Health Monitoring In Civil Engineering Research Articles

Applications of Smart and Self-Sensing Materials for Structural Health Monitoring in Civil Engineering: A Systematic Review

Civil infrastructures are constantly exposed to environmental effects that can contribute to deterioration. Early detection of damage is crucial to prevent catastrophic failures. Structural Health Monitoring (SHM) systems are essential for ensuring the safety and reliability of structures by continuously monitoring and recording data to identify damage-induced changes. In this context, self-sensing composites, formed by incorporating conductive nanomaterials into a matrix, offer intrinsic sensing capabilities through piezoresistivity and various conduction mechanisms. The paper reviews how SHM with self-sensing materials can be applied to civil infrastructure while also highlighting important research articles in this field. The result demonstrates increased dissemination of self-sensing materials for civil engineering worldwide. Their use in core infrastructure components enhances functionality, safety, and transportation efficiency. Among nanomaterials used as additions to produce self-sensing materials in small portions, carbon nanotubes have the most citations and, consequently, the most studies, followed by carbon fiber and steel fiber. This highlight identifies knowledge gaps, benchmark technologies, and outlines self-sensing materials for future research.

Fiber bragg grating accelerometer based on asymmetric shaped flexible hinge structure

To achieve real-time health monitoring in civil engineering, a FBG acceleration sensor with asymmetric shaped flexible hinge structure is proposed. Combined with the theoretical analysis, the expressions of resonance frequency and sensitivity are deduced, and the effects of the structural parameters of the sensor on the resonance frequency and sensitivity are analyzed and simulated. Based on the optimization results, a fiber Bragg grating acceleration sensor was designed and fabricated. The amplitude-frequency response, linear sensitivity response, and lateral anti-interference ability were tested experimentally. The experimental data shows a sensor resonance frequency of 350 Hz, a sensitivity of 90.96 pm/g, and a linear correlation of 99.59%. These results closely align with the theoretical predictions of a resonance frequency of 344 Hz, and a sensitivity of 98 pm/g, confirming the accuracy of the sensor's theoretical analysis.The transverse interference immunity of the sensor is 3.7%, and an operating frequency band of 10–250 Hz. It indicates that the sensor is promising for medium and low frequency vibration measurement.

Advancing Structural Health Monitoring in Civil Engineering with Greybox Modelling: A Review

This review explores the application of Greybox Modelling, specifically Physics-Informed Machine Learning (PIML), in the context of Structural Health Monitoring (SHM) within civil engineering. SHM is vital for ensuring the safety and durability of infrastructure namely bridges. Greybox Modelling, merging physics-based principles with data-driven techniques, offers a functional approach for understanding complex structural behaviors. By leveraging existing knowledge of physical laws, this method extracts valuable insights from sensor data. The integration of Greybox Modelling takes SHM a step further. By applying machine learning algorithms with the physics that govern civil engineering structures, Greybox Modelling bridges the gap between data-driven models and physics-based models. This leads more accurate predictions and interpretable outcomes. This review discusses findings from literature, recent studies, that illustrate the effectiveness of Greybox Modelling, specifically PIML in various civil engineering applications. These case studies highlight how this approach can adapt to different structural designs and environmental conditions typically encountered in civil engineering. Moreover, they indicate its potential for damage detection and early warning systems, which enhance the safety and efficiency of infrastructure. The paper also addresses current challenges and outlining future directions in the field.

State-of-the-Art Structural Health Monitoring in Civil Engineering

In the past, when structures contained elements which were prone to deterioration over time [...]

Automatic Detection of Bridge Surface Crack Using Improved YOLOv5s

Bridge crack detection is a key task in the structural health monitoring of Civil Engineering. In the traditional bridge crack detection methods, there exist some problems such as high cost, low speed, and complex structure. This paper developed a bridge surface crack detection system based on improved YOLOv5s. The GhostBottleneck module was employed to replace the classic C3 module of the YOLOv5s backbone network, meanwhile the channel attention module namely ECA-Net was also added to the network, which not only reduced the amount of calculation, but also enhanced the ability of the network in extracting cross-channel information features. The adaptive spatial feature fusion (ASFF) was introduced to address the conflict problem caused by the inconsistency of feature scale in the network feature fusion stage, and the transfer learning was utilized to train the network. The experimental results showed that the improved YOLOv5s performed better than Faster R-CNN, SSD, YOLOv3, and YOLOv5s, with the Precision of 93.6%, Recall of 95.4%, and mAP of 98.4%. Further, the improved YOLOv5s was deployed in PyQt5 to realize the real-time detection of bridge cracks. This research showed that the proposed model not only provides a novel solution for bridge surface crack detection, but also has certain industrial application value.

