Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition

Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN

Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks

Faulty structural design may cause multistory reinforced concrete (RC) buildings to collapse suddenly. All attempts are directed to avoid structural failure as it leads to human life danger as well as wasting time and property. Using traditional methods for predicting structural failure of the RC buildings will be time-consuming and complex. Recent research proved the artificial neural network (ANN) potentiality in solving various real-life problems. The traditional learning algorithms suffer from being trapped into local optima with a premature convergence. Thus, it is a challenging task to achieve expected accuracy while using traditional learning algorithms to train ANN. To solve this problem, the present work proposed a particle swarm optimization-based approach to train the NN (NN-PSO). The PSO is employed to find a weight vector with minimum root-mean-square error (RMSE) for the NN. The proposed (NN-PSO) classifier is capable to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistoried RC building structure in the future. A database of 150 multistoried buildings’ RC structures was employed in the experimental results. The PSO algorithm was involved to select the optimal weights for the NN classifier. Fifteen features have been extracted from the structural design, while nine features have been opted to perform the classification process. Moreover, the NN-PSO model was compared with NN and MLP-FFN (multilayer perceptron feed-forward network) classifier to find its ingenuity. The experimental results established the superiority of the proposed NN-PSO compared to the NN and MLP-FFN classifiers. The NN-PSO achieved 90 % accuracy with 90 % precision, 94.74 % recall and 92.31 % F-Measure.

Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings

One of the main concerns in geotechnical engineering is slope stability prediction during the earthquake. In this study, two intelligent systems namely artificial neural network (ANN) and particle swarm optimization (PSO)---ANN models were developed to predict factor of safety (FOS) of homogeneous slopes. Geostudio program based on limit equilibrium method was utilized to obtain 699 FOS values with different conditions. The most influential factors on FOS such as slope height, gradient, cohesion, friction angle and peak ground acceleration were considered as model inputs in the present study. A series of sensitivity analyses were performed in modeling procedures of both intelligent systems. All 699 datasets were randomly selected to 5 different datasets based on training and testing. Considering some model performance indices, i.e., root mean square error, coefficient of determination (R2) and value account for (VAF) and using simple ranking method, the best ANN and PSO---ANN models were selected. It was found that the PSO---ANN technique can predict FOS with higher performance capacities compared to ANN. R2 values of testing datasets equal to 0.915 and 0.986 for ANN and PSO---ANN techniques, respectively, suggest the superiority of the PSO---ANN technique.

Prediction of seismic slope stability through combination of particle swarm optimization and neural network

Axial bearing capacity (ABC) of piles is usually determined by static load test (SLT). However, conducting SLT is costly and time-consuming. High strain dynamic pile testing (HSDPT) which is provided by pile driving analyzer (PDA) is a more recent approach for predicting the ABC of piles. In comparison to SLT, PDA test is quick and economical. Implementing feed forward back-propagation artificial neural network (ANN) for solving geotechnical problems has recently gained attention mainly due to its ability in finding complex nonlinear relationships among different parameters. In this study, an ANN-based predictive model for estimating ABC of piles and its distribution is proposed. For network construction purpose, 36 PDA tests were performed on various concrete piles in different project sites. The PDA results, pile geometrical characteristics as well as soil investigation data were used for training the ANN models. Findings indicate the feasibility of ANN in predicting ultimate, shaft and tip bearing resistances of piles. The coefficients of determination, R², equal to 0.941, 0.936, and 0.951 for testing data reveal that the shaft, tip and ultimate bearing capacities of piles predicted by ANN-based model are in close agreement with those of HSDPT. By using sensitivity analysis, it was found that the length and area of the piles are dominant factors in the proposed predictive model.

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Application of artificial neural network for predicting shaft and tip resistances of concrete piles

A commonly-encountered problem in foundation design is the reliable prediction of the pile bearing capacity (PBC). This study is planned to propose a feasible soft computing technique in this field i.e.; the Gaussian process regression (GPR) for the PBC estimation. The established database includes 296 number of dynamic pile load test in the field where the most influential factors on the PBC were selected as input variables. Several GPR models were designed and built. These models were assessed using three performance indices namely value account for (VAF), coefficient of determination (R2) and system error. To have a comparison, a genetic algorithm-based artificial neural network (GA-based ANN) model was also employed. It was found that the GPR-based model with VAF value of 86.41%, R2 of 0.84 and system error of 0.2006 is capable enough to predict the PBC and outperforms the GA-based ANN model. The results showed that the GPR can be utilized as a practical tool for the PBC estimation.

Gaussian Process Regression Technique to Estimate the Pile Bearing Capacity

The pile bearing capacity is considered as the most essential factor in designing deep foundations. Direct determination of this parameter in site is costly and difficult. Hence, this study presents a new technique of intelligence system based on the adaptive neuro-fuzzy inference system (ANFIS)-group method of data handling (GMDH) optimized by the imperialism competitive algorithm (ICA), ANFIS-GMDH-ICA for forecasting pile bearing capacity. In this advanced structure, the ICA role is to optimize the membership functions obtained by ANFIS-GMDH technique for receiving a higher accuracy level and lower error. To develop this model, the results of 257 high strain dynamic load tests (performed by authors) were considered and used in the analysis. For comparison purposes, ANFIS and GMDH models were selected and built for pile bearing capacity estimation. In terms of model accuracy, the obtained results showed that the newly developed model (i.e., ANFIS-GMDH-ICA) receives more accurate predicted values of pile bearing capacity compared to those obtained by ANFIS and GMDH predictive models. The proposed ANFIS-GMDH-ICA can be utilized as an advanced, applicable and powerful technique in issues related to foundation engineering and its design.

An optimized system of GMDH-ANFIS predictive model by ICA for estimating pile bearing capacity

Estimation of axial bearing capacity plays an essential role in pile design. A part from semi-empirical and numerical methods, axial bearing capacity of piles can be either predicted by means of a maintain load test or dynamic load test. The latter test is based on wave equation analysis and it is provided by Pile driving analyzer (PDA). Combination of wave equation analysis with dynamic monitoring of the pile can result in prediction of axial bearing capacity of the pile and its distribution. This paper compares the axial capacity of pile obtained from PDA records and maintain load test (static load test) with predicted axial capacities obtained using analytical, empirical and finite element analysis. From the results it is observed that axial bearing capacity derived from numerical modelling with the aid of the finite element code, Plaxis, is in a good agreement with estimated axial capacity through analytical-empirical methods, PDA, and maintain load test.

Harnedi Maizir

Papers

Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN

Application of artificial neural network for predicting shaft and tip resistances of concrete piles

Gaussian Process Regression Technique to Estimate the Pile Bearing Capacity

An optimized system of GMDH-ANFIS predictive model by ICA for estimating pile bearing capacity

Comparative Study on Prediction of Axial Bearing Capacity of Driven Piles in Granular Materials