This paper reviews recent advances in sensor technologies for non-destructive testing (NDT) and structural health monitoring (SHM) of civil structures. The article is motivated by the rapid developments in sensor technologies and data analytics leading to ever-advancing systems for assessing and monitoring structures. Conventional and advanced sensor technologies are systematically reviewed and evaluated in the context of providing input parameters for NDT and SHM systems and for their suitability to determine the health state of structures. The presented sensing technologies and monitoring systems are selected based on their capabilities, reliability, maturity, affordability, popularity, ease of use, resilience, and innovation. A significant focus is placed on evaluating the selected technologies and associated data analytics, highlighting limitations, advantages, and disadvantages. The paper presents sensing techniques such as fiber optics, laser vibrometry, acoustic emission, ultrasonics, thermography, drones, microelectromechanical systems (MEMS), magnetostrictive sensors, and next-generation technologies.

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A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring

ABSTRACT Carbon Fiber Reinforced Polymer (CFRP), a novel high-performance material, has found extensive use across various domains. Due to the intricate structure of CFRP and variability in its manufacturing process, such as differences in resin curing speeds or carbon fiber alignment, defects including voids and inclusions can inevitably occur. These defects can compromise the material’s performance and reliability, making Non-Destructive Testing (NDT) essential for early defect detection and real-time monitoring. This paper reviews existing NDT technologies like visual inspection, computed tomography (CT), ultrasonic testing, acoustic emission testing, infrared thermal imaging, eddy current testing, and Raman spectroscopy. To facilitate performance comparison, we use spider charts to illustrate key aspects of each technique, including detection accuracy, damage localisation, interface details, and online monitoring capabilities. These charts specifically highlight the superior overall performance of CT. The principles, applications, and cutting-edge advancements in NDT techniques and Structural Health Monitoring systems (SHM) are systematically discussed. This review delves into the principles, strengths, and limitations of CFRP damage detection methods, includes detailed examples and provides a selection criterion for the work of NDT researchers.

Non-destructive testing and structural health monitoring technologies for carbon fiber reinforced polymers: a review

This research paper presents the application of the microwave infrared thermography (MIRT) technique for the purpose of detecting and characterizing defects in the carbon-fiber-reinforced polymer (CFRP) composite reinforcement of concrete specimens. Initially, a numerical model was constructed, which consisted of a broadband pyramidal horn antenna and the specimen. The present study investigated the application of a 360 W power system that operated at a frequency of 2.4 GHz, specifically focusing on two different operational modes: continuous and modulated. The specimen being examined consisted of a solid concrete slab that was coated with an adhesive layer, which was then overlaid with a layer of CFRP. Within the adhesive layer, at the interface between the concrete and CFRP, there was a defect in the form of an air gap. The study examined three distinct scenarios: a sample without any defects, a sample with a defect positioned at the center, and a sample with a defect positioned outside the center. The subsequent stage of the investigation incorporated experimental verification of the numerical modeling results. The experiment involved the utilization of two concrete specimens reinforced using CFRP, one without any defects and the other with a defect. Numerical modeling was used in this study to analyze the phenomenon of microwave heating in complex structures. The objective was to evaluate the selected antenna geometry and determine the optimal experimental configuration. Subsequently, these findings were experimentally validated. The observations conducted during the heating phase were particularly noteworthy, as they differed from previous studies that only performed observation of the sample after the heating phase. The results show that MIRT has the potential to be utilized as a method for identifying defects in concrete structures that are reinforced with CFRP.

Defect Detection in CFRP Concrete Reinforcement Using the Microwave Infrared Thermography (MIRT) Method—A Numerical Modeling and Experimental Approach

Intelligent Computational Methods for Damage Detection of Laminated Composite Structures for Mobility Applications: A Comprehensive Review

Additive manufacturing (AM) defects present significant challenges in fiber-reinforced thermoplastic composites (FRTPCs), directly impacting both their structural and non-structural performance. In structures produced through material extrusion-based AM, specifically fused filament fabrication (FFF), the layer-by-layer deposition can introduce defects such as porosity (up to 10–15% in some cases), delamination, voids, fiber misalignment, and incomplete fusion between layers. These defects compromise mechanical properties, leading to reduction of up to 30% in tensile strength and, in some cases, up to 20% in fatigue life, severely diminishing the composite’s overall performance and structural integrity. Conventional non-destructive testing (NDT) techniques often struggle to detect such multi-scale defects efficiently, especially when resolution, penetration depth, or material heterogeneity pose challenges. This review critically examines manufacturing defects in FRTPCs, classifying FFF-induced defects based on morphology, location, and size. Advanced NDT techniques, such as micro-computed tomography (micro-CT), which is capable of detecting voids smaller than 10 µm, and structural health monitoring (SHM) systems integrated with self-sensing fibers, are discussed. The role of machine-learning (ML) algorithms in enhancing the sensitivity and reliability of NDT methods is also highlighted, showing that ML integration can improve defect detection by up to 25–30% compared to traditional NDT techniques. Finally, the potential of self-reporting FRTPCs, equipped with continuous fibers for real-time defect detection and in situ SHM, is investigated. By integrating ML-enhanced NDT with self-reporting FRTPCs, the accuracy and efficiency of defect detection can be significantly improved, fostering broader adoption of AM in aerospace applications by enabling the production of more reliable, defect-minimized FRTPC components. 

