Machine learning at the interface of structural health monitoring and non-destructive evaluation

While both non-destructive evaluation (NDE) and structural health monitoring (SHM) share the objective of damage detection and identification in structures, they are distinct in many respects. This paper will discuss the differences and commonalities and consider ultrasonic/guided-wave inspection as a technology at the interface of the two methodologies. It will discuss how data-based/machine learning analysis provides a powerful approach to ultrasonic NDE/SHM in terms of the available algorithms, and more generally, how different techniques can accommodate the very substantial quantities of data that are provided by modern monitoring campaigns. Several machine learning methods will be illustrated using case studies of composite structure monitoring and will consider the challenges of high-dimensional feature data available from sensing technologies like autonomous robotic ultrasonic inspection. This article is part of the theme issue ‘Advanced electromagnetic non-destructive evaluation and smart monitoring’

Microwire-Based Sensor Array for Measuring Wheel Loads of Vehicles

This article belongs to the Special Issue Magnetic Sensing Technology, Materials and ApplicationsIn this paper, a magnetic microwire-based sensor array embedded under the pavement is proposed as a weighing system at customs ports of entry. This sensor is made of a cementitious material suitable for embedding within the core of concrete structures prior to curing. The objective of this research is to verify the feasibility of stress monitoring for concrete materials using an array of cement-based stress/strain sensors that have been developed using the magnetic sensing property of an embedded microwire in a cement-based composite. Test results for microwire-based sensors and gauge sensors are compared. The strain sensitivity and their linearity are investigated through experimental testing under compressive loadings. Sensors made of these materials can be designed to satisfy specific needs and reduce costs in the production of sensor aggregates with improved coupling performance, thus avoiding any disturbance to the stress state.This research was funded by the Ministry of Higher Education, Science and Technology of the Dominican Republic (2015 FONDOCyT program) and Ministerio de Economia y Competitividad (MINECO) of Spain under projects Call Retos-Colaboracion 2015 [RTC-2015-3185-4 (MAPMIT)] and Call Retos Investigacion [BIA2016-77992-R (AEI/FEDER, UE)]. Both projects were co-funded by the European Union through FEDER funds under the objective of promoting the technological development, innovation, and high-quality research. CSIC supported this project under Call ICOOP 2017 [COOPB20293]. This work was also supported by Spanish MCIU under PGC2018-099530-B-C31 (MCIU/AEI/FEDER, UE) and by the Government of the Basque Country under PIBA 2018-44 and Elkartek (RTM 4.0) projects and by the University of Basque Country under the scheme of "Ayuda a Grupos Consolidados" (Ref.: GIU18/192)

Electromagnetic Sensing Techniques for Non-Destructive Diagnosis of Civil Engineering Structures

Environmental policy & protocol

Non-destructive Testing in Civil Engineering

This Special Issue, entitled “Non-Destructive Testing in Civil Engineering”, aims to present to interested researchers and engineers the latest achievements in the field of new research methods, as well as the original results of scientific research carried out with their use—not only in laboratory conditions but also in selected case studies. The articles published in this Special Issue are theoretical–experimental and experimental, and also show the practical nature of the research. They are grouped by topic, and the main content of each article is briefly discussed for your convenience. These articles extend the knowledge in the field of non-destructive testing in civil engineering with regard to new and improved non-destructive testing (NDT) methods, their complementary application, and also the analysis of their results—including the use of sophisticated mathematical algorithms and artificial intelligence, as well as the diagnostics of materials, components, structures, entire buildings, and interesting case studies