Studies of Sensor Data Interpretation for Asset Management of the Built Environment

Sensing in the built environment has the potential to reduce asset management expenditure and contribute to extending useful service life. In the built environment, measurements are usually performed indirectly; effects are measured remote from their causes. Modelling approximations from many sources, such as boundary conditions, geometrical simplifications and numerical assumptions result in important systematic uncertainties that modify correlation values between measurement points. In addition, conservative behavior models that were employed - justifiably during the design stage, prior to construction - are generally inadequate when explaining measurements of real behavior. This paper summarizes the special context of sensor data interpretation for asset management in the built environment. Nearly twenty years of research results from several doctoral thesis and fourteen full-scale case studies in four countries are summarized. Originally inspired from research into model based diagnosis, work on multiple model identification evolved into a methodology for probabilistic model falsification. Throughout the research, parallel studies developed strategies for measurement system design. Recent comparisons with Bayesian model updating have shown that while traditional applications Bayesian methods are precise and accurate when all is known, they are not robust in the presence of approximate models. Finally, details of the full-scale case studies that have been used to develop model falsification are briefly described. The model-falsification strategy for data interpretation provides engineers with an easy-to-understand tool that is compatible with the context of the built environment

A study of two stochastic search methods for structural control

Abstract: Many engineering tasks involve the search for good solutions among many possibilities. In most cases, tasks are too complex to be modeled completely and their solution spaces often contain local minima. Therefore, classical optimization techniques cannot, in general, be applied effectively. This paper studies two stochastic search methods, one well-established �simulated annealing � and one recently developed �probabilistic global search Lausanne�, applied to structural shape control. Search results are applied to control the quasistatic displacement of a tensegrity structure with multiple objectives and interdependent actuator effects. The best method depends on the accuracy related to requirements defined by the objective function and the maximum number of evaluations that are allowed

Combining dynamic relaxation method with artificial neural networks to enhance simulation of tensegrity structures

Abstract: Structural analyses of tensegrity structures must account for geometrical nonlinearity. The dynamic relaxation method correctly models static behavior in most situations. However, the requirements for precision increase when these structures are actively controlled. This paper describes the use of neural networks to improve the accuracy of the dynamic relaxation method in order to correspond more closely to data measured from a full-scale laboratory structure. An additional investigation evaluates training the network during the service life for further increases in accuracy. Tests showed that artificial neural networks increased model accuracy when used with the dynamic relaxation method. Replacing the dynamic relaxation method completely by a neural network did not provide satisfactory results. First tests involving training the neural network online showed potential to adapt the model to changes during the service life of the structure. DOI: 10.1061/�ASCE�0733-9445�2003�129:5�672

Deployment and Shape Change of a Tensegrity Structure Using Path-Planning and Feedback Control

Tensegrity structures are pin-jointed assemblies of struts and cables that are held together in a stable state of stress. Shape control is a combination of control-commands with measurements to achieve a desired form. Applying shape control to a near-full-scale deployable tensegrity structure presents a rare opportunity to analytically and experimentally study and control the effects of large shape changes on a closely coupled multi-element system. Simulated cable-length changes provide an initial activation plan to reach an effective sequence for self-stress. Controlling internal forces is more sensitive than controlling movements through cable-length changes; internal force-control is thus a better objective than movement-control for small adjustments to the structure. The deployment of a tensegrity structure in previous work was carried out using predetermined commands. In this paper, two deployment methods and a method for self-stress are presented. The first method uses feedback cycles to increase speed of deployment compared with implementation of empirically predetermined control-commands. The second method consists of three parts starting with a path-planning algorithm that generates search trees at the initial point and the target point using a greedy algorithm to create a deployment trajectory. Collision and overstress avoidance for the deployment trajectory involve checks of boundaries defined by positions of struts and cables. Even actuator deployment followed by commands obtained from a search algorithm results in the successful connection of the structure at midspan. Once deployment at midspan is achieved by either method, a self-stress algorithm is implemented to correct the position and element forces in the structure to the design configuration prior to in-service loading. Modification of deployment control-commands using the feedback method (with twenty cycles) compared with empirically predetermined control-commands successfully provides a more efficient deployment trajectory prior to midspan connection with up to 50% reduction in deployment time. The path-planning method successfully enables deployment and connection at midspan with a further time reduction of 68% compared with the feedback method (with twenty cycles). The feedback control, the path-planning method and the soft-constraint algorithm successfully lead to efficient deployment and preparation for service loading. Advanced computing algorithms have potential to improve the efficiency of complex deployment challenges