Image Processing Based Non-Invasive Health Monitoring in Civil Engineering

Abstract: It has been observed that millions of dollars are being invested on highway/subway tunnel maintenanceand restoration all over the globe. But this cost can be minimized if the detection of cracks will be found as earlieras possible. As the restoration process depends on the type of cracks, it is important to plan the required steps to be taken for repairing the destruction caused as earlier as possible. Initially we need to capture very transparent images of the roads/concrete infrastructures as the detection process will depend on those images. The device usedfor scanning and capturing the images of the concrete infrastructures or roads must be configured for picturing high resolution images. After processing the image acquired, it is easier to extract information about the cracks found. Depending on this information, the images could be classified using some decision-making algorithm. Thisprocedure can be implemented on images acquired by any objects or vehicles carrying image sensing terminal, laser distance sensor, image storage and processing servers, central control system and speed sensor. The accuracydepends on the image’s quality and accurate capture.

Dielectric and piezoelectric behavior of PVDF-modified 3-3 type cement-based piezoelectric composites

In this research, 3–3 type cement-based piezoelectric composites were prepared by casting Portland cement paste in porous lead zirconate titanate (PZT) ceramics, then the polyvinylidene fluoride (PVDF) of N-methylpyrrolidone solvent with concentration of 50–200 mg ml−1 was utilized to modify the lead zirconate titanate-Potland cement (PZT-PC) composites. The effects of PVDF concentration on the density, microstructure, dielectric, piezoelectric and electromechanical properties were studied. The results indicate that the density of PZT-PC composites increased gradually with PVDF concentration for the increasing combined weight of PVDF with the composites. The introduction of PVDF has also contributed to the reduction of leakage current during the poling and testing process, which led to increased relative permittivity ϵ r and longitudinal piezoelectric strain coefficient d 33, while the dielectric loss tanδ and longitudinal piezoelectric voltage coefficient g 33 demonstrated an opposite changing trend. The thickness electromechanical coupling factor K t of composites increased with the concentration of PVDF solution. The acoustic impedance (Z) of PVDF modified PZT-PC composites ranged from 6.89 to 7.65 MRayls, making it suitable for applications in the health monitoring of civil engineering.

Application of Research and Development of Intelligent Wall Insulation Materials Based on Recycling of Waste Textiles in Civil Engineering Health Monitoring

In recent years, with the continuous acceleration of the country's economic construction, the demand for civil engineering construction is also deepening, and the accidents in construction are also rising. In order to reduce the occurrence of accidents, smart wall insulation materials were born. In order to achieve the best results of smart wall insulation materials, we have investigated many smart wall materials, such as waste and old, through literature research and data investigation, control products, coagulation, steel, etc. and by establishing relevant experimental models, investigating experiments to obtain experimental data, and researching the role of different materials in health monitoring. The results of the study found that, among the several materials tested, the smart wall insulation material made of the experimental waste prevention products performed the best in the health inspection, which was about 12% higher than other materials, and the waste textiles were resistant to damage. The ability is the strongest, about 10% higher than other materials, which shows that the research and development of intelligent wall insulation materials based on waste textiles can play a key role in civil engineering health monitoring.

Smart structural health monitoring in civil engineering: A survey

Structural health monitoring (SHM) technologies for tracking and assessing socially and historically constructed long-haul spans was being increasingly recognized, and several government officials have realized the importance of the methodology. The article reviews recent advance of the SHM technologies in order to long-span bridges. A SHM framework is implemented to deploy. The research design and quality evaluation involving modal recognition techniques, image processing approaches, and hazard detection systems were then checked and presented. This research incorporates a developing capacity tool in combination with a low- and mid-index demonstrable evaluation tool for bridge architectural condition monitoring. The strength of concrete, stiffness of the steel beam surface, density of the top surface of the hollow plate, crack propagation, cover hardness of metal bars etc.From the examples, the open issues and recent developments for a SHM system have been addressed.

