IoT-Based Interdigital Capacitance Sensing System for Damage Detection in CFRP-Concrete Structures

The monitoring of a structural condition of steel bridges is an important issue. Good condition of infrastructure facilities ensures the safety and economic well-being of society. At the same time, due to the continuous development, rising wealth of the society and socio-economic integration of countries, the number of infrastructural objects is growing. Therefore, there is a need to introduce an easy-to-use and relatively low-cost method of bridge diagnostics. We can achieve these benefits by the use of Unmanned Aerial Vehicle-Based Remote Sensing and Digital Image Processing. In our study, we present a state-of-the-art framework for Structural Health Monitoring of steel bridges that involves literature review on steel bridges health monitoring, drone route planning, image acquisition, identification of visual markers that may indicate a poor condition of the structure and determining the scope of applicability. The presented framework of image processing procedure is suitable for diagnostics of steel truss riveted bridges. In our considerations, we used photographic documentation of the Fitzpatrick Bridge located in Tallassee, Alabama, USA.

An Internet of Things (IoT) node-based nonintrusive, resilient liquid flow transmitter has been developed using a circular interdigital capacitor (IDC) as a primary sensor. A section of polyvinyl chloride (PVC) pipe is cut and a silicon tube of lesser diameter (compared with PVC pipe) is attached with the PVC pipe. A circular IDC is installed on the silicon tube and is used to convert the volumetric flow rate into corresponding capacitance. The change in capacitance with respect to the flow rate is nonlinear. Modified De Sauty bridge circuit is connected with the developed flow sensor to get the electrical output. To linearize the bridge output, three different linearization techniques, i.e., linear least square (LLS), genetic algorithm (GA), and artificial neural network (ANN) have been used and their statistical analysis is reported in this article. The best suited linearization technique (ANN) for the proposed sensor from the simulated result has been used in hardware implementation. This article proposes the IDC sensor access scheme design based on the IoT node and the transmission of the standard voltage signal 1–5 V using IoT node. The transmitted voltage and the received voltage have the same characteristic under the percentage error of ±0.5% of transmitted voltage. It has 0.286-pF resolution for measurement of liquid flow rate from 0 to 2000 litre per hour (LPH). This article highlights the theoretical analysis of the developed sensor to elucidate its working with maximum clarity. This sensor has been tested and its characteristics are thoroughly studied in the research workshop. Experimental results have proved that the wireless flow transmitter obeys linear characteristics and tuned well with theoretical equations based on the working principle. In addition, the proposed flow measurement is nonintrusive in nature as the circular IDC does not come in contact with the liquid flow, hence, increases the life span of the sensor.

Liquid level measurement is important requirement for building and industrial applications. One of the most popular methods for level measurement is the capacitive sensor. Most of the capacitive sensors are contact type which suffer from frequent maintenance, and drift of the sensor output. Present paper proposes a simple noncontact interdigitated fringing field capacitance sensor for liquid level measurement. Three different interdigitated (IDT) sensors have been designed, simulated (finite element method) and fabricated to measure water level from 0 to 40 mm. The capacitance value of all the three sensors vary almost linearly with the variation of liquid level. Experimental results show that the sensitivity of the 1-5-1 IDT sensor is 0.0398 pF/mm. The sensor is capable to detect water level with resolution of 0.1 mm

In this study, we demonstrate large-scale sensors, intended for damage detection in various engineering structures, within the framework of structural health monitoring. The construction of sensors is based on a hybrid structure of a conductive intermittent pattern (made of silver) with embedded sensing elements made of a carbon-based resistive ink. Because the electrical resistance of the silver pattern was much lower than that of the resistance of sensing elements, the sensing properties of such sensors depend mainly on the properties of the carbon ink. Thanks to such construction, it was possible to achieve the electrical resistance of 1-m-long sensors within the desired range. Conducted laboratory tests have shown relatively low sensitivity to temperature changes and the capability of the sensors for damage detection.

An optimal sensor placement design framework for structural health monitoring using Bayes risk

Most damage-assessment strategies for dynamic systems only distinguish between undamaged and damaged conditions without recognizing the level or type of damage or considering unseen conditions. This paper proposes a novel framework for structural health monitoring (SHM) that combines supervised and unsupervised learning techniques to assess damage using a system’s structural response (e.g., the acceleration response of big infrastructures). The objective is to enhance the benefits of a supervised learning framework while addressing the challenges of working in an SHM context. The proposed framework uses a Linear Discriminant Analysis (LDA)/Probabilistic Linear Discriminant Analysis (PLDA) strategy that enables learning the distributions of known classes and the performance of probabilistic estimations on new incoming data. The methodology is developed and proposed in two versions. The first version is used in the context of controlled, conditioned monitoring or for post-damage assessment, while the second analyzes the single observational data. Both strategies are built in an automatic framework able to classify known conditions and recognize unseen damage classes, which are then used to update the classification algorithm. The proposed framework’s effectiveness is first tested considering the acceleration response of a numerically simulated 12-degree-of-freedom system. Then, the methodology’s practicality is validated further by adopting the experimental monitoring data of the benchmark study case of the Z24 bridge.