Detecting leak regions through model falsification

Pressurized fluid-distribution networks are strategic elements of infrastructure. In the case of fresh-water distribution networks, advanced sensor-based diagnostic methodologies have the potential to provide enhanced management support. Since a significant percentage of fresh water is lost globally due to leaks in these networks, the challenge to improve performance is compatible with goals of sustainable development. The scope of this research includes the diagnosis of water-distribution networks and more generally, pressurized fluid-distribution networks through development of model-based data-interpretation methods to assess performance. The strategy of model falsification is combined with network reduction techniques to obtain reliable and computationally efficient diagnoses. A case study involves the detection of leaks from an initial set of 263 leak scenarios. Preliminary results show that this methodology has the potential to detect leak regions, even with a small number of sensors

Iterative structural identification framework for evaluation of existing structures

Evaluation of aging infrastructure has been a world-wide concern for decades due to its economic, ecological and societal importance. Existing structures usually have large amounts of unknown reserve capacity that may be evaluated though structural identification in order to avoid unnecessary expenses related to the repair, retrofit and replacement. However, current structural identification techniques that take advantage of measurement data to infer unknown properties of physics-based models fail to provide robust strategies to accommodate systematic errors that are induced by model simplifications and omissions. In addition, behavior diagnosis is an ill-defined task that requires iterative acquisition of knowledge necessary for exploring possible model classes of behaviors. This aspect is also lacking in current structural identification frameworks. This paper proposes a new iterative framework for structural identification of complex aging structures based on model falsification and knowledge-based reasoning. This approach is suitable for ill-defined tasks such as structural identification where information is obtained gradually through data interpretation and in-situ inspection. The study of a full-scale existing bridge in Wayne, New Jersey (USA) confirms that this framework is able to support structural identification through combining engineering judgment with on-site measurements and is robust with respect to effects of systematic uncertainties. In addition, it is shown that the iterative structural-identification framework is able to explore the compatibility of several model classes by model-class falsification, thereby helping to provide robust diagnosis and prognosis

Using measurements to reduce model uncertainty for better predictions

Accurate models of real behaviour that are determined through measurements help engineers avoid expensive interventions and structural replacement. Model calibration by “curve-fitting” measurements to predictions is not appropriate for full-scale structures. This paper compares two population methods that can include modelling and measurement uncertainties using a simple example of a one-span beam. Standard applications of Bayesian inference that involve assumptions of independent zero-mean Gaussian distributions may not lead to accurate predictions, particularly when extrapolating. Another method, error-domain model falsification provides more reliable, albeit more approximate, predictions – especially when prediction is extrapolation. An example of a full-scale bridge illustrates the usefulness of the methodology in a real situation through improvements to fatigue-life estimates compared with design-type calculations without measurements

Robust system identification and model predictions in the presence of systematic uncertainty

Model-based data-interpretation techniques are increasingly used to improve the knowledge of complex system behavior. Physics-based models that are identified using measurement data are generally used for extrapolation to predict system behavior under other actions. In order to obtain accurate and reliable extrapolations, model-parameter identification needs to be robust in terms of variations of systematic modeling uncertainty introduced when modeling complex systems. Approaches such as Bayesian inference are widely used for system identification. More recently, error-domain model falsification (EDMF) has been shown to be useful for situations where little information is available to define the probability density function (PDF) of modeling errors. Model falsification is a discrete population methodology that is particularly suited to knowledge intensive tasks in open worlds, where uncertainty cannot be precisely defined. This paper compares conventional uses of approaches such as Bayesian inference and EDMF in terms of parameter-identification robustness and extrapolation accuracy. Using Bayesian inference, three scenarios of conventional assumptions related to inclusion of modeling errors are evaluated for several model classes of a simple beam. These scenarios are compared with results obtained using EDMF. Bayesian model class selection is used to study the benefit of posterior model averaging on the accuracy of extrapolations. Finally, ease of representation and modification of knowledge is illustrated using an example of a full-scale bridge. This study shows that EDMF leads to robust identification and more accurate predictions than conventional applications of Bayesian inference in the presence of systematic uncertainty. These results are illustrated with a full-scale bridge. This example shows that the engineering knowledge necessary to perform parameter identification and remaining-fatigue-life predictions of a complex civil structure is easily represented by the EDMF methodology. Model classes describing complex systems should include two components: (1) unknown physical parameters that are identified using measurements; (2) conservative modeling error estimations that cannot be represented only as uncertainties related to physical parameters. In order to obtain accurate predictions, both components need to be included in the model-class definition. This study indicates that Bayesian model class selection may lead to over-confidence in certain model classes, resulting in biased extrapolation