Advanced soft computing techniques for predicting soil compression coefficient in engineering project: a comparative study

Nhu, Viet-Ha; Samui, Pijush; Kumar, Deepak; Singh, Anshuman; Hoang, Nhat‐Duc; Bui, Dieu Tien

doi:10.1007/s00366-019-00772-7

Cited by 13 publications

(4 citation statements)

References 42 publications

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“…To evaluate the performance of the ABC-LM-ANN model, root mean square error (RMSE), the mean absolute percentage error (MAPE), the mean absolute error (MAE), and the coefficient of determination (R 2 ) are employed. The equations used to compute these indices are provided as follows [68]: (14) where Y A,i and Y P,i is the actual and the predicted values of soil CC of the ith data instance. N is the number of data instances in the set of interest.…”

Section: Resultsmentioning

confidence: 99%

A New Approach of Hybrid Bee Colony Optimized Neural Computing to Estimate the Soil Compression Coefficient for a Housing Construction Project

et al. 2019

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In the design phase of housing projects, predicting the settlement of soil layers beneath the buildings requires the estimation of the coefficient of soil compression. This study proposes a low-cost, fast, and reliable alternative for estimating this soil parameter utilizing a hybrid metaheuristic optimized neural network (NN). An integrated method of artificial bee colony (ABC) and the Levenberg–Marquardt (LM) algorithm is put forward to train the NN inference model. The model is capable of delivering the response variable of soil compression coefficient a set of physical properties of soil. A large-scale real-life urban project at Hai Phong city (Vietnam) was selected as a case study. Accordingly, a dataset of 441 samples with their corresponding testing values of the compression coefficient has been collected and prepared during the construction phase. Experimental outcomes confirm that the proposed NN model with the hybrid ABC-LM training algorithm has attained the highly accurate estimation of the soil compression coefficient with root mean square error (RMSE) = 0.008, mean absolute percentage error (MAPE) = 10.180%, and coefficient of determination (R2) = 0.864. Thus, the proposed machine learning method can be a promising tool for geotechnical engineers in the design phase of housing projects.

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Section: Resultsmentioning

confidence: 99%

A New Approach of Hybrid Bee Colony Optimized Neural Computing to Estimate the Soil Compression Coefficient for a Housing Construction Project

et al. 2019

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“…In this way, the principal components can be obtained through a diagonalization, specifically, of defined semipositive symmetric matrices (Ferreira, 2018). The use of this technique can be found in many studies with different applications, such as: (Bounoua & Bakdi, 2021;Mahmoudi et al, 2021;Nhu et al, 2020;Song & Li, 2021;Yu et al, 2020).…”

Section: Contextualizationmentioning

confidence: 99%

Principal Component Analysis: An Overview and Applications in Multivariate Engineering Problems

Almeida¹,

Gomes²,

Balestrassi³

et al. 2022

Uncertainty Modeling: Fundamental Concepts and Models

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“…We will use an efficient approach to RDO by combining surrogate modelling based on Gaussian Process Regression (GPR) and Polynomial Chaos Expansion (PCE) to develop the required statistical information around design points. GPR is an efficient probabilistic machine learning approach [43] that can learn the mean and probability density function of outputs and has been shown to provide effective statistical surrogates for many problems [20,11,14,27] and is therefore well-suited to RDO. We will alleviate the additional cost of multiple flow simulations, needed to generate statistical moments of the objectives, using generalised polynomial chaos expansion, which provides a rigorous approach to propagating the uncertainties in input design variables into the output variables of interest [44,13].…”

Section: Introductionmentioning

confidence: 99%

Robust design optimisation of continuous flow polymerase chain reaction thermal flow systems

Wang,

Hamad,

Voss

et al. 2021

Preprint

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This paper presents an efficient methodology for the robust optimisation of Continuous Flow Polymerase Chain Reaction (CFPCR) devices. It enables the effects of uncertainties in device geometry, due to manufacturing tolerances, on the competing objectives of minimising the temperature deviations within the CFPCR thermal zones, together with minimising the pressure drop across the device, to be explored. We first validate that our training data from conjugate heat transfer simulations of the CFPCR thermal flow problems is noise free and then combine a deterministic surrogate model, based on the mean of a Gaussian Process Regression (GPR) simulator, with Polynomial Chaos Expansions (PCE) to propagate the manufacturing uncertainties in the geometry design variables into the optimisation outputs. The resultant probabilistic model is used to solve a series of robust optimisation problems. The influence of the robust problem formulation and constraints on the design conservatism of the robust optima in comparison with the corresponding deterministic cases is explored briefly.

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Advanced soft computing techniques for predicting soil compression coefficient in engineering project: a comparative study

Cited by 13 publications

References 42 publications

A New Approach of Hybrid Bee Colony Optimized Neural Computing to Estimate the Soil Compression Coefficient for a Housing Construction Project

A New Approach of Hybrid Bee Colony Optimized Neural Computing to Estimate the Soil Compression Coefficient for a Housing Construction Project

Principal Component Analysis: An Overview and Applications in Multivariate Engineering Problems

Robust design optimisation of continuous flow polymerase chain reaction thermal flow systems

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