C. G. Koh

A hybrid computational strategy for identification of structural parameters

By identifying parameters such as stiffness values of a structural system, the numerical model can be updated to give more accurate response prediction or to monitor the state of the structure. Considerable progress has been made in this subject area, but most research works have considered only small systems. A major challenge lies in obtaining good identification results for systems with many unknown parameters. In this study, a non-classical approach is adopted involving the use of genetic algorithms (GA). Nevertheless, direct application of GA does not necessarily work, particularly with regards to computational efficiency in fine-tuning when the solution approaches the optimal value. A hybrid computational strategy is thus proposed, combining GA with a compatible local search operator. Two hybrid methods are formulated and illustrated by numerical simulation studies to perform significantly better than the GA method without local search. A fairly large structural system with 52 unknown parameters is identified with good results, taking into consideration the effects of incomplete measurement and noisy data.

Substructural and progressive structural identification methods

Abstract While it is possible in principle to determine unknown structural parameters by system identification techniques, a major challenge lies in the numerical difficulty in obtaining reasonably accurate results when the system size is large. Adopting the strategy of “divide-and-conquer” to address this issue, substructural identification and progressive structural identification methods are formulated. The main idea is to divide the structure into substructures such that the number of unknown parameters is within manageable size in each stage of identification. A non-classical approach of genetic algorithms is employed as the search tool for its several advantages including ease of implementation and desirable characteristics of global search. Numerical simulation study is presented, including a fairly large system of 50 degrees of freedom, to illustrate the identification accuracy and efficiency. The methods are tested for known-mass and unknown-mass systems with up to 102 unknown parameters, accounting for the effects of incomplete and noisy measurements.

Plastic Optical Fibre Sensors for Structural Health Monitoring: A Review of Recent Progress

While a number of literature reviews have been published in recent times on the applications of optical fibre sensors in smart structures research, these have mainly focused on the use of conventional glass-based fibres. The availability of inexpensive, rugged, and large-core plastic-based optical fibres has resulted in growing interest amongst researchers in their use as low-cost sensors in a variety of areas including chemical sensing, biomedicine, and the measurement of a range of physical parameters. The sensing principles used in plastic optical fibres are often similar to those developed in glass-based fibres, but the advantages associated with plastic fibres render them attractive as an alternative to conventional glass fibres, and their ability to detect and measure physical parameters such as strain, stress, load, temperature, displacement, and pressure makes them suitable for structural health monitoring (SHM) applications. Increasingly their applications as sensors in the field of structural engineering are being studied and reported in literature. This article will provide a concise review of the applications of plastic optical fibre sensors for monitoring the integrity of engineering structures in the context of SHM.

A simple mechanical model for elastomeric bearings used in base isolation

The effect of weak shear rigidity is important in the analysis of elastomeric bearings used for base isolation of buildings. In particular, the buckling load is low and the P-Δ effect is significant, as compared to standard columns neglecting shear deformations. In this paper, a simple mechanical model accounting for both shear and flexural deformations is proposed to treat the P-Δ effect of elastomeric isolation bearings. With adequate choice of the model parameters, the simplified model agrees very well with an exact model in both static and dynamic cases. When applied to the experimental results for some natural rubber bearings, the model is found to describe with good accuracy the effects of axial load on the dynamic stiffness, the damping factor and the height reduction of bearings. A brief study of the post-buckling behaviour of elastomeric bearings using the simplified model is also presented.

System identification of linear MDOF structures under ambient excitation

This paper introduces the eigenspace structural identification technique for tall buildings subjected to ambient excitations that are stationary and where only the response time histories are measured. Based on the forward innovation model of the Kalman filter sequence, the actual response can be constructed as a function of the measured response time history with contamination of either displacement or velocity. The response time history is decomposed into subspace matrices using QR decomposition and Quotient Singular Value Decomposition (QSVD) techniques. These are then substituted into the least-square formulation to obtain the solution which is non-unique. Similarity transformation is applied to arrive at the desired solution employing the fact that eigenvalues of self-similar systems are identical. The advantages of this eigenspace technique are that it is non-iterative, initial estimates of the parameters to the identified are not required, well-established numerical algorithm of the decomposition techniques employed are available, and the method can handle MDOF systems efficiently. Copyright

