International Journal of Structural Stability and Dynamics

The intrinsic relationship between deterministic system and stochastic system is profoundly revealed by the probability density evolution method (PDEM) with introduction of physical law into the stochastic system. On this basis, stochastic dynamic stability analysis of single-layer dome structures under stochastic seismic excitation is firstly studied via incorporating an energetic physical criterion for identification of dynamic instability of dome structures into PDEM, which yields to sample stability (stable reliability). However, dynamic instability is not identical to structural failure definitely, where strength failure can be experienced not only in the stable structure but also when the structure is out of dynamic stability. It is practically feasible to decouple the stochastic dynamic response of dome structures to be a stable one and an unstable one according to the generalized density evolution equation (GDEE). Consequently, the global failure probability can be investigated separately based on the corresponding independent stochastic response. For unstable failure probability assessment, the failure probability is the unstable probability if the dome's failure is attributed to instability, whereas inverse absorbing is firstly implemented to get rid of the stochastic response before instability and a complementary process is filled in the safe domain immediately to finally assess the probability of strength failure after dynamic instability.

This paper performs degradation assessment and safety evaluation of a concrete gravity dam at Shenwo reservoir in Liaoning province of China by using two methods, i.e. deterministic method and probabilistic method, respectively. The deterministic responses of representative sluice piers are computed through static pushover and dynamic analyses. The probabilistic uncertainty analysis, including time invariant and time variant reliability analyses, is based on finite element (FE) reliability theory. For time invariant case, the material and loading parameters are considered as random variables, including elastic modulus, Poisson's ratio, mass density and pushover forces. For time variant case, the earthquake history is simulated by using random process, and the first-order reliability method (FORM) approximation combined with Koo's analytical solution is used to compute the mean upcrossing rate for given performance functions, through which the failure probabilities are calculated. Based on the analysis results using these two methods, the safety and reliability of the dam is assessed. Furthermore, the performance assessments of the dam in its current state are compared with those in the original state (i.e. when the dam was built) to quantify its performance degradation. Finally, the assessment results of using the deterministic and probabilistic methods are compared, and the discrepancies between them are quantified and explained. The computation is based on a general FE analysis platform for earthquake engineering simulation, OpenSees.

Structural damage detection and vibration control have been actively studied in the past decades. However, in most previous investigations, these two aspects have been treated separately according to their individual primary objectives pursued. Although structural identification and model updating have been used in the adaptive control techniques, it is still necessary but challenging to study the on-line integration of structural damage detection and optimal vibration control. In this paper, a technique is proposed for such purpose. First, on-line structural parameter identification and on-line detection of the onset, locations and extents of structural damage of controlled structures with partial measurements of structural acceleration responses is studied. An algorithm is proposed based on the extended Kalman estimator/Kalman estimator and error tracking. Then, the updated structural models are on-line integrated with the instantaneous optimal control scheme to reach the goal of optimal active structural control of the undamaged/damaged structures. Numerical simulation results with different structural damage patterns illustrate the performances of the proposed technique for the on-line integration of structural damage detection and active optimal vibration control.

In this paper, an approach for detecting damage and structural modifications is implemented, based on a combination of several indicators derived from the recorded ambient dynamic structural response. In a multi-step procedure, the normalized cumulative power spectral density (NCPSD) evaluated at several structural nodes and a thereof derived "NCPSD damage index" is proposed to detect a modification of the structure. A subsequent sophisticated superposition of a series of mode shape quantities yields a "mode shape damage index" as a function of the structural coordinates that reveals the specific location of this structural modification. Furthermore, it is discussed how environmental effects from varying temperatures on natural frequencies can be substantially reduced. Therefore, long-term monitoring of the structure is required to define relations that allow translating the proposed damage parameters contaminated by temperature effects into the domain of the structure in a reference state. The efficiency of the proposed method is tested on an old iron railway bridge, subjected to a series of structural modifications, such as stepwise reducing the stiffness of local elements, adding mass and an additional flexible support. From the results of this comprehensive study it can be concluded that this nondestructive approach is a promising tool for reliable structural health monitoring (SHM).