Continuous bridge monitoring generates long-duration, noisy, and multi-channel acceleration streams in which abnormal events are sparse yet safety-critical. This work targets reliable event classification under operational conditions while preserving the time-series nature of the data. A hybrid one-dimensional convolutional model with channel-wise recalibration and attention analyzes fixed-length windows from a cable-stayed bridge monitoring system, combined with peak picking, batching, and lightweight inference that suit day-scale records. Evaluation on a held-out test set shows balanced class performance: precision across classes ranges from 90.5% to 98.4%, and recall across classes ranges from 91.4% to 99.6%, yielding precision of 94.6% and recall of 94.9%. At the sensor level, classification accuracy ranges from 93.6% to 99.7%, indicating consistent behaviour across heterogeneous channels with different noise and placement conditions. The dataset is split into training, validation, and test subsets, and the test set remains untouched during model selection. The results suggest robust detection across classes and sensors, stable operation on continuous 24-h records, and practical readiness for field deployment in bridge monitoring.

This work proposes an optimal fiber optic sensor placement framework for structural health monitoring (SHM) applications. The framework is applied to an aircraft’s wing spar entirely made of composite materials. The damage of interest is debonding between laminates, which may cause local buckling that results in reduced structural load carrying capabilities. A high-fidelity finite element (FE) model is used as a synthetic data generator. The inputs to the model are loads and debonding damage parameters (size and location), and the outputs are uniaxial strain measurements and buckling eigenvalues. “Run time” surrogate models are created using different machine learning methods to overcome the high computational costs of each run of the physicsbased model. Then, Bayesian inference is used to estimate the damage parameters given strain measured at candidate sensor locations. These estimations are used to assess damage criticality, which is linked to buckling eigenvalues, and transformed into decisions. Bayesian optimization is used to select the candidates that minimize a utility function that considers the costs associated with making a certain decision plus the costs of acquiring and installing the SHM hardware (sensors, data acquisition system, etc.). The candidate with the lowest cost is selected. The resulting optimal sensor configuration is presented, consisting of the number of sensors to be deployed and their respective locations. The importance of defining an objective function that reflects the goal of the SHM system (e.g., maximizing the probability of detection, minimizing the probability of false alarms, or a balance of both) are also discussed.

Regular health monitoring of bridges is a vital process to prevent serious structural damage. Marker-based systems, which follow the trajectory of objects by placing a well-characterized pattern on their surface and identify them on photos or videos taken of these objects, have proven to be a cheap and flexible alternative for such tasks. In this work, we extend our previous laboratory implementation with a low-cost, fully automatic on-site installation at the bridge at Arosa Island, Galicia, Spain. Preliminary results presented in this paper show that our system is highly robust for the harsh climate of the installation site.

Applications of Aluminium and Concrete Composite Structures

For applications in the civil and building engineering area of the retrofitting method using carbon-fiber sheet, the behavior of durability and long-term performance of reinforced concrete members under live loads should be grasped. This paper describes about the long-term performance of reinforced concrete beams with carbon-fiber sheet. As a result of long-term loading tests for the last 4 years, it was concluded that the retrofitting system using carbon-fiber sheets is successful to increase the flexural strength and to prevent the increase of long-term deflection on the existing reinforced concrete beams or slabs, in the manner as seismic reform of columns in buildings and the pier of bridges.

This article describes the development of a method for a biocompatible sensor device for the intent of in-vivo breast tissue dielectric properties measurements. This article focuses on a specific type of sensor that utilizes an LC circuit with an inter-digital capacitor (IDC) with small size and high sensitivity for early stage breast cancer detection. To meet this objective an IDC was optimized in terms of contrast and miniaturized size via simulation techniques. For experimental testing, a scaled-up prototype inter-digital capacitor and spiral square inductor sensor was fabricated, and tested with known media, such as distilled water and glycerol. The results suggest that there is a need for further development, such as fabrication and testing for the biocompatible, miniaturized sensor for breast tissue application.

The insulation system of transformers degrades over time due to harsh environmental and operational conditions, thus reducing their designated useful life. In oil, insulation degradation compounds are released due to transformer insulation degradation. An oil-immersed transformer can be assessed based on the concentration of these compounds in the oil. Transformer paper insulation conditions can be assessed by the level of 2-furfuraldehyde (2-FAL) and moisture in the oil. The dielectric constant of 2-FAL and moisture is very high in comparison to that of fresh oil, making capacitive sensor-based detection of these compounds in oil a promising and economical option. This paper investigates the feasibility of a simple interdigital transducer (IDT) fringing field capacitive sensor in the measurement of the concentration of 2-FAL and humidity in transformer oil. Firstly, an interdigital capacitive (IDC) sensor is simulated using COMSOL Multiphysics software to design and analyse a 1-r-1 IDC sensor where polyvinyl alcohol (PVA) and polydimethylsiloxane (PDMS) are used as sensing materials. Moreover, three different arrangements of electrode structures, which have been designed, are investigated: single-sided (SS), single-sided with shielding (SSWS) and double-sided (DS). Furthermore, the IDC sensors are fabricated and characterised. The fabricated sensor is tested and characterised using oil samples with different concentrations of humidity and 2-FAL (in the 0-60 ppm range). The sensor exhibits the maximum sensitivity for a double-sided 1-1-1 configuration. In the 0-60 ppm range, the sensor exhibits approximately linear response characteristics and very small characteristics drift. The sensor facilitates real-time on-site condition monitoring of in-service transformers and circumvents all the issues of offline monitoring.

Deep learning-driven multi-level data fusion framework for predictive maintenance and structural health monitoring of concrete bridge decks

Unsupervised learning-based framework for indirect structural health monitoring using adversarial autoencoder