Showing papers by "Amir H. Alavi published in 2017"

An energy harvesting solution based on the post-buckling response of non-prismatic slender beams

Abstract: Systems based on post-buckled structural elements have been extensively used in many applications such as actuation, remote sensing and energy harvesting thanks to their efficiency enhancement. The post-buckling snap- through behavior of bilaterally constrained beams has been used to create an efficient energy harvesting mechanism under quasi-static excitations. The conversion mechanism has been used to transform low-rate and low-frequency excitations into high-rate motions. Electric energy can be generated from such high-rate motions using piezoelectric transducers. However, lack of control over the post-buckling behavior severely limits the mechanism’s efficiency. This study aims to maximize the levels of the harvestable power by controlling the location of the snapping point along the beam at different buckling transitions. Since the snap-through location cannot be controlled by tuning the geometry properties of a uniform cross-section beam, non-uniform cross sections are examined. An energy-based theoretical model is herein developed to predict the post-buckling response of non-uniform cross-section beams. The total potential energy is minimized under constraints that represent the physical confinement of the beam between the lateral boundaries. Experimentally validated results show that changing the shape and geometry dimensions of non- uniform cross-section beams allows for the accurate control of the snap-through location at different buckling transitions. A 78.59% increase in harvested energy levels is achieved by optimizing the beam’s shape.

Structural health monitoring using a hybrid network of self-powered accelerometer and strain sensors

Abstract: This paper presents a structural damage identification approach based on the analysis of the data from a hybrid network of self-powered accelerometer and strain sensors. Numerical and experimental studies are conducted on a plate with bolted connections to verify the method. Piezoelectric ceramic Lead Zirconate Titanate (PZT)-5A ceramic discs and PZT-5H bimorph accelerometers are placed on the surface of the plate to measure the voltage changes due to damage progression. Damage is defined by loosening or removing one bolt at a time from the plate. The results show that the PZT accelerometers provide a fairly more consistent behavior than the PZT strain sensors. While some of the PZT strain sensors are not sensitive to the changes of the boundary condition, the bimorph accelerometers capture the mode changes from undamaged to missing bolt conditions. The results corresponding to the strain sensors are better indicator to the location of damage compared to the accelerometers. The characteristics of the overall structure can be monitored with even one accelerometer. On the other hand, several PZT strain sensors might be needed to localize the damage.

A new method for detection of fatigue cracking in steel bridge girders using self-powered wireless sensors

Abstract: Development of fatigue cracking is affecting the structural performance of many of welded steel bridges in the United States. This paper presents a support vector machine (SVM) method for the detection of distortion-induced fatigue cracking in steel bridge girders based on the data provided by self-powered wireless sensors (SWS). The sensors have a series of memory gates that can cumulatively record the duration of the applied strain at a specific threshold level. Each sensor output has been characterized by a Gaussian cumulative density function. For the analysis, extensive finite element simulations were carried out to obtain the structural response of an existing highway steel bridge girder (I-96/M- 52) in Webberville, Michigan. The damage states were defined based on the length of the crack. Initial damage indicator features were extracted from the sensor output distribution at different data acquisition nodes. Subsequently, the SVM classifier was developed to identify multiple damage states. A data fusion model was proposed to increase the classification performance. The results indicate that the models have acceptable detection performance, specific ally for cracks larger than 10 mm. The best classification performance was obtained using the information from a group of sensors located near the damage zone.

Continuous health monitoring of asphalt concrete pavements using surface-mounted battery-free wireless sensors

Abstract: This paper presents a new surface sensing approach for health monitoring of asphalt concrete (AC) pavements utilizing a new class of self-powered wireless sensors. The proposed method was based on the interpretation of the data stored in the memory gates of the sensor. A three-dimensional finite element analysis was performed to obtain the dynamic strain at the surface of the pavement for different damage scenarios. Damage states were defined using the element weakening method. The sensor output data was generated from the time-history of the surface strains. Thereafter, the sensor data was fitted to a Gaussian mixture model (GMM) in order to define an initial damage indicator features. Finally, probabilistic neural network classification scheme was used to classify the damage states. The results indicate that the proposed method is effective in detecting and classifying bottom-up cracks in AC pavements using a surface-mounted network of sensors. Figure 1. Prototype of the SWS. ce vehicle could be effectuated using a Radio Frequency Identification (RFID) scanner to read the data stored on board the memory cells of the sensor. Previous studies on the self-powered wireless sensor showed that cracks in pavement could be detected based on the interpretation of the data of a constant injection rate class of SWS (Chatti et al., 2016). In their study, the SWS was embedded inside the asphalt layer. However, the device could be damaged and their replacement might be expensive. Therefore, placing the sensors network near the top surface of the pavement seems to be an attractive solution. In addition, for the case of a constant injection rate SWS, the data can be fitted to a cumulative density function (CDF). However, in this paper, each gate of the sensor has a specific injection rate, which makes the interpretation of the data more complicated. This study proposes a new method for pavement health monitoring based on a surface sensing approach. The proposed detection mechanism is based on integrating the finite element method (FEM) and probabilistic neural networks (PNN). Intensive finite element (FE) analysis of a moving load on a pavement section was performed to obtain a realistic response. The proposed method uses features extracted from the sensors output distributions to define initial damage indicators. Thereafter, the extracted features from the WSN were fused to increase the classification accuracy. 2 SMART SENSOR AND PROPOSED DAMAGE DETECTION SYSTEM The smart sensor is capable of continuously monitoring the strain events within the host structure. As mentioned before, the memory cells records the cumulative drop of voltage/strain at a preselected threshold level. A schematic representation of the working principle of the sensor is presented in Figure 2. Figure 2(a) represents the input signal and Figure 2(b) displays the output of the sensor. The recorded strain droppage is a function of the cumulative time intersections and the gates injection rates as follow: Sj = S0 − Igj × ∑ Ti j j=1:7 (1) Where Sj is the sensor strain at gate j, Ti j is the duration of time intersection number i at the preselected threshold j (see Figure 2), and Igj is the injection rate of gate j. The injection rates are property of the sensors and they control the strain/voltage droppage rates over time. As seen in Figure 2, there is a considerable loss of the sensed information because the data is compressed as a function of the cumulative time. Therefore, a statistical method was proposed to extract valuable features from the sensor distribution. In this paper, the output histogram is fitted to a Gaussian mixture model. GMMs are powerful tools to describe many types of data. The probability density function (PDF) of a Gaussian mixture (GM) distribution is given by the following expression: p(x) = ∑ ck √2 π σk 2 M k=1 exp [− 1 2 ( x− μk σk ) 2 ] (2)