Adaptive civil engineering structures

Most civil structures are passive and static. They are designed according to serviceability and safety requirements. When subjected to actions, they generally deflect in a passive manner. For example, they do not adapt actively to external loading. A more challenging functionality of civil structures is active adaptation to changing requirements, such as load modification, temperature variation, support settlements and eventually damage. This thesis aims to make progress in the field of adaptive civil structures. Intelligent control methodologies such as self diagnosis, multi-objective shape control, self repair and reinforcement learning are proposed and verified experimentally. Although these methodologies are validated experimentally only on a five module active tensegrity structure, there is potential for extension to more complex civil structures. The most important conclusions of this thesis are synthesized below: Self diagnosis can extend active structural control of tensegrity structures to situations where there may be partially defined loading events and damage. Multi-objective search is attractive for computing commands that control shape of an active tensegrity structure while maintaining robustness of both the structure and the active control system. These commands are particularly useful to control structures over scenarios of multiple loading events. Self repair of a damaged active tensegrity structure is possible. The active control system can be used to apply control commands that increase stiffness and decrease stresses within an active tensegrity structure that is damaged. Reinforcement learning is attractive for improving the control of an active tensegrity structure using previous loading events. Improvements involve lower command computation times and better control quality. Such interactions between learning algorithms and active control systems are attractive for control tasks. The integration of intelligent control methodologies such as self diagnosis, multi-objective shape control, self repair and reinforcement learning to an active tensegrity structure create an example of an adaptive civil structure that can be applied to a range of practical situations in the future.

Designing measurement systems for continuous monitoring of structures

Nizar Bel Hadj Ali, Irwanda Laory, Ian Smith, Ngoc Thanh Trinh

A good measurement system improves capability to identify the behavior of civil engineering structures. This paper presents a measurement-system design approach for continuous monitoring of structures. This approach performs two tasks: (1) determining the appropriate number of sensors to be employed and (2) placing the sensors in the most informative locations. Measurement systems are designed using three criteria: minimize the number of non-detectable scenarios, minimize the average time to detection and maximize damage detectability. To select the best compromise solution in the solution set obtained from multi-objective optimization, 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 where measurement data are interpreted using a model-free (non-physics based) method - Moving Principal Component Analysis (MPCA). Results demonstrate that the proposed approach is able to provide engineers with measurement-system-design support for continuous monitoring of structures.

2011

Model-Free Methodologies for Data-Interpretation during Continuous Monitoring of Structures

Irwanda Laory

Most civil engineering infrastructures, especially bridges worldwide, are approaching the end of their designed lifespan. They are continuously deteriorating due largely to material aging, excessive loads and changing environments. Therefore, it is crucial to evaluate the performance of existing structures to prevent catastrophic events. Structural Health Monitoring (SHM) has the potential to provide a proper assessment of structural performance and to further reduce cost through early damage detection and thus replacement avoidance. SHM integrates technologies to monitor structural behaviour and with current advances in sensor technology and measurement systems, the number of bridges that are continuously monitored is increasing. The bottleneck in SHM is data interpretation and this task is even more challenging in the presence of environmental variations. The main stream of data interpretation in SHM involves physics-based modelling and validation. However, building a model can be expensive, time consuming and difficult due to the structural complexity and uncertain environments. This research focuses on model-free methodologies for continuous monitoring of civil structures under environmental variations. The work involves important aspects of SHM such as damage detection, measurement system configuration and environmental variations. For damage detection, a novel model-free methodology that combines Moving Principal Component Analysis (MPCA) and regression analyses is proposed. Such approach aims to exploit the advantages of both MPCA and regression-analysis methods. The methodology has been tested on numerical and experimental studies including a full-scale bridge application. Results of a comparative study with other model-free methodologies demonstrate the superior performance of the proposed methodology in terms of damage detectability and time to detection. This research work also compares the performance of model-free methodologies for predicting natural frequencies of a continuously monitored bridge under environmental variations. Relative importance of environmental factors and traffic loading is also evaluated. Results of the case study reveal that traffic loading and temperature variations are the most influential parameters. A structural identification strategy that takes advantage of thermal variations is developed. The strategy utilizes thermal variations as load cases to evaluate structural performance. Results exhibit the promising potential of the strategy for enhancing structural identification tasks. As for measurement system configuration, a systematic approach that involves multi-objective optimization and Multi Criteria Decision Making (MCDM) methodologies is proposed. The approach is able to accommodate model-free methodologies for damage detection and provide support for selecting the best compromise configuration. It is also applicable for situations where several model-free methods are used for data interpretation.

EPFL2013

Towards biomimetic behavior of an active deployable tensegrity structure

Nicolas Willy Veuve

Tensegrity structures have non-conventional structural properties since they require self-stress in order to be stable. When a tensegrity structure integrates sensors, actuators, (structural members able to change their length) and a control system, deployment and modification of structural behavior becomes feasible. Control of tensegrity structures is a special type of control since multiple-degrees of freedom depend on a lower number of coupled actuators. In addition, there is no direct closed-form solution for achieving a given control task, and this complicates the search for control commands. This thesis investigates the behavior of a near full-scale deployable tensegrity footbridge that deploys from both sides and connects at mid-span. Tests on the structure demonstrate that deployment behavior is not reproducible and therefore, pre-defined control commands are unsuccessful for achieving desired positions. Active control is thus, required for mid-span connection of the two bridge halves. Available simulation models involve assumptions that are not satisfied on the near full-scale structure. In this case non-dimensional, friction free joints are poor assumptions. Using measurements of the response in real-time integrates variable behavior. This advantage shows potential for controlling full-scale structures where changing environment conditions are likely. The active-control methodology for the mid-span connection of the two bridge halves, combining simplified simulations and real-time measurements, accommodates lack of accuracy due to modelling simplifications and non-repeatable behavior. A strategy to partially re-use previous control command reduces the time required for the mid-span connection. In this way, the structure learns from its experience. A damage detection and location method that includes target reliability is tested for single cable damage situations. Despite important modeling simplifications, it is possible to focus on possible damage locations through combination of actuation, measurements, simulations and data interpretation techniques. The structure thus performs self-diagnosis. Active control for structural behavior enhancement and adaptation to damage (self-adaptation) is successfully tested. Simulations are used in order to avoid testing control command that lead to excessive internal forces. Real-time measurements provide a direct evaluation of the actuation effect. Stochastic search is the component that guides the interaction with measurements. Combination of these components results in adaptation and learning since the control system gradually improves performance of the structure through the interaction with the environment during the stochastic search process. Self-diagnosis, self-adaptation and learning are features that are usually associated with living organisms. Thus, this research contributes towards behavior-based biomimetics in structural engineering.