Review on the new development of vibration-based damage identification for civil engineering structures: 2010-2019

A novel unscented Kalman filter for recursive state-input-system identification of nonlinear systems

Frequent water pollution incidents have occurred recently, leading to severe damages, economic loss, and long-lasting society impact. Therefore, installation of water quality monitoring sensors in water distribution system (WDS) has been advocated as a viable solution to enable real-time pollution detection and thus the mitigation of the risks associated with catastrophic contamination incidents. Given the significant cost of placing sensors at all locations in a network, a critical issue is where to deploy the sensors within the distribution system while the network still gets covered. Although there exist a significant number of articles on sensor placement, WDS for contamination detection is unique comparing to other networks such as power grids, road networks, structural networks and microwave radio networks. In this paper, we conduct a comprehensive literature survey on the sensor placement problem for contamination detection in WDS, with a special focus on optimization strategies and framework. Current challenges, issues, and research directions are also identified and discussed.

A survey on sensor placement for contamination detection in water distribution systems

Data fusion based EKF-UI for real-time simultaneous identification of structural systems and unknown external inputs

Outlier detection and robust regression for correlated data

Summary

In the traditional extended Kalman filter approach, unknown structural parameters are included in the extended state vector. Then, the sizes of the extended state vector and the corresponding state equation are quite large, and the state equation is highly nonlinear with respect to the extended state vector. This may cause identification divergent for a large number of unknown parameters. Also, such strategy requires large computational effort and storage capacities, which is not appropriate for intelligent structural damage detection implemented by smart sensors with microprocessors. In this paper, an algorithm based on a two-step Kalman filter approach is proposed to remove the aforementioned drawbacks of the traditional extended Kalman filter. In the first step, recursive estimation of structural state vector is derived by Kalman filter with assumed structural parameters. In the second step, structural parameters and the updated structural state vector are estimated by the Kalman filter and the recursive estimation in the first step. Thus, the number of estimated variables in each step is reduced, which reduces the computational effort and storage requirements. This superiority is important for intelligent structural damage detection implemented by smart sensor in wireless sensor network. The proposed algorithm is first validated by numerical simulations results of structural damage detection of the phase-I 3-D ASCE benchmark building for structural health monitoring, a 30-story shear building with minor damage, and an experimental test of damage detection of a lab multistory frame model. Then, it is applied to structural damage detection of a lab multistory model-employed smart sensors embedded with the proposed algorithm. Copyright © 2014 John Wiley & Sons, Ltd.

An algorithm based on two-step Kalman filter for intelligent structural damage detection

Previous damage detection of frame structures mainly focuses on the detection of beam and column element damage. It has been shown that beam-column joints in frame structures are more susceptible to damage than the other members in the structure. Joint damage may be represented by the reduction of beam-column connection rigidity. Therefore, damage detection of a frame structure with joint damage includes the identification of joint connection stiffness in additional to those of beam and column stiffness, which involves the difficulty of identifying a large number of unknown structural parameters. In this paper, an algorithm based on a two-step Kalman filter approach is presented for the damage detection of frame structures with joint damage under earthquake excitation using partial measurements of structural acceleration responses. Recursive solutions for unknown structural parameters and structural state vector are derived by a two-step Kalman filter, respectively. Therefore, the number of unknown variab...

Damage Identification of Frame Structures with Joint Damage under Earthquake Excitation

Extended Kalman Filter (EKF) has been widely used for structural identification and damage detection. However, conventional EKF approaches require that external excitations are measured. Also, in the conventional EKF, unknown structural parameters are included as an augmented vector in forming the extended state vector. Hence the sizes of extended state vector and state equation are quite large, which suffers from not only large computational effort but also convergence problem for the identification of a large number of unknown parameters. Moreover, such approaches are not suitable for intelligent structural damage detection due to the limited computational power and storage capacities of smart sensors. In this paper, a two-stage and two-step algorithm is proposed for the identification of structural damage as well as unknown external excitations. In stage-one, structural state vector and unknown structural parameters are recursively estimated in a two-step Kalman estimator approach. Then, the unknown external excitations are estimated sequentially by least-squares estimation in stage-two. Therefore, the number of unknown variables to be estimated in each step is reduced and the identification of structural system and unknown excitation are conducted sequentially, which simplify the identification problem and reduces computational efforts significantly. Both numerical simulation examples and lab experimental tests are used to validate the proposed algorithm for the identification of structural damage as well as unknown excitations for structural health monitoring.

A two-stage and two-step algorithm for the identification of structural damage and unknown excitations: numerical and experimental studies

To better understand the effect of initial static loading on the dynamic shear performance of concrete deep beams reinforced with basalt fiber–reinforced polymer (BFRP) bars, a three-dimensional mesoscale numerical model was established considering the strain rate effect of each material and the interaction between concrete and BFRP bars. The effect of the initial static loading on the damage evolution and failure mechanism of the concrete deep beams (shear span ratio λ = 1.0) reinforced with BFRP bars (BFRP-RC deep beams) with different stirrup ratios was analyzed. The results prove that: (1) the shear capacity, failure mode, and deformation capacity of BFRP-RC deep beams have a strain rate effect, that is, the shear capacity, deformation capacity, and damage degree of beams increase with the increase of strain rate; (2) the increase of strain rate and stirrup ratio can improve the shear capacity and deformation capacity of beams, while the strain rate plays the leading role; and (3) the shear capacity, deformation capacity, and damage degree of BFRP-reinforced concrete deep beam decrease as the initial static loading increases under different strain rates. However, the increase in strain rate will weaken the effect of initial static loading on the dynamic performance of the beams. 

Feng Chen

Papers

An algorithm based on two-step Kalman filter for intelligent structural damage detection

Damage Identification of Frame Structures with Joint Damage under Earthquake Excitation

A two-stage and two-step algorithm for the identification of structural damage and unknown excitations: numerical and experimental studies

Effect of Initial Static Loadings on Dynamic Shear Performance of BFRP-Reinforced Concrete Deep Beams