Jet grouting (JG) is a soil treatment technique which is the best solution for several soil improvement problems. However, JG lacks design rules and quality controls. As a result, the main JG works are planned from empirical rules that are too conservative. The development of rational models to simulate the effects of the different parameters involved in the JG process is of primary importance in order to satisfy the binomial safety-economy that is required in any engineering project. In this paper, we present a new approach to predict the uniaxial compressive strength (UCS) of JG materials based on data mining techniques. This model was developed and verified using data from a JG laboratory formulation that involves the measurement of UCS. The results of the proposed approach are compared with the EC2 analytical model adapted to the JG material, and the advantages of the new approach are highlighted. We show that the novel data-driven model is able to learn (with high accuracy) the complex relationships between the UCS of JG material and its contributing factors.
Keeping large-scale transportation infrastructure networks, such as railway networks, operational under all conditions is one of the major challenges today. The budgetary constraints for maintenance purposes and the network dimension are two of the main factors that make the management of a transportation network such a challenging task. Accordingly, aiming to assist the management of a transportation network, a data-driven model is proposed for stability condition prediction of embankment slopes. For such purpose, the highly flexible learning capabilities of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) were used to fit data-driven models for Earthwork Hazard Category (EHC) prediction. Moreover, the data-driven models were created using visual information that is easy to collect during routine inspections. The proposed models were addressed following two different data modeling strategies: nominal classification and regression. Moreover, to overcome the problem of imbalanced data (since typically good conditions are much common than bad ones), three training sampling approaches were explored: no resampling, SMOTE and Oversampling. The achieved modeling results are presented and discussed, comparing the predictive performance of ANN and SVM algorithms, as well as the effect of the sampling approaches. A comparison between nominal classification and regression strategies was also carried out. Moreover, aiming a better understanding of the proposed data-driven 1 Tinoco et al., May 29, 2018 models, a detailed sensitivity analysis was applied, allowing to quantify the relative importance of each model input, as well as measuring their global effect on the prediction of embankments stability conditions.
For transportation infrastructure, one of the greatest challenges today is to keep large-scale transportation networks, such as railway networks, operational under all conditions. This task becomes even more difficult to accomplish if taken into account budget limitations for maintenance and repair works. In this paper, it is presented a tool aimed at helping in management tasks related to maintenance and repair works for a particular element of this infrastructure, the slopes. The highly flexible learning capabilities of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) were applied in the development of a tool able to identify the stability condition of rock and soil cutting slopes, keeping in mind the use of information usually collected during routine inspection activities (visual information) to feed the models. This task was addressed following two different strategies: nominal classification and regression. Moreover, to overcome the problem 1 Tinoco et al.
The reinforcement of stabilized soils with fibers arises as an interesting technique to overcome the two main limitations of the stabilized soils: the weak tensile/flexural strength and the higher brittleness of the behavior. These types of mixtures require extensive laboratory characterization since they entail the study of a great number of parameters, which consumes time and resources. Thus, this work presents an alternative approach to predict the unconfined compressive strength (UCS) and the tensile strength of soil–binder–water mixtures reinforced with short fibers, following a Machine Learning (ML) approach. Four ML algorithms (Artificial Neural Networks, Support Vector Machines, Random Forest and Multiple Regression) are explored for mechanical prediction of reinforced soil–binder–water mixtures with fibers. The proposed models are supported on representative databases with approximately 100 records for each type of test (UCS and splitting tensile strength tests) and on the consideration of sixteen properties of the composite material (soil, fibers and binder). The predictive models provide an accurate estimation (R2 higher than 0.95 for Artificial Neuronal Networks algorithm) of the compressive and the tensile strength of the soil–water–binder–fiber mixtures. Additionally, the results of the proposed models are in line with the main experimental findings, i.e., the great effect of the binder content in compressive and tensile strength, and the significant effect of the type and the fiber properties in the assessment of the tensile strength.
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