Fluorescent Probe-Based Fiber Optic Sensor for Real-Time Monitoring of Chloride Ions in Coastal Concrete Structures.

Coastal concrete structures, such as cross-sea bridges and tunnels, are susceptible to the penetration of chloride ions, which can lead to the deterioration of the passive film on the rebar surface, consequently accelerating the corrosion process. Conventional methods for monitoring chloride ions typically require in situ drilling for sample collection, thereby compromising efficiency and accuracy. Additionally, real-time monitoring and early warning cannot be achieved. To address these challenges, this work introduces a fluorescent-probe-based fiber optic sensor for monitoring chloride levels in concrete structures. Quinine sulfate was chosen as the fluorescent material due to its exceptional sensitivity to chloride ions and its stability in concrete environments. The proposed sensor was manufactured using sol-gel and 3D-printing techniques. Tests were conducted using concrete simulation fluid and cement mortar specimens. The results demonstrate that the sensitivity of the proposed sensor is greater than 0.01 M, and its accuracy in penetration depth measurement is better than 3 mm. The findings confirm that the designed fiber optic sensor based on quinine sulfate enables real-time monitoring of chloride ions in concrete structures, offering high sensitivity (0.1% in concentration and 2.7 mm in terms of penetration depth), unique selectivity (as it is immune to other ions whose concentrations are 10 times higher than those of Cl-), and a compact size (10 × 20 mm). These attributes render it promising for practical engineering applications.

Integrating the Capsule-like Smart Aggregate-Based EMI Technique with Deep Learning for Stress Assessment in Concrete.

This study presents a concrete stress monitoring method utilizing 1D CNN deep learning of raw electromechanical impedance (EMI) signals measured with a capsule-like smart aggregate (CSA) sensor. Firstly, the CSA-based EMI measurement technique is presented by depicting a prototype of the CSA sensor and a 2 degrees of freedom (2 DOFs) EMI model for the CSA sensor embedded in a concrete cylinder. Secondly, the 1D CNN deep regression model is designed to adapt raw EMI responses from the CSA sensor for estimating concrete stresses. Thirdly, a CSA-embedded cylindrical concrete structure is experimented with to acquire EMI responses under various compressive loading levels. Finally, the feasibility and robustness of the 1D CNN model are evaluated for noise-contaminated EMI data and untrained stress EMI cases.

Detection of Fatigue Cracks for Concrete Structures by Using Carbon Ink-Based Conductive Skin and Electrical Resistance Tomography.

Concrete is among the most widely used structural materials in buildings and bridges all over the world. During their service life, concrete structures may inevitably display cracks due to long-term fatigue loads, leading to the degradation of structural integrity. Thus, it is very important to detect cracks and their growth in concrete structures using an automated structural health monitoring system. In this paper, experimental research on crack detection and imaging of concrete structures by using sensing skin and electrical resistance tomography (ERT) is presented. Carbon ink is screen-printed on the surface of concrete as a conductive material to form sensing skins. With these sensing skins, when cracks occur on or near the surface, it breaks the continuity of the sensing skins and significantly reduces conductivity in cracking areas. Then, after exciting small currents in sensing skins and measuring related voltage data, an inverse analysis based on total variation (TV) regularization is adopted to reconstruct tomographic images showing conductivity changes in sensing skins, to detect the occurrence and growth of cracks. The effectiveness of conductive sensing skins and our related crack detection method is validated in experimental studies on a concrete beam subjected to fatigue tests.

Safety Evaluation of Reinforced Concrete Structures Using Multi-Source Fusion Uncertainty Cloud Inference and Experimental Study.

Structural damage detection and safety evaluations have emerged as a core driving force in structural health monitoring (SHM). Focusing on the multi-source monitoring data in sensing systems and the uncertainty caused by initial defects and monitoring errors, in this study, we develop a comprehensive method for evaluating structural safety, named multi-source fusion uncertainty cloud inference (MFUCI), that focuses on characterizing the relationship between condition indexes and structural performance in order to quantify the structural health status. Firstly, based on cloud theory, the cloud numerical characteristics of the condition index cloud drops are used to establish the qualitative rule base. Next, the proposed multi-source fusion generator yields a multi-source joint certainty degree, which is then transformed into cloud drops with certainty degree information. Lastly, a quantitative structural health evaluation is performed through precision processing. This study focuses on the numerical simulation of an RC frame at the structural level and an RC T-beam damage test at the component level, based on the stiffness degradation process. The results show that the proposed method is effective at evaluating the health of components and structures in a quantitative manner. It demonstrates reliability and robustness by incorporating uncertainty information through noise immunity and cross-domain inference, outperforming baseline models such as Bayesian neural network (BNN) in uncertainty estimations and LSTM in point estimations.

Localization of Cracks in Concrete Structures Using an Unmanned Aerial Vehicle.

