Determination of Compressive Strength of Concrete by Statistical Learning Algorithms

This article adopts three statistical learning algorithms: support vector machine (SVM), lease square support vector machine (LSSVM), and relevance vector machine (RVM), for predicting compressive strength (fc) of concrete. Fly ash replacement ratio (FA), silica fume replacement ratio (SF), total cementitious material (TCM), fine aggregate (ssa), coarse aggregate (ca), water content (W), high rate water reducing agent (HRWRA), and age of samples (AS) are used as input parameters of SVM, LSSVM and RVM. The output of SVM, LSSVM and RVM is fc. This article gives equations for prediction of fc of concrete. A comparative study has been carried out between the developed SVM, LSSVM, RVM and Artificial Neural Network (ANN). This article shows that the developed SVM, LSSVM and RVM models are practical tools for the prediction of fc of concrete

Vector machine techniques for modeling of seismic liquefaction data

AbstractThis article employs three soft computing techniques, Support Vector Machine (SVM); Least Square Support Vector Machine (LSSVM) and Relevance Vector Machine (RVM), for prediction of liquefaction susceptibility of soil. SVM and LSSVM are based on the structural risk minimization (SRM) principle which seeks to minimize an upper bound of the generalization error consisting of the sum of the training error and a confidence interval. RVM is a sparse Bayesian kernel machine. SVM, LSSVM and RVM have been used as classification tools. The developed SVM, LSSVM and RVM give equations for prediction of liquefaction susceptibility of soil. A comparative study has been carried out between the developed SVM, LSSVM and RVM models. The results from this article indicate that the developed SVM gives the best performance for prediction of liquefaction susceptibility of soil

Determination of Strain Energy for Triggering Liquefaction Based on Gaussian Process Regression

The determination of seismic liquefaction potential of soil is an imperative task in earthquake engineering. This article adopts Gaussian Process Regression (GPR) for determination of amount of strain energy required to induce liquefaction. Effective mean confining pressure (σ' mean), initial relative density after consolidation (Dr), percentage of fines content (FC), coefficient of uniformity (Cu), and mean grain size (D50) are considered as input of the GPR model. The developed GPR gives the variance of predicted output. The results of GPR have been compared with the Artificial Neural Network (ANN). The results of this article show the suitability of the proposed approach for determination of stain energy for triggering liquefaction

Determination of Electrical Resistivity of Soil Based on Thermal Resistivity Using RVM and MPMR

This article adopts Relevance Vector Machine (RVM) and Minimax Probability Machine Regression (MPMR) for prediction Soil Electrical Resistivity(RE) of soil. RVM uses an improper hierarchical prior. It optimizes over hyperparameters. MPMR is a probabilistic model. Two models (MODEL I and MODEL II) have been adopted. Percentage sum of the gravel and sand size fractions (F) and Soil Thermal Resistivity(RT) has been takes as inputs in MODEL I. MODEL II uses F,RT and saturation of soils(S) as input variables. The results of RVM and MPMR have  been compared with the Artificial Neural Network (ANN). The developed RVM and MPMR proves his ability for prediction of RE of soil

A Multivariate Adaptive Regression Spline Approach for Prediction of Maximum Shear Modulus and Minimum Damping Ratio

This study uses multivariate adaptive regression spline (MARS) for determination of maximum shear modulus (Gmax) and minimum damping ratio (ξmin) of synthetic reinforced soil. MARS employs confining pressure (σ, psi), rubber (r, %) and sand (s, %) as input variables. The outputs of the MARS are Gmax and ξmin. The developed MARS gives equations for determination of Gmax and ξmin. The results of MARS have been compared with the adaptive neuro-fuzzy inference system (ANFIS), multi-layer perception (MLP) and multiple regression analysis method (MRM). A sensitivity analysis has been also carried out to determine the effect of each input variable on Gmax and ξmin. This study shows that the developed MARS is a robust model for prediction of Gmax and ξmin

Operational use of machine learning models for sea-level modeling

1427-1434Intense activity offshore warrants a temporal and accurate prediction of sea-level variability. Besides, the sea-level plays an important role in the groundwater level and quality of coastal aquifer. Climate change influences considerable change in all the hydrological parameters and apparently affects sea-level variability. For prediction, highly complex numerical models are usually generated. To address these challenges, the study proposes the use of machine learning (ML) models with the climate change predictands and sea-level predictors. Three ML models are employed in this study, viz., Regression Vector Machine (RVM), Extreme Learning Machine (ELM), and Gaussian Process Regression (GPR). The performance of the developed models is evaluated by visual comparison of predicted and observed datasets. Regression error curve plots, frequency of forecasting errors and Taylor diagram, along with statistical performance metrics were developed. Overall, it is found that the operational use of the selected ML algorithms was quite appealing for modeling studies. Among the three ML models, GPR performed slightly better than ELM and RVM

Estimating Concrete Compressive Strength Using MARS, LSSVM and GP

The estimation of concrete compressive strength is utmost important for the construction of a building. Organizations have a limited budget for mix design; therefore, proper estimation of concrete data has a significant impact on site operations and the construction of the building. In this paper, the prediction of concrete compressive strength is done by Multivariate Adaptive Regression Spline (MARS), Least Squares Support Vector Machine (LSSVM) and genetic programming (GP) which is a very new approach in the field of concrete technology.  MARS is a supervised technique, performs well for high dimensional data, interacts less with the input variables, whereas LSSVM is generally based on a statistical learning algorithm and GP builds equations that are generated for modeling. All the developed LSSVM, MARS and GP gives an equations for prediction of compressive strength which makes easy to predict the compressive strength of the concrete. The efficiency of the MARS, LSSVM and GP are measured by the comparative study of the statistical parameters and can be concluded that the all the models performed very well as the output results are very close to the desired value, while the MARS slightly outperformed the other two models

Performance of traditional and machine learning-based transformation models for undrained shear strength

In geotechnical engineering, transformation models are often used as first estimates of parameters and to verify the order of magnitude of field and laboratory tests, which reliability might be constrained by many uncertainties. The undrained shear strength has been for long of particular interest for such models. The traditional transformation models for undrained shear strength are often rather simple. Still, the geotechnical community does not seem to have agreed upon which models to use. In particular, the question of including index properties to the models seems to be open. In the paper, the performance of traditional transformation models is compared to that of machine learning (ML)-based models. In addition, the influence of data coherence is studied by using two datasets of different quality. The ML-based transformation models proved to perform better than traditional ones for both datasets. Clearly, most dominant variables in the transformation model are the preconsolidation pressure and the effective vertical stress. Although including additional variable often may well improve the performance of the training set, the prediction of the testing sets generally tends to worsen, indicating overtraining. The risks for overtraining increase with incoherent data.Peer reviewe

SITE CHARACTERIZATION OF (IGCAR) KALPAKKAM USING SOFT COMPUTING TECHNICES

Abstract This study employ