A hybrid computational intelligence approach for predicting soil shear strength for urban housing construction: a case study at Vinhomes Imperia project, Hai Phong city (Vietnam)

Nhu, Viet-Ha; Hoang, Nhat‐Duc; Duong, Binh Van; Vu, Hong-Dang; Bui, Dieu Tien

doi:10.1007/s00366-019-00718-z

Cited by 51 publications

(36 citation statements)

References 40 publications

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“…However, the LSSVM was more successful in understanding the relationship between the shear strength and input factors (R 2 = 0.922 > 0.90). Similarly, the PSO-SVM used by Nhu et al [6] predicted the shear strength less accurately (R 2 = 0.888) than both hybrid models of our study. More than 90% generalization accuracy of the WDO-ANN shows that it is a capable model to analyze and predict the shear strength for unseen soil conditions.…”

Section: Resultscontrasting

confidence: 62%

“…In the building sector, for example, having a reliable approximation of the shear strength helps foundation engineers to choose the type of foundation, as well as the necessity of subsoil improvement. It is also a very determinant factor for exploring soil problems like slope stability [4,5] and bearing capacity [6,7] in different conditions. Figure 1 illustrates an example of shear failures for strip footing subsoil and rainfall-induced landslide.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, it was proven that synthesizing these algorithms with typical predictive models led to raising their accuracy through prevailing computational weaknesses [31][32][33]. In the field of shear strength analysis, well-known optimization algorithms such as the cuckoo search optimization (CSO), particle swarm optimization (PSO), and genetic algorithm (GA) were applied for the performance improvement of intelligent approaches of least-squares SVM, support vector regression (SVR), and adaptive neuro-fuzzy inference system (ANFIS) [6,34,35]. The findings of these studies demonstrated the better functioning of the metaheuristically reinforced models compared to regular versions.…”

Section: Introductionmentioning

confidence: 99%

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Shuffled Frog Leaping Algorithm and Wind-Driven Optimization Technique Modified with Multilayer Perceptron

2020

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The prediction aptitude of an artificial neural network (ANN) is improved by incorporating two novel metaheuristic techniques, namely, the shuffled frog leaping algorithm (SFLA) and wind-driven optimization (WDO), for the purpose of soil shear strength (simply called shear strength) simulation. Soil information of the Trung Luong national expressway project (Vietnam) including depth of the sample (m), percentage of sand, percentage of silt, percentage of clay, percentage of moisture content, wet density (kg/m3), liquid limit (%), plastic limit (%), plastic index (%), liquidity index, and the shear strength (kPa) was collocated through a field survey. After constructing the hybrid ensembles of SFLA–ANN and WDO–ANN, both models were optimized in terms of complexity using a population-based trial-and error-scheme. The learning quality of the ANN was compared with both improved versions to examine the effect of the used metaheuristic techniques. In this phase, the training error dropped by 14.25% and 28.25% by applying the SFLA and WDO, respectively. This reflects a significant improvement in pattern recognition ability of the ANN. The results of the testing data revealed 25.57% and 39.25% decreases in generalization (i.e., testing) error. Moreover, the correlation between the measured and predicted shear strengths (i.e., the coefficient of determination) rose from 0.82 to 0.89 and 0.92, which indicates the efficiency of both SFLA and WDO metaheuristic techniques in optimizing the ANN.

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Section: Resultscontrasting

confidence: 62%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Shuffled Frog Leaping Algorithm and Wind-Driven Optimization Technique Modified with Multilayer Perceptron

2020

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“…The results showed that the proposed hybrid models outperformed benchmark models with outstanding predictive accuracy. Prediction of shear strength using ML approaches is also an interesting topic of many studies [7,8].Although advanced ML approaches are good compared with traditional approaches, these models are very sensitive to the selection of input parameters used in the modeling. Das et al [9] investigated the performance of two popular ML methods, namely SVM and ANN, for prediction of soil shear strength under the effects of different input properties and stated that the performance of SVM and ANN are good but very different under the effects of different input properties.…”

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confidence: 99%

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confidence: 99%

Extreme Learning Machine Based Prediction of Soil Shear Strength: A Sensitivity Analysis Using Monte Carlo Simulations and Feature Backward Elimination

