3D displacement measurement model for health monitoring of structures using a motion capture system

System identification in structural engineering

Road surface conditions are continuously degrading due to meteorological conditions, ground movements and traffic that leads to the formation of defects, such as grabbing, holes, and cracks. This article presents a method to automatically distinguish images of road surfaces with defects from road surfaces without defects. This method, based on supervised learning, is generic and may be applied to all type of defects present in the images. They typically present strong textural information with patterns that show fluctuations at small scales and some uniformity at larger scales. The textural information is described by applying a large set of linear and nonlinear filters. To select the most pertinent ones for the current application, a supervised learning based on AdaBoost is performed. The whole process is tested both on a textural recognition task based on the VisTex image database and on road images collected by a dedicated road imaging system. A comparison with a recent crack detection algorithm from Oliveira and Correia demonstrates the proposed method's efficiency.

Automatic Road Defect Detection by Textural Pattern Recognition Based on AdaBoost

Continuous health monitoring of pavement systems using smart sensing technology

Velocity-Free MS/AE Source Location Method for Three-Dimensional Hole-Containing Structures

: Currently, pavement instrumentation for condition monitoring is done on a localized and short-term basis. Existing technology does not allow for continuous long-term monitoring and network level deployment. Long-term monitoring of mechanical loading for pavement structures could reduce maintenance costs, improve longevity, and enhance safety. In this article, on-going research to develop and validate a smart pavement monitoring system is described. The system mainly consists of a novel self-powered wireless sensor based on the integration of piezoelectric transduction with floating-gate injection capable of detecting, storing, and transmitting strain history for long-term monitoring and a novel passive temperature gauge. A technique for estimating full-field strain distributions using measured data from a limited number of implemented sensors is also described. The ultimate purpose is to incorporate the traffic wander effect in the fatigue prediction algorithms. Preliminary results are shown and limitations are discussed.

Toward an Integrated Smart Sensing System and Data Interpretation Techniques for Pavement Fatigue Monitoring

A novel multi-segment path analysis based on a heterogeneous velocity model for the localization of acoustic emission sources in complex propagation media.

Application of a moment tensor inversion code developed for mining-induced seismicity to fracture monitoring of civil engineering materials

The world-wide ageing and deteriorating civil infrastructure network has necessitated the development of new Structural Health Monitoring (SHM) approaches that are capable of producing quantitative feedback on fracture mechanisms in critical civil structures such as bridges in real-time. In this paper, we present a methodology that combines techniques applied in quantitative seismological analyses with the enabling capabilities of the Acoustic Emission (AE) technique in order to gain insight into ongoing fracture processes in concrete structures. The proposed methodology employs the technique of Moment Tensor Inversion (MTI), a key analysis tool in seismology, in order to develop a full understanding of the physics of the source of fracture and infer its properties. The MTI technique is simple in concept but solution accuracy is highly dependent on the sampling of the wavefield in 3-D, especially in the case where the sensors are all uniaxial. For accuracy purposes, all our experiments were performed using high-fidelity Glaser/NIST point-contact sensors that show a flat response for frequencies between 20 kHz and 1 MHz. In this paper, we provide a general description of the proposed methodology with emphasis on the so-called hybrid MTI technique. Finally, we present a set of initial experiments conducted in the laboratory using repeatable artificial AE sources and we discuss the results.

Development of seismology-based acoustic emission methods for civil infrastructure applications

Over the last few decades there has been sustained interest in learning about fracture processes to achieve a better understanding of how concrete structures behave and what leads to failure. Such understanding would greatly benefit the concrete industry as it would provide efficient engineering solutions to improve the quality, durability, strength, and behavior of concrete structures and answer more challenging questions such as how, where, and when cracks initiate and propagate. The type of cracking also matters. For example, shear-type failure happens suddenly without warning and means for early detection would be useful. Due to the complex nature of concrete, however, monitoring approaches are challenging to apply and are often subject to significant measurement noise. This PhD research investigated the feasibility of employing moment tensor inversion (MTI), a powerful quantitative seismology-based technique, combined with the capabilities of the acoustic emission (AE) technique for the quantitative monitoring of crack initiation and propagation in concrete materials. The goal was to monitor fracture mechanisms in concrete structures in real time. For this reason a hypothesis was put forth that MTI techniques applied to AE data can produce pertinent information about cracks such as location, type, orientation, and intensity, and thus can distinguish flexural from shear and mixed-mode cracks. The motivation behind using MTI techniques is the strong analogy between AE and seismic events, albeit at different scales. To test the hypothesis identified above, a methodology that capitalizes on the strengths of both the AE technique and the MTI technique was proposed and applied according to which AE waveforms recorded from concrete fracture were inverted using a MTI code, called MTI Toolbox, in order to study the sources of fracture and infer their properties. The MTI Toolbox was applied to estimate source mechanisms

Lassaad Mhamdi

Papers

Toward an Integrated Smart Sensing System and Data Interpretation Techniques for Pavement Fatigue Monitoring

A novel multi-segment path analysis based on a heterogeneous velocity model for the localization of acoustic emission sources in complex propagation media.

Application of a moment tensor inversion code developed for mining-induced seismicity to fracture monitoring of civil engineering materials

Development of seismology-based acoustic emission methods for civil infrastructure applications

Seismology-based approaches for the quantitative acoustic emission monitoring of concrete structures