James-A. Goulet

Multimodel Structural Performance Monitoring

Measurements from load tests may lead to numerical models that better reflect structural behavior. This kind of system identification is not straightforward due to important uncertainties in measurement and models. Moreover, since system identification is an inverse engineering task, many models may fit measured behavior. Traditional model updating methods may not provide the correct behavioral model due to uncertainty and parameter compensation. In this paper, a multimodel approach that explicitly incorporates uncertainties and modeling assumptions is described. The approach samples thousands of models starting from a general parametrized finite-element model. The population of selected candidate models may be used to understand and predict behavior, thereby improving structural management decision making. This approach is applied to measurements from structural performance monitoring of the Langensand Bridge in Lucerne, Switzerland. Predictions from the set of candidate models are homogenous and show an average discrepancy of 4-7% from the displacement measurements. The tests demonstrate the applicability of the multimodel approach for the structural identification and performance monitoring of real structures. The multimodel approach reveals that the Langensand Bridge has a reserve capacity of 30% with respect to serviceability requirements.

Model falsification diagnosis and sensor placement for leak detection in pressurized pipe networks

Pressurized pipe networks used for fresh-water distribution can take advantage of recent advances in sensing technologies and data-interpretation to evaluate their performance. In this paper, a leak-detection and a sensor placement methodology are proposed based on leak-scenario falsification. The approach includes modeling and measurement uncertainties during the leak detection process. The performance of the methodology proposed is tested on a full-scale water distribution network using simulated data. Findings indicate that when monitoring the flow velocity for 14 pipes over the entire network (295 pipes) leaks are circumscribed within a few potential locations. The case-study shows that a good detectability is expected for leaks of 50L/min or more. A study of measurement configurations shows that smaller leak levels could also be detected if additional pipes are instrumented.

Measurement, Data Interpretation, and Uncertainty Propagation for Fatigue Assessments of Structures

The real behavior of existing structures is usually associated with large uncertainty that is often covered by the use of conservative models and code practices for the evaluation of remaining fatigue lives. To make better decisions related to retrofit and replacement of existing bridges, new techniques that can quantify fatigue reserve capacity are required. This paper presents a population-based prognosis methodology that takes advantage of in-service behavior measurements using model-based data interpretation. This approach is combined with advanced traffic and fatigue models to refine remaining fatigue-life predictions. Study of a full-scale bridge revealed that this methodology provides less conservative estimations of remaining fatigue lives. In addition, this approach propagates uncertainties associated with finite-element, traffic, and fatigue-damage models to quantify their effects on fatigue-damage assessments and shows that traffic models and structural model parameters are the most influential sources of uncertainty.

Extended Uniform Distribution Accounting for Uncertainty of Uncertainty

Increasingly, uncertainties are explicitly considered for important engineering tasks. Often, little case-specific information is available for characterizing these uncertainties. Uniform distributions are an easy way to describe errors in absence of more precise information. In many situations, the bounds are fixed based on user experience. The extended uniform distribution (EUD) provides a probability density function that accounts for higher orders of uncertainty (uncertainty of uncertainty) when using a uniform distribution to describe errors. Since the EUD accounts for several orders of uncertainty it is more representative than uniform and curvilinear distributions. The extended uniform distribution helps increase the reliability and robustness of tasks requiring uncertainty combination through better representing incomplete knowledge of parameters.

Exploring approaches to improve the performance of autonomous monitoring with imperfect data in location-aware wireless sensor networks

In recent years, information and sensing technologies have been applied to the construction industry to collect and provide rich information to facilitate decision making processes. One of the applications is using location data to support autonomous crane safety monitoring (e.g., collision avoidance and dangerous areas control). Several location-aware wireless technologies such as GPS (Global Positioning System), RFID (Radio-frequency identification), and Ultra-Wide Band sensors, have been proposed to provide location information for autonomous safety monitoring. However, previous studies indicated that imperfections (errors, uncertainty, and inconsistency) exist in the data collected from those sensors and the data imperfections have great impacts on autonomous safety monitoring system performance. This paper explores five computationally light-weight approaches to deal with the data imperfections, aiming to improve the system performance. The authors built a scaled autonomous crane safety monitoring testbed with a mounted localization system to collect location data and developed five representative test cases based on a live construction jobsite. Seven hundred and sixty location readings were collected at thirty-eight test points from the sensors. Those location data was fed into the reasoning mechanisms with five approaches to generate the safety decisions at those thirty-eight test points and evaluate system performance in terms of precision, recall and accuracy. The results indicate that system performance can be improved if at least ten position readings from sensors can be collected at small intervals at any location along the moving path. However, by including additional data such as velocity and acceleration that may be read from devices mounted on workers, localization error may be significantly reduced. These findings represent a path forward to improve localization accuracy by mixing imperfect data from the sensed environment with supplemental input.