Active research on crack detection technology for structures based on unmanned aerial vehicles (UAVs) has attracted considerable attention. Most of the existing research on localization of cracks using UAVs mounted the Global Positioning System (GPS)/Inertial Measurement Unit (IMU) on the UAVs to obtain location information. When such absolute position information is used, several studies confirmed that positioning errors of the UAVs were reflected and were in the order of a few meters. To address these limitations, in this study, without using the absolute position information, localization of cracks was defined using relative position between objects in UAV-captured images to significantly reduce the error level. Through aerial photography, a total of 97 images were acquired. Using the point cloud technique, image stitching, and homography matrix algorithm, 5 cracks and 3 reference objects were defined. Importantly, the comparative analysis of estimated relative position values and ground truth values through field measurement revealed that errors in the range 24-84 mm and 8-48 mm were obtained on the x- and y-directions, respectively. Also, RMSE errors of 37.95-91.24 mm were confirmed. In the future, the proposed methodology can be utilized for supplementing and improving the conventional methods for visual inspection of infrastructures and facilities.

Thermal Strain Detection for Concrete Structure Cold Shrinkage under Stress Constraint with FBG.

Additional strain increments occur in concrete subject to stress constraints during cold shrinkage, resulting in irregular deformation and reducing the concrete structure's stability. When an annular concrete structure is subjected to radial pressure, two tensile stress concentration zones will appear at the intersection of the inner wall and the diameter along the pressure direction. When exposed to low temperatures, the total strain in the tensile stress concentration zones is caused by the combined effect of applied stress strain and thermal strain. Then, the thermal strain of the structure can be obtained from the difference between the total strain and the applied stress strain. Gradient cooling was performed after applying radial pressure to the annular concrete using a counterforce device. The applied stress strain and total strain of the tensile stress concentration zones are measured by fiber Bragg grating (FBG) strain sensors fixed along the stress direction. According to the measurement results, the thermal strains of the concrete structure under the stress constraint are extracted to analyze the influence of the tensile stress constraint on the thermal strain of the concrete structure. In the temperature range of -40∘C∼20∘C, the thermal strains of the structure under radial pressures of 1500 N, 2000 N, and 3000 N are extracted, respectively. The thermal expansion coefficients are calculated based on the thermal strain of the structure. The free thermal expansion coefficient of concrete structures fluctuates around 11×10-6/∘C. When the temperature is reduced to 10∘C, the difference between the thermal expansion coefficient under the stress constraint and the free thermal expansion coefficient is the largest. When the temperature is reduced to -20∘C, the thermal expansion coefficients under each stress condition are close to the same. The results show that the stress confinement significantly inhibits the cold shrinkage of the concrete structure, and the inhibitory effect is gradually weakened when the temperature decreases.

Strain Transfer Mechanisms and Mechanical Properties of Optical Fiber Cables.

Understanding the strain transfer mechanism is required to interpret strain sensing results for fiber optic cables. The strain transfer mechanism for fiber optic cables embedded in cementitious materials has yet to be thoroughly investigated experimentally. Interpretation of fiber optic sensing results is of particular concern when there is a displacement discontinuity. This study investigates the strain transfer mechanism for different types of fiber optic cables while embedded in concrete cubes, sustaining a boundary condition which features a displacement discontinuity. The strain transfer mechanisms for different cables are compared under increasing strain levels. Under cyclic loading, the nonlinear behavior of the force-displacement relation and of the strain distribution in the fiber optic cable are discussed. The mechanical properties of the fiber optic cables are presented and discussed. A parameter is proposed to quantify the strain transfer length. The results of this study will assist researchers and engineers to select appropriate cables for strain sensing and interpret the fiber optic sensing results.

Distributed Fiber-Optic Strain Sensing of an Innovative Reinforced Concrete Beam-Column Connection.

Distributed fiber-optic sensing (DFOS) technologies have been used for decades to detect damage in infrastructure. One recent DFOS technology, Optical Frequency Domain Reflectometry (OFDR), has attracted attention from the structural engineering community because its high spatial resolution and refined accuracy could enable new monitoring possibilities and new insight regarding the behavior of reinforced concrete (RC) structures. The current research project explores the ability and potential of OFDR to measure distributed strain in RC structures through laboratory tests on an innovative beam-column connection, in which a partial slot joint was introduced between the beam and the column to control damage. In the test specimen, fiber-optic cables were embedded in both the steel reinforcement and concrete. The specimen was tested under quasi-static cyclic loading with increasing displacement demand at the structural laboratory of the Pacific Earthquake Engineering Research (PEER) Center of UC Berkeley. Different types of fiber-optic cables were embedded both in the concrete and the rebar. The influence of the cable coating and cable position are discussed. The DFOS results are compared with traditional measurements (DIC and LVDT). The high resolution of DFOS at small deformations provides new insights regarding the mechanical behavior of the slotted RC beam-column connection, including direct measurement of beam curvature, rebar deformation, and slot opening and closing. A major contribution of this work is the quantification of the performance and limitations of the DFOS system under large cyclic strains. Performance is quantified in terms of non-valid points (which occur in large strains when the DFOS analyzer does not return a strain value), maximum strain that can be reliably measured, crack width that causes cable rupture, and the effect of the cable coating on the measurements. Structural damage indices are also proposed based on the DFOS results. These damage indices correlate reasonably well with the maximum sustained drift, indicating the potential of using DFOS for RC structural damage assessment. The experimental data set is made publicly available.