Pham

Nguyen-Thoi

et al. 2020

Sustainability

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Machine Learning (ML) has been applied widely in solving a lot of real-world problems. However, this approach is very sensitive to the selection of input variables for modeling and simulation. In this study, the main objective is to analyze the sensitivity of an advanced ML method, namely the Extreme Learning Machine (ELM) algorithm under different feature selection scenarios for prediction of shear strength of soil. Feature backward elimination supported by Monte Carlo simulations was applied to evaluate the importance of factors used for the modeling. A database constructed from 538 samples collected from Long Phu 1 power plant project was used for analysis. Well-known statistical indicators, such as the correlation coefficient (R), root mean squared error (RMSE), and mean absolute error (MAE), were utilized to evaluate the performance of the ELM algorithm. In each elimination step, the majority vote based on six elimination indicators was selected to decide the variable to be excluded. A number of 30,000 simulations were conducted to find out the most relevant variables in predicting the shear strength of soil using ELM. The results show that the performance of ELM is good but very different under different combinations of input factors. The moisture content, liquid limit, and plastic limit were found as the most critical variables for the prediction of shear strength of soil using the ML model. Sustainability 2020, 12, 2339 2 of 29 the laboratory, including direct shear test, triaxial compression tests, or unconfined compression tests which might increase the cost and prolong the time of completing the projects. Moreover, the test accuracy depends significantly on the instruments, the meticulous procedures, and the expertise of the experimenters [1]. Therefore, the development of new advanced techniques for quick and accurate prediction of shear strength of soil is essential and practical.Traditionally, the shear strength of soil is often predicted by using traditional formula-based methods. Garven and Vanapalli [2] summarized and evaluated nineteen empirical techniques that are available for the prediction of the shear strength of unsaturated soils. Out of these, six techniques used tool of the soil-water retention curve (SWRC) and the remainder thirteen procedures are based on mathematical formulations. In these empirical techniques, various parameters of soil were used to correlate with the shear strength in unsaturated soils such as the texture of soil surface, pore size distribution, residual suction. In another study, Sheng et al. [3] proposed different empirical equations for the prediction of shear strength of unsaturated soils using different approaches, which are based on the independent stress, Bishop's stress, and constitutive models. Vanapalli and Fredlund [4] compared different empirical approaches for the prediction of shear strength of unsaturated soils. Various parameters used for forming the correlation equations such as particle gain distribution, liquid limit, plasticity indices, water con...

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A framework for estimating the matric suction in unsaturated soils using multiple artificial intelligence techniques

Wang,

Vanapalli

2024

Num Anal Meth Geomechanics

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Implementation of the state‐of‐the‐art understanding of the mechanics of unsaturated soils into geotechnical engineering practice is partly limited due to the lack of quick, reliable, and economical techniques for matric suction measurement. Matric suction is one of the key stress state variables that significantly influences the hydro‐mechanical behavior of unsaturated soils. In this paper, to address this objective, two artificial intelligence (AI) models were developed for estimating matric suction in unsaturated soils based on the particle swarm optimization support vector regression (PSO‐SVR) and multivariate adaptive regression spline (MARS) algorithms. The results suggest that both these models can reasonably estimate matric suction. Compared to the MARS model, the PSO‐SVR model can achieve higher accuracy. Nonetheless, the MARS model facilitates the sensitivity analysis and the selection of essential inputs. A novel integrated framework is proposed and validated, leveraging the strengths, and alleviating the limitations of the PSO‐SVR and MARS algorithms for reliable and rapid estimation of matric suction in the range of 0–1500 kPa for low plastic soils (0 < Ip ≤ 7). Six inputs are required to use this model successfully; some can be measured using conventional laboratory tests, and others can be calculated from mass‐volume relationships.

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A hybrid computational intelligence approach for predicting soil shear strength for urban housing construction: a case study at Vinhomes Imperia project, Hai Phong city (Vietnam)

Cited by 51 publications

References 40 publications

Shuffled Frog Leaping Algorithm and Wind-Driven Optimization Technique Modified with Multilayer Perceptron

Shuffled Frog Leaping Algorithm and Wind-Driven Optimization Technique Modified with Multilayer Perceptron

Extreme Learning Machine Based Prediction of Soil Shear Strength: A Sensitivity Analysis Using Monte Carlo Simulations and Feature Backward Elimination

A framework for estimating the matric suction in unsaturated soils using multiple artificial intelligence techniques

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