Fiber curvature sensor based on concave‐heterotypic cascaded fiber Sagnac interferometer

AbstractA fiber optical curvature sensor comprising a concave‐heterotypic cascaded fiber structure built into Sagnac loop is demonstrated experimentally. A section of multimode fiber is sandwiched between the single mode fiber and polarization maintaining fiber in order to construct the concave‐heterotypic cascaded fiber structure. The concave structure presented is used to effectively excite guided modes. The spectra of the proposed bending sensor will shift toward long wavelength with curvature. The measured the curvature sensitivity of 11.6 nm/m−1 has been achieved. The proposed curvature sensor characterizes cost‐efficient, simple fabrication process and can be used for health monitoring in civil engineering.

Small‐Scale Void‐Size Determination in Reinforced Concrete Using GPR

The detection and evaluation of void in concrete are imperative issues in health monitoring of civil engineering. However, the void is difficult to be detected at its early stage of formation on account of its small scale and concealment. Although, in view of the remarkable performance such as precision and continuity, ground penetrating radar (GPR) is widely employed in some special fields of nondestructive testing, the strong reflection of rebar which could submerge the echo of void is now a stumbling block for the application of GPR in reinforced concrete. In order to develop the tactic of small‐scale void‐size determination with considerable noise originated from rebar, a two‐stage regression method is proposed in this study. First, a superposition model of target echoes was established to analyze the relationship between the space‐time distribution and spectrum of the rebar echo and the void echo, and a curvelet transform was employed to reduce the strong reflection of the rebar. Then, the echo amplitude of small‐scale void was investigated, and a regression method was put forward to determine the void size of small scale. Finally, forward simulation and experiment were carried out to verify the feasibility of the proposed method. Results demonstrated that the spectral distribution of the rebar echo is significantly higher than that of the void echo. The curvelet‐based low‐pass filter can effectively ensure the discrimination of void echo. The regression method performs a high‐accuracy void‐size determination with more than 90% in small scale.

Smart RFID Sensors Embedded in Building Structures for Early Damage Detection and Long-Term Monitoring.

In civil engineering, many structures are made of reinforced concrete. Most degradation processes relevant to this material, e.g., corrosion, are related to an increased level of material moisture. Therefore, moisture monitoring in reinforced concrete is regarded as a crucial method for structural health monitoring. In this study, passive radio frequency identification (RFID)-based sensors are embedded into the concrete. They are well suited for long-term operation over decades and are well protected against harsh environmental conditions. The energy supply and the data transfer of the humidity sensors are provided by RFID. The sensor casing materials are optimised to withstand the high alkaline environment in concrete, having pH values of more than 12. Membrane materials are also investigated to identify materials capable of enabling water vapour transport from the porous cement matrix to the embedded humidity sensor. By measuring the corresponding relative humidity with embedded passive RFID-based sensors, the cement hydration is monitored for 170 days. Moreover, long-term moisture monitoring is performed for more than 1000 days. The experiments show that embedded passive RFID-based sensors are highly suitable for long-term structural health monitoring in civil engineering.