Detecting Multi-Scale Defects in Material Extrusion Additive Manufacturing of Fiber-Reinforced Thermoplastic Composites: A Review of Challenges and Advanced Non-Destructive Testing Techniques

Composite materials have been extensively used in different fields due to their excellent properties, among which carbon fiber-reinforced polymer (CFRP) is the representative. Especially in high-precision fields, such as aerospace, CFRP has become the main structural material for some core components instead of metal. The particularity of such materials and their components in terms of structure, material properties, and required detection conditions puts forward more stringent and targeted detection requirements for detection technology. Ultrasonic testing technology as one of the important means of composite defect detection, which is derived from advanced nondestructive testing (NDT), has also been a rapid development. The propagation behavior and variation of ultrasonic waves in CFRP composites can reveal defects and damages in CFRP composites. Moreover, by constructing reasonable defect identification technology and detection technology, not only the qualitative and quantitative positioning analysis of defects and damages in CFRP composites can be realized but also the automatic, visual and intelligent NDT, and evaluation of CFRP composites can be realized. This paper mainly reviews the innovative nondestructive ultrasonic testing technology for CFRP composites and briefly introduces the research progress and application of this technology in CFRP defect detection. Finally, advanced nondestructive ultrasonic testing technology is summarized, and the problems and development direction of this kind of testing technology are put forward. 

Ultrasonic Testing of Carbon Fiber-Reinforced Polymer Composites

Flexible capacitive pressure sensors have garnered significant attention in research areas such as electronic skin, wearable devices, medical diagnosis, physical health detection, and artificial intelligence due to their advantageous characteristics, including high sensitivity, flexibility, lightness, and easy integration. Over the past few years, the field of flexible capacitive pressure sensors has experienced notable advancements in materials, structural design, fabrication processes, and applications. This review aims to examine the different materials employed for sensor electrodes and dielectric layers, as well as the structural design of these sensors. Additionally, we delve into the diverse fabrication processes and techniques utilised, including electrode and dielectric layer fabrication, as well as weaving technology. Lastly, we explore the various applications of flexible capacitive pressure sensors, encompassing electronic skin, health monitoring device, electronic textiles, and structural health monitoring. 

Flexible capacitive pressure sensor: material, structure, fabrication and application

Accurate multiphase flow measurement is vital in monitoring and optimizing various production processes. Deep learning has as of late arose as a promising approach for assessing multiphase flowrate dependent on various customary flow meters. In this paper, we propose a multi-modal sensor and Temporal Convolution Network (TCN) based method to predict the volumetric flowrate of oil/gas two-phase flows. The volumetric flowrates of the liquid and gas phase vary from 0.96 - 6.13 $\text{m}^{{3}}$ /h and 5.5 - 121.2 $\text{m}^{{3}}$ /h, respectively. The multi-modal sequential sensing data are simultaneously collected from a Venturi tube and a dual-plane Electrical Capacitance Tomography (ECT) sensor in a pilot-scale multiphase phase flow facility. The reference data are derived from the single-phase flowmeters. Z-score and First-Difference (FD) data pre-processing methods are employed to manipulate the collected instantaneous time series multi-modal sensing data. The pre-processed data are utilized for training the TCN model. Experimental results reveal that the TCN model can effectively predict the multiphase flowrate based on the multi-modal sensing data. The results provide guidance on data pre-processing methods for multiphase flowrate estimation and demonstrate the effectiveness of combining multi-modal sensors and TCN for multiphase flowrate prediction under complex flow conditions.

Multiphase Flowrate Measurement With Multimodal Sensors and Temporal Convolutional Network

To reduce noncommutative error (NCE) and improve the attitude-update accuracy in the attitude algorithm of the strapdown inertial navigation system (SINS), we assume that angular rate is represented as a polynomial to deduce the relationship between the angular increment sampling sequence and the derivatives of the quaternion. Subsequently, a high-order numerical solution of the quaternion differential equation is obtained by substituting the derivatives of the quaternion into its Taylor expansion. Generally, the quaternion will be constructed with a series of angular increment samples in a constant updating interval. Furthermore, the piecewise quaternion high-order numerical (PWQHN) algorithm is deduced by reconstructing quaternion in each sampling interval to ulteriorly improve attitude accuracy. The simulation results indicate that the PWQHN algorithm is stable in two kinds of simulation conditions. In the coning motion condition, PWQHN algorithm has the highest accuracy. In the high-dynamic condition, the extreme accuracy of PWQHN is not significantly higher than other algorithms. However, PWQHN can still output accurate attitude in the situation without many angular increment subsamples.

A Super-High-Accuracy Attitude Measurement Method of SINS Based on PWQHN Algorithm in the High-Dynamic Maneuver Environment

Ultrasonic tomography (UT) technology is a typical industrial process tomography (IPT) technology and an effective method for the cross-sectional visualization of a gas–liquid two-phase flow pipeline. For typical noniterative or iterative algorithms, the design of sensitivity maps is an important aspect for imaging results of UT. The construction of sensitivity maps is often based on the assumption of wave propagation theory; therefore, a sharp edge in a sensitive map (SESM) seems difficult to avoid, which produces a low quality of reconstructed images when the sensing objects appear on the edge. This article proposes the multilevel slope reduction methods to improve image quality via changing the effects of SESM. The multilevel slope reduction method is used to create a gradient slope and to eliminate the SESM effect. The Pearson correlation coefficient (PCC), position error (PE), and area error (AE) are selected as image quality evaluation criteria. The simulation and experimental results show that the proposed method can reduce the AE of the reconstruction results by approximately 0.1. The multilevel slope reduction method can increase the PCCs of the simulation and experimental reconstruction results by 0.02 and 0.06, respectively.

A Sensitivity Map Deformation Method for Image Quality Improvement of Ultrasonic Tomography

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