Early warning systems (EWS) are monitoring devices designed to avoid or to mitigate the impact posed by a threat. Since EWS are time-sensitive or stochastic, it is necessary to develop a design methodology that defines the integration of the participating monitoring information sources, the identification of potential warning thresholds, and the assessment of the associated risk within an explicit causal analysis. This paper proposes a framework for a stochastic design of an early warning system, introducing a risk measure as the reference variable that encapsulates the different effects retrieved by the monitoring instruments. Within a decision-making framework the risk measure serves as the index for defining the system warning thresholds. A Bayesian approach is proposed as a suitable tool for integrating and updating the joint states of information and the warning levels as the information flows through the warning system.

Predicting the range of achievable strength and stiffness from stabilized soil mixtures is critical for engineering design and construction, especially for organic soils, which are often considered “unsuitable” due to their high compressibility and the lack of knowledge about their mechanical behavior after stabilization. This study investigates the mechanical behavior of stabilized organic soils using machine learning (ML) methods. ML algorithms were developed and trained using a database from a comprehensive experimental study (see Part I), including more than one thousand unconfined compression tests on organic clay samples stabilized by wet soil mixing (WSM) technique. Three different ML methods were adopted and compared, including two artificial neural networks (ANN) and a linear regression method. ANN models proved reliable in the prediction of the stiffness and strength of stabilized organic soils, significantly outperforming linear regression models. Binder type, mixing ratio, soil organic and water content, sample size, aging, temperature, relative humidity, and carbonation were the control variables (input parameters) incorporated into the ML models. The impacts of these factors were evaluated through rigorous ANN-based parametric analyses. Additionally, the nonlinear relations of stiffness and strength with these parameters were developed, and their optimum ranges were identified through the ANN models. Overall, the robust ML approach presented in this paper can significantly improve the mixture design for organic soil stabilization and minimize the experimental cost for implementing WSM in engineering projects.

The use of Digital Image Correlation (DIC) technique has become increasingly popular for displacement measurements and for characterizing localized material deformation. In this study, a three-dimensional digital image correlation (3D DIC) analysis was performed to investigate the displacements on the surface of a dense sand specimen during a triaxial compression test (drained, vacuum consolidated). The deformation of a representative volume of the material captured by 3D DIC is used for the estimation of the kinematic and volumetric conditions of the specimen at different stages of deformation, combined with the readings of the global axial compression of the specimen, which allow for the characterization of the Mohr-Coulomb plasticity model with hardening and softening law. A 2D finite element model is developed for comparing the experimental results under displacement controlled loading conditions. A comparison between model predictions and the expected displacement fields show good agreement, as to reproduce accurately the overall mechanical behavior of a dense sand specimen and to take into account the influence of local effects in the mechanical parameters obtained by 3D DIC.