Predictive modeling of swell-strength of expansive soils using artificial intelligence approaches: ANN, ANFIS and GEP.

This study presents the development of new empirical prediction models to evaluate swell pressure and unconfined compression strength of expansive soils (PsUCS-ES) using three soft computing methods, namely artificial neural networks (ANNs), adaptive neuro fuzzy inference system (ANFIS), and gene expression programming (GEP). An extensive database comprising 168 Ps and 145 UCS records was established after a comprehensive literature search. The nine most influential and easily determined geotechnical parameters were taken as the predictor variables. The network was trained and tested, and the predictions of the proposed models were compared with the observed results. The performance of all the models was tested using mean absolute error (MAE), root squared error (RSE), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), regression coefficient (R2) and relative root mean square error (RRMSE). The sensitivity analysis indicated that the increasing order of inputs importance in case of Ps followed the order: maximum dry density MDD (30.5%) > optimum moisture content OMC (28.7%) > swell percent SP (28.1%) > clay fraction CF (9.4%) > plasticity index PI (3.2%) > specific gravity Gs (0.1%), whereas, in case of UCS it followed the order: sand (44%) > PI (26.3%) > MDD (16.8%) > silt (6.8%) > CF (3%) > SP (2.9%) > Gs (0.2%) > OMC (0.03%). Parametric analysis was also performed and the resulting trends were found to be in line with findings of past literature. The comparison results reflected that GEP and ANN are efficacious and reliable techniques for estimation of PsUCS-ES. The derived mathematical GP-based equations portray the novelty of GEP model and are comparatively simple and reliable. The Roverall values for PsUCS-ES followed the order: ANN > GEP > ANFIS, with all values lying above the acceptable range of 0.80. Hence, all the proposed AI approaches exhibit superior performance, possess high generalization and prediction capability, and evaluate the relative importance of the input parameters in predicting the PsUCS-ES. The GEP model outperformed the other two models in terms of closeness of training, validation and testing data set with the ideal fit (1:1) slope. Evidently the findings of this study can help researchers, designers and practitioners to readily evaluate the swell-strength characteristics of the widespread expansive soils thus curtailing their environmental vulnerabilities which leads to faster, safer and sustainable construction from the standpoint of environment friendly waste management.

Cracking propagation in expansive soils under desiccation and stabilization planning using Bayesian inference and Markov decision chains.

Desiccation cracking endangers the stability of expansive soils subjected to cyclic moisture variations. In the current research, prominent cracking prediction models including linear, linear elastic, linear elastoplastic, and linear elastic fracture were studied. Then, Monte Carlo limit state functions were generated based on predictions. Results indicate that there is less than 5% chance of cracking for depths beyond 0.5, 6, 8, and 9 m as predicted by the linear elastoplastic, linear elastic, linear, and linear elastic fracture models, respectively. Moreover, a series of sensitivity analysis was performed to evaluate model and parameter uncertainties. Comparatively, it was found that the linear model exhibits the highest uncertainty while linear elastoplastic model possesses the least uncertainty thus yielding a reasonable prediction. Additionally, soil parameters including matric suction followed by dry density were identified to govern the overall cracking. Using Bayesian inference, numerous conditional probabilities of variation of soil properties were investigated. Then, several cracking probabilities under history of low to high matric suction and dry density were obtained. Accordingly, Monte Carlo Markov decision chains were established based on several ecofriendly and feasible stabilization policies and their performance was also evaluated. The obtained safety factors (SF) suggest that stabilization plans resulting in high moisture and dry density have the least likelihood of cracking with a SF equal to 5.1. However, stabilization policies having low dry density and moisture yield have the least SF of 0.39. Findings of this study can improve the decision-making processes for expansive soil stabilization by considering a variety of environmental conditional probabilities.

Fractal approach in expansive clay-based materials with special focus on compacted GMZ bentonite in nuclear waste disposal: a systematic review.

Knowledge of the behavior of highly compacted expansive clays, as an engineered barrier, in disposal of high-level nuclear waste (HLW) systems to prevent the pollution due to migration of radionuclide is extremely essential. The prominent properties of globally and widely used bentonites have been extensively studied during past two decades. In China, GaoMiaoZi (GMZ) bentonite is the first choice as a buffer or backfill material for deep geological repositories. This review article presents the recent progresses of knowledge on water retention properties, hydromechanical behavior, and fractal characteristics of GMZ bentonite-based materials, by reviewing 217 internationally published research articles. Firstly, the current literature regarding hydrogeochemical and mechanical characteristics of GMZ bentonite influenced by various saline solutions are critically summarized and reviewed. Then, the role of osmotic suction π alongside the application of surface fractal dimension Ds is presented from the standpoint of fractal theory. Finally, the strength characteristics of GMZ bentonites using fractal approach have been discussed. Furthermore, this study sheds light on gaps, opportunities, and further research for understanding and analyzing the long-term hydromechanical characteristics of the designed backfill material, from the standpoint of surface fractality of bentonites, and implications of sustainable buffer materials in the field of geoenvironmental engineering.