H. F. Lam

A probabilistic approach to structural model updating

The problem of updating a structural model and its associated uncertainties by utilizing measured dynamic response data is addressed. A Bayesian probabilistic formulation is followed to obtain the posterior probability density function (PDF) of the uncertain model parameters for given measured data. The present paper discusses the issue of identifiability of the model parameters and reviews existing asymptotic approximations for identifiable cases. The focus of the paper is on the treatment of the general unidentifiable case where the earlier approximations are not applicable. In this case the posterior PDF of the parameters is found to be concentrated in the neighborhood of an extended and extremely complex manifold in the parameter space. The computational difficulties associated with calculating the posterior PDF in such cases are discussed and an algorithm for an efficient approximate representation of the above manifold and the posterior PDF is presented. Numerical examples involving noisy data are presented to demonstrate the concepts and the proposed method.

Structural Health Monitoring via Measured Ritz Vectors Utilizing Artificial Neural Networks

A pattern recognition approach for structural health monitoring (SHM) is presented that uses damage-induced changes in Ritz vectors as the features to characterize the damage patterns defined by the corresponding locations and severity of damage. Unlike most other pattern recognition methods, an artificial neural network (ANN) technique is employed as a tool for systematically identifying the damage pattern corresponding to an observed feature. An important aspect of using an ANN is its design but this is usually skipped in the literature on ANN-based SHM. The design of an ANN has significant effects on both the training and performance of the ANN. As the multi-layer perceptron ANN model is adopted in this work, ANN design refers to the selection of the number of hidden layers and the number of neurons in each hidden layer. A design method based on a Bayesian probabilistic approach for model selection is proposed. The combination of the pattern recognition method and the Bayesian ANN design method forms a practical SHM methodology. A truss model is employed to demonstrate the proposed methodology.

A Bayesian Probabilistic Approach for Crack Characterization in Plate Structures

This article explores the possibility of using a Bayesian probabilistic approach for detection of cracks in thin plate structures, utilizing measured dynamic responses at only a few points on the plate. Existing laser scanning or shearography based crack detection methods are applicable only when measurement at the region near the defect is possible. These types of techniques are important in providing information, in addition to that obtained through visual inspection, for the purpose of structural health monitoring. Because of the global nature of the vibration characteristics of structural systems, this article puts forward a crack detection approach that can be applicable with only a few sensors and when the sensor locations are not close to the crack. This kind of method is particularly valuable as it can be applied when visual inspection is not possible (e.g., part of the plate is obstructed and is not assessable by inspectors). Owing to the problems of measurement noise and incomplete measurement (i.e., only a limited number of measurement points are employed and high-mode information is lost because of the digitization of the signal and measurement noise), the results of crack detection as an inverse problem contain uncertainties. To explicitly handle such uncertainty, the proposed crack detection method follows the Bayesian statistical system identification framework. Rather than pinpointing the crack parameters (i.e., the crack location, length, and depth), the posterior probability density function (PDF) of the crack parameters is calculated to quantify the confidence level of the identified results, which is extremely important for engineers when they make judgments about remedial works. This article reports the theoretical development of the modeling of a cracked plate and a crack detection method together with numerical verification of the proposed method. The case study results are very encouraging, and indicate that the proposed method is feasible.

Analysis and design of the general and outmost-ring stiffened suspen-dome structures

Abstract This paper addresses the analysis and design issues of the suspen-dome structural system. A simple design method for calculating prestress forces in the cables of suspen-dome structures is presented. Emphasis is made in a particular case of the suspen-dome system—the outmost-ring stiffened suspen-dome structure. Nonlinear static, dynamic, and buckling analyses are carried out on the three different types of dome structures. The results show that the tensegric system (cable-strut system) significantly improves the structural properties of the single-layer dome system. The outmost-ring stiffened suspen-dome structure, which has a relatively low construction cost, is as efficient as the general suspen-dome structure under both static and dynamic conditions. The outmost-ring stiffened suspen-dome structure is recommended for cases in which the buckling capacity is not a primary consideration in design.

Treatment of unidentifiability in structural model updating

The present study addresses the issues of non-uniqueness and unidentifiability arising in structural model updating. A Bayesian probabilistic framework is used for model updating which properly handles the uncertainties due to model error and measurement noise associated with model updating. Uncertainties in the model parameters are quantified by probability density functions (PDF) specifying the relative plausibilities of the possible values of the parameters. The Bayesian formulation is well-suited for updating the PDF of the uncertain model parameters taking into account engineering experience and measured dynamic data. Methods are presented for approximating this updated PDF for the general unidentifiable case for which the region of significant probabilities is concentrated in the neighborhood of a manifold of lower dimension than the original parameter space. This PDF is useful for both model updating and structural damage predictions. Asymptotic approximations are also developed for computing the uncertainties in the model response predictions. It is demonstrated that unidentifiable cases are not treatable by existing results valid only for identifiable cases for which the dimension of the manifold is exactly zero. Two examples involving simulated model error and measurement noise are presented to demonstrate the advantages of the new proposed method in effectively addressing unidentifiability issues.

