Statistics and Data Analysis in Geology

A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS

Learning incorporates a broad range of complex procedures. Machine learning (ML) is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine readable data. ML covers main domains such as data mining, difficult-to-program applications, and software applications. It is a collection of a variety of algorithms (e.g. neural networks, support vector machines, self-organizing map, decision trees, random forests, case-based reasoning, genetic programming, etc.) that can provide multivariate, nonlinear, nonparametric regression or classification. The modeling capabilities of the ML-based methods have resulted in their extensive applications in science and engineering. Herein, the role of ML as an effective approach for solving problems in geosciences and remote sensing will be highlighted. The unique features of some of the ML techniques will be outlined with a specific attention to genetic programming paradigm. Furthermore, nonparametric regression and classification illustrative examples are presented to demonstrate the efficiency of ML for tackling the geosciences and remote sensing problems.

https://davidlary.info/wp-content/uploads/2015/09/1-s2.0-S1674987115000821-main.pdf

Machine learning in geosciences and remote sensing

Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS

Computer Aided Civil and Infrastructure Engineering

Resilient modulus (Mr) of subgrade soils is one of the crucial inputs in pavement structural design methods. However, the spatial variability of soil properties and the nature of test protocols, the laboratory determination of Mr has become inexpedient. This paper aims to design an accurate soft computing technique for the prediction of Mr of subgrade soils using the hybrid least square support vector machine (LSSVM) approaches. Six swarm intelligence algorithms, namely particle swarm optimization (PSO), grey wolf optimizer (GWO), symbiotic organisms search (SOS), salp swarm algorithm (SSA), slime mould algorithm (SMA), and Harris hawks optimization (HHO) have been applied and compared to optimize the LSSVM parameters. For this purpose, a literature dataset (891 datasets) of different types of soils has been used to design and evaluate the proposed models. The input variables in all of the proposed models included confining stress, deviator stress, unconfined compressive strength, degree of soil saturation, soil moisture content, optimum moisture content, plasticity index, liquid limit, and percent of soil particles (P #200). The accuracy of the proposed models was assessed by comparing the predicted with the observed of Mr values with respect to different statistical analyses, i.e., root means square error (RMSE) and determination coefficient (R2). For modeling the Mr of subgrade soils, percent passing No. 200 sieve, optimum moisture content, and unconfined compressive strength were found to be the most significant variables. It is observed that the performance of LSSVM-GWO, LSSVM-SOS, and LSSVM-SSA outperforms other models in predicting accurate values of Mr. The (RMSE and R2) of the LSSVM-GWO, LSSVM-SSA, and LSSVM-SOS are (6.79 MPa and 0.940), (6.78 MPa and 0.940), and (6.72 MPa and 0.942), respectively, and hence, LSSVM-SOS can be used for high estimating accuracy of Mr of subgrade soils. 

/pdf/modeling-resilient-modulus-of-subgrade-soils-using-lssvm-1wcao0xa.pdf

Modeling resilient modulus of subgrade soils using LSSVM optimized with swarm intelligence algorithms

The present study focused on the design of geothermal energy piles based on cone penetration test (CPT) data, which was obtained from the Perniö test site in Finland. The geothermal piles are heat-capacity systems that provide both a supply of energy and structural support to civil engineering structures. In geotechnical engineering, it is necessary to provide an efficient, reliable, and precise method for calculating the group capacity of the energy piles. In this research, the first aim is to determine the most significant variables required to calculate the energy pile capacity, i.e., the pile length (L), pile diameter (D), average cone resistance (qc0), minimum cone resistance (qc1), average of minimum cone resistance (qc2), cone resistance (qc), Young’s modulus (E), coefficient of thermal expansion (αc), and temperature change (ΔT). The values of qc0, qc1, qc2, qc, and E are then employed as model inputs in soft computing algorithms, which includes random forest (RF), the support vector machine (SVM), the gradient boosting machine (GBM), and extreme gradient boosting (XGB) in order to predict the pile group capacity. The developed soft computing models were then evaluated by using several statistical criteria, and the lowest system error with the best performance was attained by the GBM technique. The performance parameters, such as the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean biased error (MBE), median absolute deviation (MAD), weighted mean absolute percentage error (WMAPE), expanded uncertainty (U95), global performance indicator (GPI), Theil’s inequality index (TIC), and the index of agreement (IA) values of the testing data for the GBM models are 0.80, 0.10, 0.08, −0.01, 0.06, 0.21, 0.28, −0.00, 0.11, and 0.94, respectively, demonstrating the strength and capacity of this soft computing algorithm in evaluating the pile’s group capacity for the energy pile. Rank analysis, error matrix, Taylor’s diagram, and the reliability index have all been developed to compare the proposed model’s accuracy. The results of this research also show that the GBM model developed is better at estimating the group capacity of energy piles than the other soft computing models.

/pdf/design-of-an-energy-pile-based-on-cpt-data-using-soft-2oqvf62p.pdf

Design of an Energy Pile Based on CPT Data Using Soft Computing Techniques

Stress Intensity Factor Prediction on Offshore Pipelines using Surrogate Modeling Techniques

Reliability Analysis of Pile Foundation Using GMDH, GP and MARS

The compressive strength is an important mechanical feature of concrete that is needed in construction design. Thus, a lot of investigations were carried out to predict the compressive strength of various concretes. However, the prediction models for the compressive strength of cement mortar or paste that include magnetic water (MW) and granulated blast-furnace slag (GBFS) are still limited. The current study has developed hybrid algorithms based on adaptive neuro-fuzzy inference system (ANFIS) for modeling the compressive strength of cement mortar and paste that made with MW and GBFS as a novel mixture content. A total of 144 experimental sets of concrete-compressive strength tests for each cement mortar and paste were collected to train and validate the proposed methods, in which the cycles number of water magnetization, cement, GBFS, superplasticizer contents and curing time are set as the input data while the compressive strength value is set as the output. The developed hybrid algorithms of ANFIS optimized by firefly algorithm (FA), Improved Particle Swarm Optimization (IPSO) and biogeographybased optimization (BBO) algorithms for predicting the compressive strength of the mortar and paste. The proposed models and relevance vector machine (RVM) approach were evaluated and compared. The results showed that the ANFIS-FA outperforms other models for modeling the compressive strength of cement mortar and paste. The adjusted-coefficient of determination and root mean square error values of cement mortar models (96.20%, 92.33%, 92.36% and 89.41%) and (2.17 MPa, 3.10 MPa, 3.18 MPa and 3.06 MPa) and of cement paste models (96.92%, 80.91%, 92.19% and 88.18%) and (2.45 MPa, 5.80 MPa, 4.39 MPa and 5.20 MPa) were determined for ANFIS-FA, ANFIS-IPSO, ANFIS-BBO and RVM models, respectively, which indicate that the ANFIS-FA is a suitable model for estimating the compressive strength of cement mortar and paste that include MW. Moreover, the sensitivity of MW and GBFS is shown high for modeling the compressive strength of cement mortar.

Pijush Samui

Papers

Modeling resilient modulus of subgrade soils using LSSVM optimized with swarm intelligence algorithms

Design of an Energy Pile Based on CPT Data Using Soft Computing Techniques

Stress Intensity Factor Prediction on Offshore Pipelines using Surrogate Modeling Techniques

Reliability Analysis of Pile Foundation Using GMDH, GP and MARS

Hybrid-ANFIS approaches for compressive strength prediction of cementitious mortar and paste employing magnetic water