Sai Ganesh Sarvotham Pai | EPFL Graph Search

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

A methodology to design measurement systems when multiple model classes are plausible

Numa Joy Bertola, Sai Ganesh Sarvotham Pai, Ian Smith

The management of existing civil infrastructure is challenging due to evolving functional requirements, aging and climate change. Civil infrastructure often has hidden reserve capacity because of conservative approaches used in design and during construction. Information collected through sensor measurements has the potential to improve knowledge of structural behavior, leading to better decisions related to asset management. In this situation, the design of the monitoring system is an important task since it directly affects the quality of the information that is collected. Design of optimal measurement systems depends on the choice of behavior-model parameters to identify using monitoring data and non-parametric uncertainty sources. A model that contains a representation of these parameters as variables is called a model class. Selection of the most appropriate model class is often difficult prior to acquisition of information regarding the structural behavior, and this leads to suboptimal sensor placement. This study presents strategies to efficiently design measurement systems when multiple model classes are plausible. This methodology supports the selection of a sensor configuration that provides significant information gain for each model class using a minimum number of sensors. A full-scale bridge, The Powder Mill Bridge (USA), and an illustrative beam example are used to compare methodologies. A modification of the hierarchical algorithm for sensor placement has led to design of configurations that have fewer sensors than previously proposed strategies without compromising information gain.

2021

Model-Based Interpretation of Measurements for Fatigue Evaluation of Existing Reinforced Concrete Bridges

Imane Bayane, Eugen Brühwiler, Sai Ganesh Sarvotham Pai, Ian Smith

New methods are required for sustainable and economical management of bridges. Efficient management can be achieved by a detailed understanding of bridge behavior through monitoring and model-based data interpretation. This paper presents a methodology to evaluate the fatigue safety of existing bridges based on conducting measurements onsite and interpreting measurement data using physics-based behavior models. The methodology combines data from different nondestructive measurements with structural models to develop a suitable set of feasible models that describe accurately structural behavior. The methodology is illustrated with a case study of a composite steel–concrete road viaduct instrumented with acoustic emission channels and strain gauges. Information from measurements is used to update a set of structural models and then evaluate the fatigue safety of the viaduct. While commonly used curve-fitting methods are inaccurate, this methodology is useful to accurately employ the measured behavior of existing civil infrastructure for evaluating nonaccessible elements and scheduling inspections and decision-making related to actions such as strengthening and retrofit.

2021

Using footstep-induced vibrations for occupant detection and recognition in buildings

Slah Drira, Sai Ganesh Sarvotham Pai, Yves Sylvain Gilles Reuland, Ian Smith

Occupant detection and recognition support functional goals such as security, healthcare, and energy management in buildings. Typical sensing approaches, such as smartphones and cameras, undermine the privacy of building occupants and inherently affect their behavior. To overcome these drawbacks, a non-intrusive technique using floor-vibration measurements, induced by human footsteps, is outlined. Detection of human-footstep impacts is an essential step to estimate the number of occupants, recognize their identities and provide an estimate of their probable locations. Detecting the presence of occupants on a floor is challenging due to ambient noise that may mask footstep-induced floor vibrations. Also, signals from multiple occupants walking simultaneously overlap, which may lead to inaccurate event separation. Signals corresponding to events, once extracted, can be used to identify the number of occupants and their locations. Spurious events such as door closing, chair dragging and falling objects may produce vibrations similar to footstep-impacts. Signals from such spurious events have to be discarded as outliers to prevent inaccurate interpretations of floor vibrations for occupant detection. Walking styles differ among occupants due to their anatomies, walking speed, shoe type, health and mood. Thus, footstep-impact vibrations from the same person may vary significantly, which adds uncertainty and complicates occupant recognition. In this paper, efficient strategies for event-detection and event-signal extraction have been described. These strategies are based on variations in standard deviations over time of measured signals (using a moving window) that have been filtered to contain only low-frequency components. Methods described in this paper for event detection and event-signal extraction perform better than existing threshold-based methods (fewer false positives and false negatives). Support vector machine classifiers are used successfully to distinguish footsteps from other events and to determine the number of occupants on a floor. Convolutional neural networks help recognize the identity of occupants using footstep-induced floor vibrations. The utility of these strategies for footstep-event detection, occupant counting, and recognition is validated successfully using two full-scale case studies.

2021