Guided-wave-based method for concrete de-bonding damage identification using DISC

Guided-wave-based structural damage identification techniques have received more and more attention in the civil engineering community. They not only have the capability of detecting smaller damages on a structure than vibration-based methods, but also can cover a relatively larger magnitude, compared with other traditional non-destructive evaluation techniques. To realize damage identification, features usually need to be extracted from the time domain responses. This is achievable for homogeneous materials, including steel and aluminum. But for composite materials, such as concrete, the features are usually very difficult to be extracted, because of the existence of small aggregates and the nature of uneven material properties which generate multiple reflections. It is very difficult to simulate the time domain responses and to identify damages by using time domain responses directly for such random material. Recently, a new damage identification scheme is proposed, named as DISC (Damage Identification based on Sparse Coding). This method is essentially a pattern recognition technique, which avoids the traditional fixed transform process but takes advantage of the existing data by dictionary learning techniques. This paper will review the DISC method and then apply it to identification of de-bonding damage in concrete beam using guided wave test data. The results will demonstrate the effectiveness of the DISC methodology

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Metal-polymer functionally graded materials for removing guided wave reflections at beam end boundaries

This paper investigates the potential of a metal-polymer functionally graded material (FGM) to remove beam end boundary wave reflections that produce complicated interference patterns in the response signals used for guided wave damage identification methodologies. The metal-polymer FGM matches the material properties to a metal beam for total wave transmission on one side and is continuously graded to a viscoelastic polymer on the other side. An Aluminium-Polycarbonate (Al-PC) FGM was fabricated and characterised using microscopy, hardness testing and through-transmission ultrasonics to verify the continuous gradient. Measurements of guided waves on an aluminium beam attached to the FGM on one end show reduction in boundary wave reflections that varies with wave frequency. A damaged aluminium beam attached with the FGM produced promising improvements in a damage identification system

Body randomization reduces the sim-to-real gap for compliant quadruped locomotion

Designing controllers for compliant, underactuated robots is challenging and usually requires a learning procedure. Learning robotic control in simulated environments can speed up the process whilst lowering risk of physical damage. Since perfect simulations are unfeasible, several techniques are used to improve transfer to the real world. Here, we investigate the impact of randomizing body parameters during learning of CPG controllers in simulation. The controllers are evaluated on our physical quadruped robot. We find that body randomization in simulation increases chances of finding gaits that function well on the real robot

An acoustic emission approach to the structural health monitoring of historical metallic tie-rods

The application of Non-Destructive Testing and Structural Health Monitoring systems in historical buildings is of great interest due to the need to guarantee safety and conservation. The present memory focuses on the case study of the historical wrought iron tie-rods of Duomo di Milano, Italy. In recent years, two of these elements presented critical failures. Consequently, a monitoring methodology based on acoustic emission was defined. First, the fracture toughness of wrought iron was characterized by employing standard small-scale specimens taken from one of the failed tie-rods. Meanwhile, acoustic emission was acquired to define a methodology for detecting and localizing the damage events, separating those due to background noise by applying suitable pattern recognition algorithms. Subsequently, a tensile test was performed on a full-scale section of the same tie-rod. Before and after the test, phased-array ultrasonic testing and magnetic particle inspections were carried out to identify and map defects and their possible development due to load application. Finally, it was possible to conclude that magnetic inspections allow identifying the presence of surface defects effectively, phased-array testing estimates the geometry of the defect accurately, and acoustic emission is a promising technique for monitoring the structural integrity of historical metallic tie-rods

Nondestructive Testing (NDT)

The aim of this book is to collect the newest contributions by eminent authors in the field of NDT-SHM, both at the material and structure scale. It therefore provides novel insight at experimental and numerical levels on the application of NDT to a wide variety of materials (concrete, steel, masonry, composites, etc.) in the field of Civil Engineering and Architecture

Towards Interpretable Machine Learning for Automated Damage Detection Based on Ultrasonic Guided Waves

Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based on an ML toolbox for industrial condition monitoring. The toolbox combines multiple complementary algorithms for feature extraction and selection and automatically chooses the best combination of methods for the dataset at hand. Here, this toolbox is applied to a guided wave-based SHM dataset for varying temperatures and damage locations, which is freely available on the Open Guided Waves platform. A classification rate of 96.2% is achieved, demonstrating reliable and automated damage detection. Moreover, the ability of the ML model to identify a damaged structure at untrained damage locations and temperatures is demonstrated