This paper presents a novel two-stage damage detection method by integrating modal strain energy and revised particle swarm optimization (RPSO). In the first stage, the modal strain energy change ratio (MSECR) is used to roughly identify the locations of damaged elements via an appropriate MSECR threshold which is determined through parameter estimation. In the second stage, RPSO that integrates evolutionary theory with general PSO is used to precisely locate and quantify the damage with the gravity position of the selected excellent particles in the current entire population taken into consideration. Two numerical simulations and a seven-story steel frame experiment in laboratory conditions are performed to validate the proposed method, and a comparison is made between the proposed approach and existing methods. The results show that: (1) the proposed method can not only effectively locate damage, but also accurately evaluate the extent of damage. Meanwhile, it also enjoys good noise-tolerance and adaptability; (2) the damage threshold of the MSECR presented in this paper can be determined by the parameter estimation and reliability index, and then used to reduce the number of elements to be analyzed and to improve the computation efficiency in the second stage; and (3) compared with general PSO algorithm, RPSO is more efficient and robust for damage detection with a better noise-tolerance. This study shows that the proposed method can provide a reliable and fast tool to accurately identify, locate and quantify single- and multi-damage of complex engineering structures.

Traditional structural system identification and damage detection methods use vibration responses under single excitation. This paper presents an auto/cross-correlation function-based method using acceleration responses under multiple ambient white noise or impact excitations. The auto/cross-correlation functions are divided into two parts. One is associated with the structural parameters and the other with the energy of the excitation. These two parts are updated sequentially using a two-stage method. Numerical and experimental studies are conducted to demonstrate the accuracy and robustness of the proposed method. The effects of measurement noise and number of measurement points on the identification results are also studied.

This paper presents an experimental study on using an improved extended Kalman filter (EKF) to identify impact-induced structural damage. By introducing the optimization of estimated residual error into the classical EKF, this real-time approach demonstrates an excellent capability to identify the abrupt changes of structural parameters instantly and accurately. The optimization procedure is activated when a prescribed threshold is exceeded. A shaking table test of a three-story steel frame subjected to abrupt damage induced by impact load was conducted to validate the improved EKF approach. The results clearly reveal its improved performance and good anti-noise ability in identifying time-variant structural parameters.

In this study, a new ultrasonic wave based imaging techniques developed using longitudinal ultrasonic waves for detecting defects in pipeline structures. Ultrasonic waves are gaining popularity for pipeline monitoring because of its sensitivity to small defects and a long sensing range. Based on the merits of the ultrasonic waves, several research groups have developed ultrasonic wave based imaging techniques for pipeline monitoring. Conventionally, a pure torsional mode is often generated using shear-mode piezoelectric transducers or electromagnetic acoustic transducers (EMAT) and used for pipe damage detection. In this study, a new ultrasonic wave based imaging technique is developed using a longitudinal wave mode instead of the pure torsional mode. The longitudinal mode is generated using inexpensive macro fiber composite (MFC) transducers attached at one end of the pipe, eliminating the need for the shear-mode transducers or EMATs. Then, the reflections generated by the interaction of the incident longitudinal mode with a defect are measured in a pulse echo manner. Using a normal mode expansion technique, flexural modes are extracted from the reflected signals. When a defect-induced reflected wave mode is propagated back along the longitudinal direction of the pipe, its dispersive nature is minimized and best-compensated at the defection location. Therefore, by virtually propagating each defect-induced flexural mode back in the wave propagation direction, an image which visualizes the focusing of the back-propagated flexural modes can be obtained and the defect location can be identified. Numerical simulations and experimental tests are conducted to demonstrate that a wall-thinning in a steel pipe can be detected and quantified using the proposed imaging technique.

