Abstract

Abstract

Abstract

The advances in sensors and communication technologies open great possibilities in the management and maintenance of engineering systems. In general, the performance of monitoring devices has undergone relevant improvements in terms of both accuracy and reliability, which have resulted in more information available on the behaviour of the structure under consideration. However, the investments made in the modernization of the monitoring systems are not recovered unless complemented by applications capable of handling such large and diverse information. In this contribution, we present a software tool for importing, exploring, cleaning and analysing monitoring data. Also, it allows for fitting machine-learning behaviour models, as well as interpreting the response of the system to the actions or loads in operation. It was initially developed for dam safety assessment, but can be used -with minor changes- for other engineering systems. The methodology and the overall structure can be categorized in two main sections: (i) the monitoring data can be uploaded, cleaned, completed and analysed and (ii) the machine-learning model can be fitted to predict the variables of interest of the system. The same model can then be used for online detection of anomalies by comparing predictions with recorded behaviour. For example, this allows the identification of abnormal displacements in dams for a given load combination. The software can be equally run locally or in the cloud, with appropriate safe access. It has been written in the R language using the Shiny package for interactivity with the following functionalities: zooming and showing information for data exploration, selecting time periods to interpolate and choosing the training parameters to fit behaviour models.

Abstract

Abstract

'''The installation of automatic data acquisition systems, together with the use of machine learning, allow obtaining useful information on the behaviour of dams. In this contribution, an example of application for a machine learning based predictive model is presented. Specifically, the level in a piezometer and its association with the reservoir level is studied for an embankment dam. The results show the model's ability to identify changes in dam response by taking full advantage of the available monitoring data. The flexibility of the algorithm allows different types of variables to be analysed without the need to determine a priori which are the most influential loads or how they affect the target value. The model has been implemented in a software tool that includes additional functionalities, specific for the treatment and exploration of dam monitoring data. It can be applied to different dam types and response variables.''' '''