Ambient Vibration Test, Modal Identification and Structural Model Updating Following Bayesian Framework

Structural health monitoring (SHM) of civil engineering structures based on vibration data includes three main components: ambient vibration test, modal identification and model updating. This paper discussed these three components in detail and proposes a general framework of SHM for practical application. First, a fast Bayesian modal identification method based on Fast Fourier Transform (FFT) is introduced for efficiently extracting modal parameters together with the corresponding uncertainties from ambient vibration data. A recently developed Bayesian model updating method using Markov chain Monte Carlo simulation (MCMCS) is then discussed. To illustrate the performance of the proposed modal identification and model updating methods, a scale-down transmission tower is investigated. Ambient vibration test is conducted on the target structure to obtain modal parameters. By using the measured modal parameters, model updating is carried out. The MCMC-based Bayesian model updating method can efficiently eva...

Web‐Based Information Management System for Construction Projects

This paper presents a web-based project information management (WebPIM) system for civil engineering applications, with particular emphasis on construction project management. In the proposed system, all project information is centralized in a project database residing in the project server, instead of being distributed to many different locations. By utilizing the latest web technology, the system works as an information platform for all design and construction participants of a construction project throughout the life cycle of the project. A prototype model is designed in this paper to illustrate the application of the proposed system and the hardware and software requirements for the intended application. Discussion is given on the security and speed of data transfer, as well as an effectiveness comparison among various project management methodologies.

Entropy-Based Optimal Sensor Placement for Model Identification of Periodic Structures Endowed with Bolted Joints

The number of sensors and the corresponding locations are very important for the information content obtained from the measured data, which is a recognized challenging problem for large-scale structural systems. This article pays special attention to the sensor placement issues on a large-scale periodically articulated structure representing typical pipelines to extract the most information from measured data for the purpose of model identification. The minimal model parameter estimation uncertainties quantified by the information entropy (IE) measure is taken as the optimality criterion for sensors placement. By utilizing the inherent periodicity property of this type of structure together with the Bloch theorem, a novel tailor-made modeling approach is proposed and the computational cost required for dynamic analysis to form the IE with respect to the entire periodic structure can be dramatically reduced regardless of the number of contained periodic units. In addition, to avoid the error of dynamic modeling induced by conventional finite element method based on static shape function, the spectral element method, a highly accurate dynamic modeling method, is employed for modeling the periodic unit. Moreover, a novel discrete optimization method is developed, which is very efficient in terms of the number of function evaluations. The proposed methodology is demonstrated by both numerical and laboratory experiments conducted for a bolt-connected periodic beam model.

Series of Full-Scale Field Vibration Tests and Bayesian Modal Identification of a Pedestrian Bridge

Many spectacular pedestrian bridges were designed and constructed recently. Owing to their special shapes, it is expected that various types and a wide range of vibration frequency components will be induced by pedestrians. To avoid accidents and reduce risk, the vibration characteristics of pedestrian bridges during their service life must be carefully assessed. The most direct and reliable way to study the vibration characteristics of a structural system is through field vibration tests. In this paper, a series of full-scale field vibration tests (including ambient, forced, and free vibration tests) were carried out on a pedestrian bridge at City University of Hong Kong (CityU). The difficulties encountered in the field tests are reported. The recently developed Bayesian methods were utilized to determine the modal parameters of the bridge based on measurements from all three kinds of tests. In addition to the most probable values (MPVs) of modal parameters, the associated posterior uncertainties were also analytically calculated. Four modes were identified, including three vertical bending modes and one torsional mode. The accuracy of the identified modal parameters was assessed through the posterior uncertainty. Because the amplitudes of the vibration in the three kinds of tests were different, the modal parameters determined from these kinds of tests were compared and discussed. Suggestions related to the proper use and potential vibration problems during the lifecycle of pedestrian bridges were provided based on the analysis results.

Use of Measured Vibration of In-Situ Sleeper for Detecting Underlying Railway Ballast Damage

The identification of railway ballast damage under a concrete sleeper is investigated by following the Bayesian approach. The use of a discrete modeling method to capture the distribution of ballast stiffness under the sleeper introduces artificial stiffness discontinuities between different ballast regions. This increases the effects of modeling errors and reduces the accuracy of the ballast damage detection results. In this paper, a continuous modeling method was developed to overcome this difficulty. The uncertainties induced by modeling error and measurement noise are the major difficulties of vibration-based damage detection methods. In the proposed methodology, Bayesian probabilistic approach is adopted to explicitly address the uncertainties associated with the identified model parameters. In the model updating process, the stiffness of the ballast foundation is assumed to be continuous along the sleeper by using a polynomial of order N. One of the contributions of this paper is to calculate the order N conditional on a given set of measurement utilizing the Bayesian model class selection method. The proposed ballast damage detection methodology was verified with vibration data obtained from a segment of full-scale ballasted track under laboratory conditions, and the experimental verification results are very encouraging showing that it is possible to use the Bayesian approach along with the newly developed continuous modeling method for the purpose of ballast damage detection.