Numerical and Experimental Studies of a Substructural Identification Strategy

For structural health monitoring it is impractical to identify a large structure with complete measurement due to limited number of sensors and difficulty in field instrumentation. Furthermore, it is not desirable to identify a large number of unknown parameters in a full system because of numerical difficulty in convergence. A novel substructural strategy was presented for identification of stiffness matrices and damage assessment with incomplete measurement. The substructural approach was employed to identify large systems in a divide-and-conquer manner. In addition, the concept of model condensation was invoked to avoid the need for complete measurement, and the recovery process to obtain the full set of parameters was formulated. The efficiency of the proposed method is demonstrated numerically through multi-storey shear buildings subjected to random force. A fairly large structural system with 50 DOFs was identified with good results, taking into consideration the effects of noisy signals and the limited number of sensors. Two variations of the method were applied, depending on whether the sensor could be repositioned. The proposed strategy was further substantiated experimentally using an eight-storey steel plane frame model subjected to shaker and impulse hammer excitations. Both numerical and experimental results have shown that the proposed substructural strategy gave reasonably accurate identification in terms of locating and quantifying structural damage.

Compression stiffness of bonded square layers of nearly incompressible material

Abstract Two series solutions are presented for computing the compression stiffness of a thin square layer of nearly incompressible material bonded to and compressed between two rigid plates. The objective is to examine the validity of the four ad hoc assumptions made in a commonly adopted simple solution that deals with only the hydrostatic pressure. Eliminating the two stress assumptions, the first solution retains two remaining kinematic assumptions that the ‘bulge’ shape is parabolic and horizontal plane sections remain plane. The second solution further eliminates the first kinematic assumption. An error estimate resulting from the last assumption is shown to be small for rubber layers used in typical base isolation bearings. Comparison of these three solutions reveals that the parabolic bulge shape is indeed a realistic assumption. The convergence of the series solutions is shown to be very rapid. The ‘pressure’ solution is found to be a good approximation for a nearly incompressible material. Nevertheless, the first solution is recommended since it involves less assumptions and yet it is no more complicated in its final form. The influences of compressibility and shape factor on the compression stiffness are also illustrated.

Analytical solution for compression stiffness of bonded rectangular layers

It is well known that the compression stiffness of bonded layers increases due to the restricted lateral expansion if Poissons ratio is near 0.5. While analytical solutions have previously been obtained for circular, infinite-strip and square shapes, this paper presents the first analytical attempt for bonded rectangular layers. On the basis of two kinematic assumptions and by means of variable transformation, the governing equations are derived. The double series approach provides a direct means of computation with second-order convergence. The solutions agree well with the published results for special cases of square layers and infinite strips, and with finite element results for rectangular layers. Besides illustrating the importance of including the compressibility effect, the numerical study shows that the effect of length-to-width ratio is significant on the effective compression modulus of rectangular pads.

Dynamic analysis of large displacement cable motion with experimental verification

Cables used in engineering applications may undergo large displacement motion when subjected to dynamic loads, particularly for cables with relatively low tension. As cables are much weaker in the out-of-plane motion than the in-plane motion, three-dimensional dynamic analysis is often necessary even when the excitation contains only a small out-of-plane component. This paper presents the dynamic analysis of three-dimensional cable motion, accounting for axial, flexural and torsional deformations as well as geometric non-linearity due to large displacements and rotations. The comprehensive study covers analytical formulation, numerical strategy based on an iterative finite difference scheme, and experimental verification by means of shaking table tests. A specific problem of cable motion due to support excitation is used to illustrate the asymmetry and sensitivity of the dynamic tension response associated with geometric non-linearity of large displacement cable motion. The shaking table tests validate the accuracy of the numerical results obtained for both two-dimensional and three-dimensional cases.

Structural Damage Detection by Integrating Data Fusion and Probabilistic Neural Network

Over the past two decades, multi-sensor data fusion method has attracted increasing attention to structural health monitoring due to its inherent capabilities in extracting information from different sources and integrating them into a consistent, accurate and intelligible data set. Meanwhile, since the probabilistic neural network (PNN) describes measurement data in a Bayesian probabilistic approach, it has been successfully applied to structural damage detection (Jiang et al. 2004; Klenke et al. 1996; Ko et al. 1999; Ni et al. 2001). In order to make full use of multi-sensor data (or information) from multi-resource and to improve the diagnosis accuracy of the health conditions for complex structures, it is advisable to combine these methods and exploit their individual advantages. In this paper, a 5-phase complex structural damage detection method by integrating data fusion and PNN is developed and implemented. The proposed method is then applied to damage detection and identification of two simulation examples. The result shows that the proposed method is feasible and effective for damage identification.