Data-Driven Model for Stability Condition Prediction of Soil Embankments Based on Visual Data Features

Abstract

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.

Application of Artificial Neural Networks in Construction Management: A Scientometric Review

“…Reliable results can avoid potential errors and reduce unnecessary waste, which is essential to the success of the construction project. The optimization effect is reflected in generating valuable suggestions for improvement in complex CM tasks under conflicting requirements and limitations, such as cash flow control [74], capital allocation plan [101], optimization control of schedule plan [69], time allocation of material processing [67], construction quality inspection [136], and adjustment of construction machinery posture and position [98]. Such findings can guide the optimization of the construction execution process, enabling timely adjustments at an early stage.…”

“…The ANN model can identify and evaluate risks in the new environment by capturing the interdependence between accidents and their causes in historical data, which effectively avoids the limitations of traditional risk analysis, such as the vagueness and subjectivity of expert experience. In the case of high uncertainty, ANN has been predominantly adopted for risk analysis in terms of finance [64], safety [77], contract [46], and quality [136]. Research on project dispute claim risk prediction, optimal risk allocation in PPP projects, risk analysis for BOT project contracts, early-warning for site work risk and bid selection has been carried out to improve the level of risk management.…”

Soil-Cement Mixtures Reinforced with Fibers: A Data-Driven Approach for Mechanical Properties Prediction

“…These advanced algorithms have been widely applied in different knowledge domains [23,24] with very promising results and taking advantage of a consolidated experience. In the field of Civil Engineering, several successful applications of these tools can be found [25][26][27], including solving complex geotechnical problems related to slopes stability assessment [28,29]. These algorithms have also been applied in the study of mechanical properties of soil-binder-water mixtures as reported on Tinoco et al [30], which underline the non-linear learning capabilities of these algorithms.…”

Prediction of Elastic Modulus for Fibre-Reinforced Soil-Cement Mixtures: A Machine Learning Approach

“…Therefore, such advanced algorithms are extensively used in diverse areas of learning [28,29] with very encouraging results by leveraging on consolidated experience. In engineering, these tools have been used successfully to solve complicated geotechnical problems associated with slope stability and mechanical properties of soil-cement mixtures [30][31][32][33][34][35].…”