Ian Smith | EPFL Graph Search

PhD, Cambridge University, 1982 Interests 1 Active shape control for structures for deployment and where serviceability criteria governs 2 Biomimetic structures (learning, self-diagnosis, self-repair) 3 Infrastructure management support through structural identification 4 Advanced computer-aided engineering applications of stochastic optimization and search, multi-criteria analysis, system uncertainties (measurement and modelling), multi-modal approaches (combining statistics with behavior models) More details : see https://www.epfl.ch/labs/imac/research/iansmith/

Methodology for selecting measurement points that optimize information gain for model updating

Numa Joy Bertola, Ian Smith

Information collected through sensor measurements has the potential to improve knowledge of complex-system behavior, leading to better decisions related to system management. In this situation, and particularly when using digital twins, the quality of sensor data determines the improvement that sensors have on decision-making. The choice of the monitoring system, including sensor types and their configuration, is typically made using engineering judgement alone. As the price of sensor devices is usually low, large sensor networks have been implemented. As sensors are often used to monitor at high frequencies over long periods, very large data sets are collected. However, model predictions of system behavior are often influenced by only a few parameters. Informative data sets are thus difficult to extract as they are often hidden amid redundant and other types of irrelevant data when updating key parameter values. This study presents a methodology for selecting informative measurements within large data sets for a given model-updating task. By selecting the smallest set that maximizes the information gain, data sets can be significantly refined, leading to increased data-interpretation efficiency. Results of an excavation case study show that the information gains with refined measurement sets that are much smaller than the entire data set are better than using the data set prior to refinement for the same probability of identification, while the computational time of model updating is significantly reduced. This methodology thus supports engineers for significant data filtering to improve model-updating performance.

2023

A framework for occupancy detection and tracking using floor-vibration signals

Slah Drira, Ian Smith

In sensed buildings, information related to occupant movement helps optimize important func-tionalities such as caregiving, energy management, and security enhancement. Typical sensing approaches for occupant tracking rely on mobile devices and cameras. These systems compromise the privacy of building occupants and may affect their behavior. Occupant detection and tracking using floor-vibration measurements that are induced by footsteps is a non-intrusive and inex-pensive sensing method. Detecting the presence of occupants on a floor is challenging due to ambient noise that may mask footstep-induced floor vibrations. In addition, spurious events such as door closing and falling objects may produce vibrations that are similar to footstep impacts. These events have to be detected and disregarded. Tracking occupants is complicated due to uncertainties associated with walking styles, walking speed, shoe type, health, and mood. Also, spatial variation in structural behavior of floor slabs adds ambiguity to the task of occupant tracking, which cannot be addressed using data-driven strategies alone. In this paper, a frame-work for occupant detection and tracking is developed. Occupant detection is carried out based on signal information. This method outperforms existing threshold-based methods. Support- vector-machine classifiers, trained with time and frequency-domain features, successfully distinguish footsteps from spurious events and determine the number of occupants walking simultaneously. A model-based data-interpretation approach is used for occupant tracking. Structural-mechanics models are used to identify a population of possible occupant locations and trajectories. Up to two occupants can be tracked by accommodating systematic bias and un-certainties from sources such as modeling assumptions and variability in walking gaits. A hybrid framework for occupant detection and tracking that combines model-free approaches for occu-pancy detection with structural behavior models for tracking is developed and tested on two full- scale case studies. These studies successfully validate the utility of the framework for buildings having sparse sensor configurations that measure floor vibrations.

2022

The impact of uncertainties on post-earthquake response predictions of a building with unreinforced masonry walls

Katrin Beyer, Mathias Christian Haindl Carvallo, Ian Smith

In this paper, the impact of several sources of uncertainty on the seismic response of an experimentally tested building is assessed for the case of a post-earthquake scenario. The building comprises unreinforced masonry and reinforced concrete walls. It is modeled using an equivalent frame model and latin hypercube sampling is used to generate randomized nonlinear models of the building. A sensitivity analysis is performed in order to identify the model parameters that contribute the most to the response. To deal with several sources of uncertainty and take advantage of the information of the building at the end of the fifth ground motion sequence of the experimental campaign, a data-interpretation methodology called error-domain model falsification is implemented. This methodology involves discarding models that do not reasonably match the measured response in terms of post-earthquake frequency and damage grade, which is determined from the maximum roof displacement. The models that are not falsified are used to predict the response of the building for the final four runs, which is assessed here in terms of the maximum roof displacements. The falsification procedure significantly reduces the number of models that reasonably explain the observed behavior and improves the reliability of response predictions for all ground motions following falsification. Results are accurate in the sense that the experimentally obtained response lies always within the range of predicted responses.

2022

A framework for occupancy detection and tracking using floor-vibration signals

Slah Drira, Ian Smith

2022