Overcoming the limitations of traditional model-updating approaches

Model updating is useful for improving structural performance assessments. This paper examines an important assumption of traditional model-updating approaches. This assumption requires the error independence between points where predictions and measurements are compared. Simulations performed on a full-scale bridge show that uncertainties are correlated for both static and dynamic predictions. Therefore, traditional model-updating techniques are not appropriate in such situations. Model updating limitations related to randomness and independence of uncertainties may be overcome by an interpretation strategy called Candidate Model Search for System Identification (CMS4SI). Instead of judging a model by its ability to fit measured data, the approach falsifies models using threshold values that are based upon uncertainties. Uncertainties may be correlated, systematic, independent or random.

Measurement system design for civil infrastructure using expected utility

For system identification, most sensor-placement strategies are based on the minimization of the model-parameter uncertainty. However, reducing the uncertainty in remaining-life prognosis of structures is often more relevant. This paper proposes an optimization strategy using utility theory and probabilistic behavior prognoses based on model falsification to support decisions related to monitoring interventions. This approach, illustrated by the full-scale case study of a bridge, allows quantification of the expected utility of measurement systems while also indicating the profitability of monitoring actions. In addition, this approach is able to determine when the expected performance of monitoring configurations is reduced due to over-instrumentation. The use of model falsification for system identification allows for explicit inclusion of engineering heuristics in this knowledge intensive task while also offering robustness to effects of systematic modeling errors that are associated with idealization of complex civil structures.

Considering sensor characteristics during measurement-system design for structural system identification

This paper presents a method for measurement-system design through criteria related to model based structural identification. Using a multi-model approach and results from previous research carried out at EPFL, an improved algorithm is proposed. The algorithm accounts for various types of sensors having different accuracies and taking different kinds of measurements. The algorithm selects sensor types and locations that minimise the number of non-identified candidate models. The results show that the approach provides an alternative to selecting and placing sensors using engineering experience alone, and that a scientific approach based on sensor characteristics and modelling error is feasible. A single span composite bridge is used to illustrate the algorithm. It is shown that adding more than 9 sensors, from a possible set of 34, will not provide further useful information for structural identification.

Structural Identification to Improve Bridge Management

This paper presents results from static loads tests performed on the new Langensand Bridge built in Switzerland. A systematic study of over 1000 models subjected to three load cases identifies a set of 11 candidate models through static measurements. Predictions using the set of candidate models are homogenous and show an averaged discrepancy ranging of 4 to 7% from the displacement measurements. All candidate models have values for material proprieties that are close to expected values. This finding confirms that the behaviour of the structure conforms to the design expectations. Comparing the candidate model set to a design model that takes into account only main structural elements shows that the structure has approximately 30% reserve capacity with respect to a typical deflection risk scenario according to Swiss codes. The population of candidate models may be used to understand and predict the behaviour of the full bridge prior to its completion.

A machine learning approach for characterizing soil contamination in the presence of physical site discontinuities and aggregated samples

Rehabilitation of contaminated soils in urban areas is in high demand because of the appreciation of land value associated with the increased urbanization. Moreover, there are financial incentives to minimize soil characterization uncertainties. Minimizing uncertainty is achieved by providing models that are better representation of the true site characteristics. In this paper, we propose two new probabilistic formulations compatible with Gaussian Process Regression (GPR) and enabling (1) to model the experimental conditions where contaminant concentration is quantified from aggregated soil samples and (2) to model the effect of physical site discontinuities. The performance of approaches proposed in this paper are compared using a Leave One Out Cross-Validation procedure (LOO-CV). Results indicate that the two new probabilistic formulations proposed outperform the standard Gaussian Process Regression.