Yuequan Bao

Fractal Dimension-Based Damage Detection Method for Beams with a Uniform Cross-Section

: For civil structures, damage usually occurs in localized areas. As fractal dimension (FD) analysis can provide insight to local complexity in geometry, a damage detection approach based on Katzs estimation of the FD measure of displacement mode shape for homogeneous, uniform cross-sectional beam structures is proposed in this study. An FD-based index for damage localization (FDIDL) is developed utilizing the difference of angles of sliding windows between two successive points, which is expressed in FD. To improve robustness against noise, FDIDL is calculated using multisliding windows. The influence of the spatial sampling interval length and the number of 2-sampling sliding windows on sensitivity to damage and robustness against noise is investigated. The relationship between the angle expressed in FD and the modal strain energy is established and thereby an FD-based index for the estimation of damage extent (FDIDE) is presented. The two damage indices are applied to a simply supported beam to detect the simulated damage in the beam. The results indicate that the proposed FDIDL can locate the single or multiple damages, and FDIDE can reliably quantify the damage extent. The optimal spatial sampling interval and the number of sliding windows are investigated. Furthermore, the simulation with measurement noise is carried out to demonstrate the effectiveness and robustness of the two defined FD-based damage indices. Finally, experiments are conducted on simply supported steel beams damaged at different locations. It is demonstrated that the proposed approach can locate the damages to a satisfactory precision.

Compressive sampling–based data loss recovery for wireless sensor networks used in civil structural health monitoring

In a wireless sensor network, data loss often occurs during the data transmission between the wireless sensor nodes and the base station. In the wireless sensor network applications for civil structural health monitoring, the errors caused by data loss inevitably affect the data analysis of the structure and subsequent decision making. This article explores a novel application of compressive sampling to recover the lost data in a wireless sensor network used in structural health monitoring. The main idea in this approach is to first perform a linear projection of the transmitted data x onto y by a random matrix and subsequently to transmit the data y to the base station. The original data x are then reconstructed on the base station from the data y using the compressive sampling method. The acceleration time series collected by the field test on the Jinzhou West Bridge and the Structural Health Monitoring System on the National Aquatics Center in Beijing are employed to validate the accuracy of the proposed data loss recovery approach. The results indicate that good recovery accuracy can be obtained if the original data have a sparse characteristic in some orthonormal basis, whereas the recovery accuracy is degraded when the original data are not sparse in the orthonormal basis.

Embedding Compressive Sensing-Based Data Loss Recovery Algorithm Into Wireless Smart Sensors for Structural Health Monitoring

Lossy transmission is a common problem for monitoring systems based on wireless sensors. Reliable communication protocols, which enhance communication reliability by repetitively transmitting unreceived packets, is one approach to tackle the problem of data loss. An alternative approach allows data loss to some extent and seeks to recover the lost data from an algorithmic point of view. Compressive sensing (CS) provides such a data loss recovery technique. This technique can be embedded into smart wireless sensors and effectively increases wireless communication reliability without retransmitting the data; the promise of this approach is to reduce communication and thus power savings. The basic idea of CS-based approach is that, instead of transmitting the raw signal acquired by the sensor, a transformed signal that is generated by projecting the raw signal onto a random matrix, is transmitted. Some data loss may occur during the transmission of this transformed signal. However, according to the theory of CS, the raw signal can be effectively reconstructed from the received incomplete transformed signal given that the raw signal is compressible in some basis and the data loss ratio is low. Specifically, this paper targets to provide accurate compensation for stationary and compressible acceleration signals obtained from structural health monitoring (SHM) systems with data loss ratio below 20%. This CS-based technique is implemented into the Imote2 smart sensor platform using the foundation of Illinois Structural Health Monitoring Project Service Tool-suite. To overcome the constraints of limited onboard resources of wireless sensor nodes, a method called random demodulator (RD) is employed to provide memory and power efficient construction of the random sampling matrix. Adaptation of RD sampling matrix is made to accommodate data loss in wireless transmission and meet the objectives of the data recovery. The embedded program is tested in a series of sensing and communication experiments. Examples and parametric study are presented to demonstrate the applicability of the embedded program as well as to show the efficacy of CS-based data loss recovery for real wireless SHM systems.

