Anomaly Detection in Dam Behaviour with Machine Learning Classification Models

Dam safety assessment is typically made by comparison between the outcome of some predictive model and measured monitoring data. This is done separately for each response variable, and the results are later interpreted before decision making. In this work, three approaches based on machine learning classifiers are evaluated for the joint analysis of a set of monitoring variables: multiclass, two-class and one-class classification. Support vector machines are applied to all prediction tasks, and random forest is also used for multi-class and two-class. The results show high accuracy for multi-class classification, although the approach has limitations for practical use. The performance in two-class classification is strongly dependent on the features of the anomalies to detect and their similarity to those used for model fitting. The one-class classification model based on support vector machines showed high prediction accuracy, while avoiding the need for correctly selecting and modelling the potential anomalies. A criterion for anomaly detection based on model predictions is defined, which results in a decrease in the misclassification rate. The possibilities and limitations of all three approaches for practical use are discussed

Machine Learning for Structural Monitoring and Anomaly Detection

Autonomous structural health monitoring (SHM) of a large number of structures became a topic of paramount importance for maintenance purposes and safety reasons in the last few decades. Civil infrastructures are the backbone of modern society, and the assessment of their conditions is of renowned importance. This aspect is even more exacerbated because of the existing system that are fast approaching their service life. Since the replacement of those structures is functionally and economically demanding, maintenance and retrofitting operations must be planned wisely. Moreover, the increasing amount and variety of data generated by users and sensors interconnected to the future 6G network requires new strategies to manage several types of data with highly different characteristics and also requires solutions to power the wireless network with renewable energies. In this scenario, the adoption of artificial intelligence and in particular machine learning (ML) strategies represents a flexible and potentially powerful solution that must be investigated. To manage the big and widespread amount of data generated by the extensive usage of multiple types of sensors, several ML techniques can be investigated, with the aim to perform data fusion and reduce the amount of data. Furthermore, the usage of anomaly detection techniques to identify potentially critical situations in infrastructures and buildings represents a topic of particular interest that still needs a significant investigation effort. In this research activity, we provide the fundamental guidelines to perform automatic damage detection, which combines SHM strategies and ML algorithms capable of performing anomaly detection on a wide set of structures. In particular, several algorithms and strategies capable of extracting relevant features from large amounts of data generated by different types of sensors are investigated. Finally, to effectively manage such an amount of data in communication constraints, we obtained some design rules for the acquisition system for bridge monitoring

Classification, Localization, and Quantification of Structural Damage in Concrete Structures using Convolutional Neural Networks

Applications of Machine Learning (ML) algorithms in Structural Health Monitoring (SHM) have recently become of great interest owing to their superior ability to detect damage in engineering structures. ML algorithms used in this domain are classified into two major subfields: vibration-based and image-based SHM. Traditional condition survey techniques based on visual inspection have been the most widely used for monitoring concrete structures in service. Inspectors visually evaluate defects based on experience and engineering judgment. However, this process is subjective, time-consuming, and hampered by difficult access to numerous parts of complex structures. Accordingly, the present study proposes a nearly automated inspection model based on image processing, signal processing, and deep learning for detecting defects and identifying damage locations in typically inaccessible areas of concrete structures. The work conducted in this thesis achieved excellent damage localization and classification performance and could offer a nearly automated inspection platform for the colossal backlog of ageing civil engineering structures

Advances in Remote Sensing-based Disaster Monitoring and Assessment

Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones

A machine learning-based surrogate model for the identification of risk zones due to off-stream reservoir failure

Approximately 70,000 Spanish off-stream reservoirs, many of them irrigation ponds, need to be evaluated in terms of their potential hazard to comply with the new national Regulation of the Hydraulic Public Domain. This requires a great engineering effort to evaluate different scenarios with two-dimensional hydraulic models, for which many owners lack the necessary resources. This work presents a simplified methodology based on machine learning to identify risk zones at any point in the vicinity of an off-stream reservoir without the need to elaborate and run full two-dimensional hydraulic models. A predictive model based on random forest was created from datasets including the results of synthetic cases computed with an automatic tool based on the two-dimensional numerical software Iber. Once fitted, the model provided an estimate on the potential hazard considering the physical characteristics of the structure, the surrounding terrain and the vulnerable locations. Two approaches were compared for balancing the dataset: the synthetic minority oversampling and the random undersampling. Results from the random forest model adjusted with the random undersampling technique showed to be useful for the estimation of risk zones. On a real application test the simplified method achieved 91% accuracy.This work was partially funded by the Spanish Ministry of Science, Innovation and Universities through the Projects ACROPOLIS (RTC2019-007343-5), TRISTAN (RTI2018-094785-B-I00) and DOLMEN (PID2021-122661OB-I00), as well as by the Spanish Ministry of Economy and Competitiveness, through the “Severo Ochoa Programme for Centres of Excellence in R & D” (CEX2018-000797-S), and by the Generalitat de Catalunya through the CERCA Program.Peer ReviewedPostprint (published version

Validation of Machine Learning Models for Structural Dam Behaviour Interpretation and Prediction

The main aim of structural safety control is the multiple assessments of the expected dam behaviour based on models and the measurements and parameters that characterise the dam’s response and condition. In recent years, there is an increase in the use of data-based models for the analysis and interpretation of the structural behaviour of dams. Multiple Linear Regression is the conventional, widely used approach in dam engineering, although interesting results have been published based on machine learning algorithms such as artificial neural networks, support vector machines, random forest, and boosted regression trees. However, these models need to be carefully developed and properly assessed before their application in practice. This is even more relevant when an increase in users of machine learning models is expected. For this reason, this paper presents extensive work regarding the verification and validation of data-based models for the analysis and interpretation of observed dam’s behaviour. This is presented by means of the development of several machine learning models to interpret horizontal displacements in an arch dam in operation. Several validation techniques are applied, including historical data validation, sensitivity analysis, and predictive validation. The results are discussed and conclusions are drawn regarding the practical application of data-based models

A Machine Learning-Based Surrogate Model for the Identification of Risk Zones Due to Off-Stream Reservoir Failure

Approximately 70,000 Spanish off-stream reservoirs, many of them irrigation ponds, need to be evaluated in terms of their potential hazard to comply with the new national Regulation of the Hydraulic Public Domain. This requires a great engineering effort to evaluate different scenarios with two-dimensional hydraulic models, for which many owners lack the necessary resources. This work presents a simplified methodology based on machine learning to identify risk zones at any point in the vicinity of an off-stream reservoir without the need to elaborate and run full two-dimensional hydraulic models. A predictive model based on random forest was created from datasets including the results of synthetic cases computed with an automatic tool based on the two-dimensional numerical software Iber. Once fitted, the model provided an estimate on the potential hazard considering the physical characteristics of the structure, the surrounding terrain and the vulnerable locations. Two approaches were compared for balancing the dataset: the synthetic minority oversampling and the random undersampling. Results from the random forest model adjusted with the random undersampling technique showed to be useful for the estimation of risk zones. On a real application test the simplified method achieved 91% accuracy