Port structures such as pile supported piers and quay walls generally face severe conditions, and deterioration of port structures is often observed. For an example, at a sheet pile quay wall, sheet pile is a structural component which is easily deteriorated by corrosion. As a result of deterioration of sheet pile, the backfilled soil would be washed out due to the tidal action. Suffusion, the phenomena of the washed out of fine particles and loosening of soil layer, is a problem to be considered in the maintenance of a quay wall. Therefore, a health monitoring method for the backfill of a sheet pile quay wall is necessary. In this study, the applicability of a surface-wave method for health monitoring of a sheet pile quay wall was examined. First, the variation of shear wave velocity in the ground due to suffusion and variation in water content is measured from bender element tests. Then, 2D finite element method (FEM) dynamic analysis was conducted to simulate a surface-wave measurement on a sheet pile quay wall. From the result of the analysis, the estimated shear wave velocity profile based on surface-wave method agrees fairly well with the assumed soil profile. Finally, surface-waves were measured at a sheet pile quay wall, and the measured results were compared with the result of Swedish weight sounding test at the site. Furthermore, the difference between the shear wave velocity profiles at ebb tide and that at high tide was successfully measured by the surface-wave method. Thus, the applicability of the surface-wave method for a sheet pile quay wall is demonstrated in this study.

Risers are the conduits between the subsea wellhead and the drilling/production platform for development, production, gas lift or water injection purposes, which are also one of the most important and the most vulnerable components for deepwater floating platforms. To address the lack of appropriate global structural health monitoring (SHM) system for deepwater risers, this paper proposes a time-frequency domain approach using a wavelet modified second order blind identification (WMSOBI) method and combined distributed force change (CDFC) index. WMSOBI provides a reliable time-frequency domain identified modal properties of riser systems, even with large damping and under-determinate conditions. In addition, CDFC index generated from modal properties extracted by WMSOBI can accurately identify the damage location and damage level for both single and multiple crack scenarios. Details of experiments conducted on suspended pipe are presented. Both numerical and experimental verification are presented to validate the effectiveness of the proposed WMSOBI/CFDC algorithm and SHM system.

The optimal placement of wireless sensors is very different from conventional wired sensor placement due to the limited transmission range of the wireless sensors. This constraint on the inter-sensor distance makes the optimization problem difficult to solve with conventional gradient-based methods. In this paper, an improved generalized genetic algorithm (GGA) based on a self-adaptive dynamic penalty function (SADPF) is proposed for the optimal wireless sensor placement (OWSP) in bridge vibration monitoring. The mathematical model of the OWSP problem is established, and it considers both the bridge vibration monitoring requirements and the constraints of the data transmission range in wireless sensor networks (WSNs). SADPF, which can automatically adjust the amount of penalization for constraint violations according to the evolution generation number and the degree of violation, is then developed so that the wireless sensor placement can be optimized using GGA. Subsequently, the GGA is improved by implementing an elite conservation strategy, a worst elimination policy and a dual-structure coding system. Finally, a numerical experiment is presented with a long-span suspension bridge to demonstrate the feasibility and efficiency of the proposed method, and some indispensible discussions are also given. The results indicate that the wireless sensor configurations that are optimized by the improved SADPF-based GGA can simultaneously meet the data transmission demands in a WSN and fulfill the requirements for structural condition assessment. The developed SADPF can minimize the influence of the limited data transmission range on the search process for the OWSP. The improved SADPF-based GGA quickly and robustly converges to the global optimal solution.

Optimal sensor placement (OSP) method plays a key role in setting up a health monitoring system for large-scale structures. This paper describes the implementation of monkey algorithm (MA) as a strategy for the optimal placement of a predefined number of sensors. To effectively maintain the population diversity while enhancing the exploitation capacities during the optimization process, a novel niching monkey algorithm (NMA) by combining the MA with the niching techniques is developed in this paper. In the NMA, the dual-structure coding method is adopted to code the design variables and a chaos-based approach instead of a pure random initialization is employed to initialize the monkey population. Meanwhile, the niche generation operation and fitness sharing mechanism are modified and incorporated to alleviate the premature convergence problem while enhancing the exploration of new search domain. In addition, to promote interactions and share the available resources, the replacement scheme is proposed and adopted among the niches. Finally, numerical experiments are conducted on a high-rise structure to evaluate the performance of the proposed NMA. It is found that the innovations in the proposed NMA can effectively improve the convergence of algorithm and generate superior sensor configurations when compared to the original MA.