Hassene Hasni

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

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

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

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.

A novel self-powered approach for structural health monitoring

This computational simulation study presents an innovative approach for structural damage detection in “smart” civil infrastructure systems. The proposed approach is predicated upon the utilization of the compressed data stored in memory chips of a newly developed self-powered wireless sensor. An efficient data interpretation system, integrating aspects of the finite element method (FEM) and probabilistic neural networks (PNN) based on Bayesian decision theory, is developed for damage detection. Several features extracted from the cumulative limited static strain data are used as damage indicator variables. The efficiency of the method is tested and evaluated for the complicated case of a bridge gusset plate. The gusset plate structure is analysed via 3D FE models. A general scheme is presented for finding the optimal number of data acquisition points (sensors) on the structure and the associated optimal locations, taking into account the influence of sensor sparsity and the level of data corruption due to noise.

Quasi-self-powered Infrastructural Internet of Things: The Mackinac Bridge Case Study

Autonomous, continuous and long-term monitoring systems are required to prognosticate failures in civil infrastructures due to material fatigue or extreme events like earthquakes. While current battery-powered wireless sensors can evaluate the condition of the structure at a given instant of time, they require frequent replacement of batteries due to the need for continuous or frequent sampling. On the other hand, self-powered sensors can continuously monitor the structural condition without the need for any maintenance; however, the scarcity of harvested power limits the range at which the sensors could be wirelessly interrogated. In this paper, we propose a quasi-self-powered sensor that combines the benefits of self-powered sensing and with the benefits of battery-powered wireless transmission. By optimizing both of the functionalities, a complete sensor system can be designed that can continuously operate between the structures maintenance life-cycles and can be wirelessly interrogated at distances that obviates the need for taking the structure out-of-service. As a case study, in this paper we present the design considerations involved in prototyping quasi-self-powered sensors for deployment on the Mackinac Bridge in northern Michigan, with a target operational life span greater than 20 years.

Damage Detection in Pavement Structures Using Self-powered Sensors

This paper presents a new approach for the continuous health monitoring of asphalt concrete pavements using self-powered wireless sensors. Numerical and experimental studies were carried out to evaluate the damage detection performance of the proposed self-sustained sensing system. A three-dimensional finite element analysis was performed to obtain the pavement responses under moving tire loading. Damage was introduced as bottom-up fatigue cracks at the bottom of the asphalt layer. Thereafter, features extracted from the simulated dynamic strain data for a number of sensing nodes were used to detect damage. Laboratory tests were carried out on an asphalt concrete specimen in three point bending mode to verify the sensor response. The results indicate that the proposed method is effective in detecting different damage states including crack propagation.