Dempster–Shafer evidence theory approach to structural damage detection

In this study, the Dempster–Shafer (D–S) evidence theory-based approach for structural damage detection is presented. First, the damage basic probability assignment (BPA) function of substructures using each data set measured from the monitored structure is calculated. Then, the D–S evidence theory is employed to combine the individual damage BPAs in order to reach the final damage detection results. To reduce the computational cost of this method when used in complex structures, the preliminary damage range is first localized by modal strain energy method. An experimental investigation on a 20-bay rigid truss structure is carried out to illustrate the identified ability of the proposed approach with considering the uncertainty of model and measurement noise. The results indicate that the damage detection results obtained by combining the damage BPAs from each test data are better than the individual results obtained by each test data separately.

An Approach of Reliable Data Transmission With Random Redundancy for Wireless Sensors in Structural Health Monitoring

Lossy transmission is a common problem suffered from monitoring systems based on wireless sensors. Though extensive works have been done to enhance the reliability of data communication in computer networks, few of the existing methods are well tailored for the wireless sensors for structural health monitoring (SHM). These methods are generally unsuitable for resource-limited wireless sensor nodes and intensive data SHM applications. In this paper, a new data coding and transmission method is proposed that is specifically targeted at the wireless SHM systems deployed on large civil infrastructures. The proposed method includes two coding stages: 1) a source coding stage to compress the natural redundant information inherent in SHM signals and 2) a redundant coding stage to inject artificial redundancy into wireless transmission to enhance the transmission reliability. Methods with light memory and computational overheads are adopted in the coding process to meet the resource constraints of wireless sensor nodes. In particular, the lossless entropy compression method is implemented for data compression, and a simple random matrix projection is proposed for redundant transformation. After coding, a wireless sensor node transmits the same payload of coded data instead of the original sensor data to the base station. Some data loss may occur during the transmission of the coded data. However, the complete original data can be reconstructed losslessly on the base station from the incomplete coded data given that the data loss ratio is reasonably low. The proposed method is implemented into the Imote2 smart sensor platform and tested in a series of communication experiments on a cable-stayed bridge. Examples and statistics show that the proposed method is very robust against the data loss. The method is able to withstand the data loss up to 30% and still provides lossless reconstruction of the original sensor data with overwhelming probability. This result represents a significant improvement of data transmission reliability of wireless SHM systems.

Structural damage identification based on substructure sensitivity and l1 sparse regularization

Sparsity constraints are now very popular to regularize inverse problems in the field of applied mathematics. Structural damage identification is a typical inverse problem of structural dynamics and Structural damage is a spatial sparse phenomenon, i.e., structural damage occurs, only part of elements or substructures are damaged. In this paper, a structural damage identification method based on the substructure-based sensitivity analysis and the sparse constraints regularization is proposed. Substructure sensitivity analysis, the establishment of structural damage stiffness parameter variation and change of modal parameters of linear equations between the measured degrees of freedom is limited, the equations for a morbid equation. The introduction of structural damage sparsity conditions, to minimize the l1 norm optimization solution. The numerical example of the 20 bay-truss structure with considering measurement noise, incomplete of measurements and multi-damage cases are carried out. The effects of number sensor and layout to the identification results are also investigated. The results indicated that the damage locations and extents can be effectively identified by the proposed method. Additionally, the sensor location can be random arrangement, which has great significance to the sensor placement of the actual structural health monitoring because robust structural damage identification also can be obtained even a few of sensor are failure.

