Nizar Bel Hadj Ali

Measurement System Configuration for Damage Identification of Continuously Monitored Structures

Measurement system configuration is an important task in structural health monitoring in that decisions influence the performance of monitoring systems. This task is generally performed using only engineering judgment and experience. Such approach may result in either a large amount of redundant data and high data‐interpretation costs, or insufficient data leading to ambiguous interpretations. This paper presents a systematic approach to configure measurement systems where static measurement data are interpreted for damage detection using model‐free (non‐physics‐based) methods. The proposed approach provides decision support for two tasks: (1) determining the appropriate number of sensors to be employed and (2) placing the sensors at the most informative locations. The first task involves evaluating the performance of measurement systems in terms of the number of sensors. Using a given number of sensors, the second task involves configuring a measurement system by identifying the most informative sensor locations. The locations are identified based on three criteria: the number of non‐detectable damage scenarios, the average time to detection and the damage detectability. A multi‐objective optimization is thus carried out leading to a set of non‐dominated solutions. To select the best compromise solution in this set, two multi criteria decision making methods, Pareto‐Edgeworth‐Grierson multi‐criteria decision making (PEG‐MCDM) and Preference Ranking Organization METhod for Enrichment Evaluation (PROMETHEE), are employed. A railway truss bridge in Zangenberg (Germany) is used as a case study to illustrate the applicability of the proposed approach. Measurement systems are configured for situations where measurement data are interpreted using two model‐free methods: Moving Principal Component Analysis (MPCA) and Robust Regression Analysis (RRA). Results demonstrate that the proposed approach is able to provide engineers with decision support for configuring measurement systems based on the data‐interpretation methods used for damage detection. The approach is also able to accommodate the simultaneous use of several model‐free data‐interpretation methods. It is also concluded that the number of non‐detectable scenarios, the average time to detection and the damage detectability are useful metrics for evaluating the performance of measurement systems when data are interpreted using model‐free methods.

Configuration of control system for damage tolerance of a tensegrity bridge

Tensegrity structures are spatial, discrete, and lightweight structures that are composed of struts in compression and pre-stressed cables. Stability is provided by the self-stress state between elements independently of external actions. Tensegrity structures are attractive due to their potential for deployability, ease of tuning and high precision control. Since tensegrity structures have highly coupled behavior, placement of actuators is a primary concern when designing active control systems. This study investigates the active control performance of cable members of a tensegrity bridge. The actuation efficiencies of cable members are evaluated through a multi-criteria approach. The configuration of the control system is thus identified through outranking candidate active members. A multi-objective damage tolerance strategy is then proposed and optimally directed control solutions are identified using stochastic search. Case studies for several damage scenarios are examined to validate results. The most efficient active cable configuration is compared with that needed for deployment. This study is divided into two phases. After the description of a 16m-span tensegrity bridge, optimally directed locations of active cables are determined in the first phase. Secondly, a procedure to ensure damage tolerance of the structure is proposed. The multi-objective self-repair procedure provides damage tolerance minimizing both maximum deflections in the structure and stresses in the structural members. Results indicate that the control strategy for deployment is a near-optimal solution for damage tolerance. The proposed methodology is applicable to a range of complex active structures.

Estimation of fugitive landfill methane emissions using surface emission monitoring and Genetic Algorithms optimization.

As municipal solid waste (MSW) landfills can generate significant amounts of methane, there is considerable interest in quantifying fugitive methane emissions at such facilities. A variety of methods exist for the estimation of methane emissions from landfills. These methods are either based on analytical emission models or on measurements. This paper presents a method to estimate methane emissions using ambient air methane measurements obtained on the surface of a landfill. Genetic Algorithms based optimization combined with the standard Gaussian dispersion model is employed to identify locations as well as emission rates of potential emission sources throughout a municipal solid waste landfill. Four case studies are employed in order to evaluate the performance of the proposed methodology. It is shown that the proposed approach enables estimation of landfill methane emissions and localization of major emission hotspots in the studied landfills. The proposed source-locating-scheme could be seen as a cost effective method assisting landfill operators to reasonably estimate and locate major methane emissions.

Configuration of Control System for an Adaptive Tensegrity Structure

Keywords: structural control ; optimum active control ; control efficiency ; damage tolerance ; Pareto optimum ; multi‐criteria decision making ; multi‐objective optimization Reference EPFL-CONF-153268 Record created on 2010-10-15, modified on 2016-08-08

Comparison of multi-criteria decision methods through a ranking stability index

Various multi-criteria decision-making (MCDM) procedures have been developed over the last few decades to help decision-making in complex and seemingly intractable decision tasks. A major criticism of various MCDM methods is that they may yield different results when applied to the same problem. A comparative study is proposed here to identify similarities and divergences between the most used MCDM methods. Compared approaches include: ELECTRE III, PROMETHEE I and II, TOPSIS, AHP and PEG. Two multi-criteria case studies are presented. Studied methods are employed to establish an arrangement of a number of alternatives based on two and eight conflicting criteria, respectively. A Gini index is used to quantify rankings dispersion of Pareto optima obtained through studied MCDM methods. Results highlight the sensitivity of the Pareto-compromise design and ranking to the applied MCDM method.