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.

Automated summary

This study investigated different cracking prediction models and performed sensitivity analysis to evaluate the uncertainties of the models and parameters. The findings suggest that the linear elastoplastic model provides reasonable predictions, while soil parameter variations play an important role. Furthermore, the findings of this study can improve the decision-making processes for expansive soil stabilization by considering a variety of environmental conditional probabilities.