al. 2020

AJPAS

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Proper planning and improved productivity is highly desired in industrial settings to optimize the available resources when there is limited resources and to control excessive spending in time of surplus. Productivity is achievable by good levels of Materials, Time and Labor inputs which needs to be measured scientifically in order to maintain long run profit. This study explored processing data and incorporated Statistical Package for Social Science (SPSS) to find the relationship and predict the response of the available budget with the inputs of Materials, Time and Labor using Olam Cocoa Processing Industry, Nigeria as case study. The analyses were done using Multiple Linear Regression Model developed (i.e. ), it was discovered that the inputs of the selected strategic decisions collectively affected the response of the available budget with F-value of 88.48 but each of them cannot reduce or increase the amount of budget except for manpower which has 0.069 or 93.1 % significant effect on the available budget. Also, Coefficient of determination established a strong fitness of the relationship between the strategic decisions and the available budget with the value of 0.974 (or 97.4 %). It is recommended that the project manager should subject his decisions making into scientific measures rather than brainstorming so as to increase productivity in the company.

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Application of multilinear regression analysis in modeling of soil properties for geotechnical civil engineering works in Calabar South

Cited by 23 publications

References 2 publications

Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest

Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest

Application of Feedforward Neural Network and SPT Results in the Estimation of Seismic Soil Liquefaction Triggering

Multiple Linear Regression Approach for Strategic Decisions on Industrial Productivity under Limited Available Budget

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