Prediction of tunneling-induced ground movement with the multi-layer perceptron

Neaupane, K.M.; Adhikari, Nav Raj

doi:10.1016/j.tust.2005.07.001

Cited by 125 publications

(43 citation statements)

References 12 publications

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“…Feed-Forward Backpropagation, FFBP, is the most applied algorithm used to train the neural networks for a broad range of engineering applications [26]. In this training algorithm, firstly and in forward pass, assuming a primary value for connection between neurons, outputs are forecasted and then the computational error is calculated.…”

Section: Neural Network Trainingmentioning

confidence: 99%

“…Hence, the application of neural network modeling and evolutionary polynomial regressions (EPRs), which are believed to be common ways to accurately and timely predict engineering complicated functions, can be examined. Different attempts to apply neural networks and EPRs to model different civil and geotechnical problems are presented in the literature [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. They are well-applied in a wide range of problems from deep soil stabilizations, concrete, and their related structures, compressive strength of soils, rocks, and stabilized samples, bearing capacity of shallow and deep foundations, lateral spreading, rock mechanics, rock engineering, and soil mechanics [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Induced Settlements of Piled Rafts in the Coupled Static-Dynamic Loads Using Neural Networks and Evolutionary Polynomial Regression

Ghorbani

Niavol

2017

Applied Computational Intelligence and Soft Computing

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Coupled Piled Raft Foundations (CPRFs) are broadly applied to share heavy loads of superstructures between piles and rafts and reduce total and differential settlements. Settlements induced by static/coupled static-dynamic loads are one of the main concerns of engineers in designing CPRFs. Evaluation of induced settlements of CPRFs has been commonly carried out using threedimensional finite element/finite difference modeling or through expensive real-scale/prototype model tests. Since the analyses, especially in the case of coupled static-dynamic loads, are not simply conducted, this paper presents two practical methods to gain the values of settlement. First, different nonlinear finite difference models under different static and coupled static-dynamic loads are developed to calculate exerted settlements. Analyses are performed with respect to different axial loads and pile's configurations, numbers, lengths, diameters, and spacing for both loading cases. Based on the results of well-validated three-dimensional finite difference modeling, artificial neural networks and evolutionary polynomial regressions are then applied and introduced as capable methods to accurately present both static and coupled static-dynamic settlements. Also, using a sensitivity analysis based on Cosine Amplitude Method, axial load is introduced as the most influential parameter, while the ratio l/d is reported as the least effective parameter on the settlements of CPRFs.

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Section: Neural Network Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Induced Settlements of Piled Rafts in the Coupled Static-Dynamic Loads Using Neural Networks and Evolutionary Polynomial Regression

Ghorbani

Niavol

2017

Applied Computational Intelligence and Soft Computing

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“…This technique has caught the interest of most researchers and has today become an essential part of the technology industry, providing a good ground for solving many of the most difficult prediction problems in various areas of engineering applications (Baughman 1995;Guler 2005;Inan et al 2006;Li and Jiao 2002;Moghadassi et al 2009;Mohaghegh 1995;Nascimento et al 2000;Phung and Bouzerdoum 2007;Ü beyli 2009). ANN has also gained vast popularity in solving various Civil Engineering problems (Baughman 1995;Beale and Demuth 2013;Chen et al 1995;Flood and Kartam 1994;Hasancebi and Dumlupınar 2013;Kang and Yoon 1994;Kirkegaard and Rytter 1994;Neaupane and Adhikari 2006;Pandey and Barai 1995;Rafiq et al 2001). Azad et al (2010) proposed the following two-step procedure to predict the residual flexural strength of corroded beams for which the cross-sectional details, material strengths, corrosion activity index I corr T , and diameter of rebar, D were known.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Residual Strength of Corroded Reinforced Concrete Beams Using an Adaptive Model Based on ANN

Imam

Anifowose

Azad

2015

International Journal of Concrete Structures and Materials

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Estimation of the residual strength of corroded reinforced concrete beams has been studied from experimental and theoretical perspectives. The former is arduous as it involves casting beams of various sizes, which are then subjected to various degrees of corrosion damage. The latter are static; hence cannot be generalized as new coefficients need to be re-generated for new cases. This calls for dynamic models that are adaptive to new cases and offer efficient generalization capability. Computational intelligence techniques have been applied in Construction Engineering modeling problems. However, these techniques have not been adequately applied to the problem addressed in this paper. This study extends the empirical model proposed by Azad et al. (Mag Concr Res 62(6):405-414, 2010), which considered all the adverse effects of corrosion on steel. We proposed four artificial neural networks (ANN) models to predict the residual flexural strength of corroded RC beams using the same data from Azad et al. (2010). We employed two modes of prediction: through the correction factor (C f ) and through the residual strength (M res ). For each mode, we studied the effect of fixed and random data stratification on the performance of the models. The results of the ANN models were found to be in good agreement with experimental values. When compared with the results of Azad et al. (2010), the ANN model with randomized data stratification gave a C f -based prediction with up to 49 % improvement in correlation coefficient and 92 % error reduction. This confirms the reliability of ANN over the empirical models.

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“…The feed forward backpropagation neural network (BPNN) always consists of at least three layers; input layer, hidden layer and output layer (Neaupane and Adhikari, 2006). Each layer consists of a number of elementary processing units, called neurons, which are connected to the next layer through weights, i.e.…”

Section: Ann Architecture In Blasting Problemsmentioning

confidence: 99%