Research on new FBG soil pressure sensor and its application in engineering

Since the existing soil pressure box sensor has low accuracy for monitoring the main lateral soil pressure outside the foundation pit wall, and inability to achieve three-dimensional soil pressure monitoring, a three-dimensional soil pressure sensor is designed based on the fiber Bragg grating (FBG) sensing device, containing six pressure surfaces and pressure diaphragms. This sensor has the capability to monitor the space's three-dimensional stress distribution with high sensitivity to monitor data, less liability to damage and long service life. Performance tests implemented on the produced prototype transducer and sensitivity is tested as 382.30769pm/MPa, 362.83692pm/MPa, 341.56308pm/MPa, 318.46154pm/MPa, 415.04615pm/MPa and 428.92308pm/MPa in the range of 0–3 MPa with lowest fitting error 0.99902. Moreover, the deviation of drift values for the measured wavelength to theoretically calculated value in the full range is nearly 2.3%, 1.6%, 0.49%, 1.6%, 0.4% and 1.3%. Consequently, this transducer meets the accuracy requirements of stress and deformation measurement for the structural health monitoring in civil engineering. To monitor the real-time deep soil pressure of the foundation pit wirelessly, four displacement sensors, which have been tested and calibrated, are installed in the foundation pit of the east square in the special-class station on Line 1 of Taiyuan rail transit. Subsequently, the reason for the change in the soil pressure on the monitored section is analyzed and compared with actual working conditions in the field verifying the effectiveness of the three-dimensional soil pressure sensing based on FBG in the soil pressure monitoring when constructing foundation pit.

Structural damage identification based on autoencoder neural networks and deep learning

Artificial neural networks are computational approaches based on machine learning to learn and make predictions based on data, and have been applied successfully in diverse applications including structural health monitoring in civil engineering. It is difficult to optimize the weights in the neural networks that have multiple hidden layers due to the vanishing gradient issue. This paper proposes an autoencoder based framework for structural damage identification, which can support deep neural networks and be utilized to obtain optimal solutions for pattern recognition problems of highly non-linear nature, such as learning a mapping between the vibration characteristics and structural damage. Two main components are defined in the proposed framework, namely, dimensionality reduction and relationship learning. The first component is to reduce the dimensionality of the original input vector while preserving the required necessary information, and the second component is to perform the relationship learning between the features with the reduced dimensionality and the stiffness reduction parameters of the structure. Vibration characteristics, such as natural frequencies and mode shapes, are used as the input and the structural damage are considered as the output vector. A pre-training scheme is performed to train the hidden layers in the autoencoders layer by layer, and fine tuning is conducted to optimize the whole network. Numerical and experimental investigations on steel frame structures are conducted to demonstrate the accuracy and efficiency of the proposed framework, comparing with the traditional ANN methods.

Strain Transfer Analysis of FBG Sensors Bonded on the Surface of Structure

Health monitoring of civil engineering has been widely required in construction, operation and service-life prediction. FBG sensors, a kind of the most efficient sensing elements, for the superior advantages of small size, high sensitivity, long-term stability and durability, good geometrical shape-versatility, corrosion resistance and anti-electromagnetic interference, are highly applied in different engineering fields. Measurement precision of this sensor is paid attention and strain transfer analysis is developed to give an explanation on the testing mechanism and provide the quantitative strain transfer error modification functions. Theoretical derivation on strain transfer analysis of sensors with debonding interface is executed and related functions have been offered to eliminate the strain transfer error. Research in this article is meaningful for improving the precision of FBG sensors with debonding interface, which is helpful for much long-term detection of structures.

Application of Modal Identification Methods to Spatial Structure Using Field Measurement Data

Modal identification with output-only measurements plays a key role in vibration-based damage detection, model updating, and structural health monitoring in civil engineering. This paper addresses the application of modal identification method to a triangle steel tube truss natatorium using the field measurement data. To obtain dynamic characteristics of the spatial structure, four different output-only system identification methods are employed. They are natural excitation technique–eigensystem realization algorithm, data-driven stochastic subspace identification method, frequency-domain decomposition/frequency-spatial domain decomposition method, and half spectra/rational fractional orthogonal polynomial method. First an analytical modal analysis was performed on the three-dimensional finite element model according to the factual layout design to obtain the calculated frequencies and mode shapes. Then the whole procedure of the field vibration tests on the natatorium was presented. Finally, practical issues and efficiency of the four output-only modal identification techniques are investigated, and compared with the results from a finite element model. The system identification results demonstrate that both methods can provide reliable information on dynamic characteristics of the spatial structure. The frequency-domain methods, however, can quickly identify the modal parameters, but the leakage error introduced by power-spectral density estimation is existent due to the limited length of data. And the time-domain methods can avoid the leakage error, but the computational modes and the computational cost are the main two drawbacks in application. The conclusion is that several system identification methods should be consulted to ensure the accuracy of the estimated modal parameters.