A Study on Data Loss Compensation of WiFi-Based Wireless Sensor Networks for Structural Health Monitoring

In wireless data transmissions processes, data loss is an important factor that reduces the robustness of wireless sensor networks (WSNs). In many practical engineering applications, data loss compensation algorithms are then required to maintain the robustness, and such algorithms are typically based on compressive sensing with a large memory of microcontrollers needed. This paper presents an improved algorithm, based on random demodulator, to overcome the difficulty of microcontroller-dependence in the traditional data loss algorithms. The newly developed algorithm demonstrates the following advantages: low space complexity; low floating-point calculations; and low time complexity. Therefore, it is more suitable to be embedded into ordinary nodes, comparing with the traditional algorithms. In this paper, a WSN based on WiFi is also developed for verifying the effectiveness and feasibility of the proposed algorithm. Field experiment on Xinghai Bay Bridge is done. Experimental results show that the WSN works properly and steady. Moreover, the data loss can be compensated effectively and efficiently through the use of the present algorithm.

Investigation of Temperature Effects on Modal Parameters of the China National Aquatics Center

For the China National Aquatics Center, this paper reports an investigation into the effects of temperature change on modal parameters. Both analytical and field monitoring approaches were involved. Temperature-induced variations of structural member modulus of elasticity, internal forces and changes to boundary conditions were regarded as the essential factors influencing modal parameters. The contribution of these three effects on modal parameters for both uniform and non-uniform temperature distributions over the structure was investigated. It is concluded that temperature-induced internal forces affect the modal parameter values to greater extent than temperature-induced modulus of elasticity. The relationships between temperature and variation of modal parameters are different in the case of uniform and non-uniform temperature distributions. The analytical results were validated by using the acceleration and temperature measurements supplied by the China National Aquatics Center field monitoring system.

Sparse representation for Lamb-wave-based damage detection using a dictionary algorithm

&NA; Lamb waves are being investigated extensively for structural health monitoring (SHM) because of their characteristics of traveling long distances with little attenuation and sensitivity to minor local damage in structures. However, Lamb waves are dispersive, which results in the complex overlap of waveforms in the damage detection applications of the SHM community. This paper proposes a sparse representation strategy based on an Symbol‐norm optimization algorithm for guided‐Lamb‐wave‐based inspections. A comprehensive dictionary is designed containing various waveforms under diverse conditions so that the received waveform can be decomposed into a spatial domain for the identification of damage location. Furthermore, the Symbol‐norm optimization algorithm is employed to pursue the sparse solution related to the physical damage location. The functionality of the created dictionary is validated by both metal beam and composite wind turbine experiments. The results indicate a great potential for the proposed sparse representation using a dictionary algorithm, which provides an effective alternative approach for damage detection. Symbol. No caption available.

Computer vision and deep learning–based data anomaly detection method for structural health monitoring:

The widespread application of sophisticated structural health monitoring systems in civil infrastructures produces a large volume of data. As a result, the analysis and mining of structural health monitoring data have become hot research topics in the field of civil engineering. However, the harsh environment of civil structures causes the data measured by structural health monitoring systems to be contaminated by multiple anomalies, which seriously affect the data analysis results. This is one of the main barriers to automatic real-time warning, because it is difficult to distinguish the anomalies caused by structural damage from those related to incorrect data. Existing methods for data cleansing mainly focus on noise filtering, whereas the detection of incorrect data requires expertise and is very time-consuming. Inspired by the real-world manual inspection process, this article proposes a computer vision and deep learning–based data anomaly detection method. In particular, the framework of the proposed method includes two steps: data conversion by data visualization, and the construction and training of deep neural networks for anomaly classification. This process imitates human biological vision and logical thinking. In the data visualization step, the time series signals are transformed into image vectors that are plotted piecewise in grayscale images. In the second step, a training dataset consisting of randomly selected and manually labeled image vectors is input into a deep neural network or a cluster of deep neural networks, which are trained via techniques termed stacked autoencoders and greedy layer-wise training. The trained deep neural networks can be used to detect potential anomalies in large amounts of unchecked structural health monitoring data. To illustrate the training procedure and validate the performance of the proposed method, acceleration data from the structural health monitoring system of a real long-span bridge in China are employed. The results show that the multi-pattern anomalies of the data can be automatically detected with high accuracy.