Multi-Sensors Geophysical Monitoring for Reinforced Concrete Engineering Structures: A Laboratory Test.

Non-destructive tests are strongly required in engineering applications for monitoring civil structures. The use of compared and integrated innovative approaches based on geophysical methodologies represents an effective tool for the characterization and monitoring of reinforced concrete (RC) structures. Therefore, the main aim of the work was to improve the knowledge on the potentiality and limitations of the Ground Penetrating Radar (GPR) and the Electrical Resistivity Tomography (ERT) with electrodes disposed both on the surface and in the boreholes. The work approach was adopted on an analog model of a reinforced concrete frame built ad hoc at the Hydrogeosite Laboratory (CNR-IMAA), where simulated experiments on full-size physical models are defined. Results show the ability of an accurate use of GPR to reconstruct the rebar dispositions and detect in detail possible constructive defects, both highlighting the lack of reinforcements into the nodes and providing useful information about the safety assessment of the realized structure. The results of the ERT method defined the necessity to develop ad-hoc electrical resistivity methods to support the characterization and monitoring of buried foundation structures for civil engineering applications. Finally, the paper introduces a new approach based on the use of cross-hole ERTs (CHERTs) for the engineering structure monitoring, able to reduce the uncertainties usually affecting the indirect results.

Design and Performance Analysis of an Ultrasonic System for Health Monitoring of Concrete Structure.

The development and research of an ultrasonic-based concrete structural health monitoring system encounters a variety of problems, such as demands of decreasing complexity, high accuracy, and extendable system output. Aiming at these requirements, a low-cost extendable system based on FPGA with adjustable system output has been designed, and the performance has been evaluated by different assessment parameters set in this paper. Besides the description of the designed system and the experiments in air medium, the residual similarity and Pearson correlation coefficients of experimental and theoretical data have been used to evaluate the submodules' output. The output performance of the overall system is evaluated by the Pearson correlation coefficient, root-mean-square error (RMSE), and magnitude-squared coherence with 40 experimental data. The maximum, median, minimum, and mean values in three-parameter datasets are analyzed for discussing the working condition of the system. The experimental results show that the system works stably and reliably with tunable frequency and amplitude output.

3D Internal Visualization of Concrete Structure Using Multifaceted Data for Ultrasonic Array Pulse-Echo Tomography.

This research proposes a 3D internal visualization using ultrasonic pulse-echo tomography technique to evaluate accurately the state of concrete structures for their efficient maintenance within a limited budget. Synthetic aperture focusing technique (SAFT) is used as a post-processing algorithm to manipulate the data measured by the ultrasonic pulse-echo technique. Multifaceted measurements improve the weakness of the existing ultrasonic pulse-echo tomography technique that cannot identify the area beyond a reflector as well as the area located far away from measuring surfaces. The application of apodization factor, pulse peak delay calibration and elimination of trivial response not only complements the weaknesses of the SAFT algorithm but also improves the accuracy of the SAFT algorithm. The results show that the proposed method reduces the unnecessary surface noise and improves the expressiveness of the reflector's boundaries on the resulting images. It is expected that the proposed 3D internal visualization technique will provide a useful non-destructive evaluation tool in combination with another structure evaluation method.

Ultrasonic Detection Method for Grouted Defects in Grouted Splice Sleeve Connector Based on Wavelet Pack Energy.

Grouted splice sleeve (GSS) connectors are mainly used in precast concrete structures. However, errors in manual operation during construction cause grouted defects in the GSS connector, which can lead to a negative effect on the overall mechanical properties of the structures. Owing to the complex structure of precast concrete members with a GSS connector, it is difficult to detect grouted defects effectively using traditional ultrasonic parameters. In this paper, a wavelet packet analysis algorithm was developed to effectively detect grouted defects using the ultrasonic method, and a verified experiment was carried out. Laboratory detection was performed on the concrete specimens with a GSS connector before grouting, in which the grouted defects were mimicked with five sizes in five GSS connectors of each specimen group. A simple and convenient ultrasonic detection system was developed, and the specimens were detected. According to the proposed grouted defect index, the results demonstrated that when the grouted defects reached certain sizes, the proposed method could detect the grouted defects effectively. The proposed method is effective and easy to implement at a construction site with simple instruments, and so provides an innovative method for grouted defects detection of precast concrete members.

Bond-Slip Monitoring of Concrete Structures Using Smart Sensors-A Review.

Concrete structures with various reinforcements, such as steel bars, composite material tendons, and recently steel plates, are commonly used in civil infrastructures. When an external force overcomes the strength of the bond between the reinforcement and the concrete, bond-slip will occur, resulting in a relative displacement between the reinforcing materials and the concrete. Monitoring bond health plays an important role in guaranteeing structural safety. Recently, researchers have recognized the importance of bond-slip monitoring and performed many related investigations. In this paper, a state-of-the-art review on various smart sensors based on piezoelectric effect and fiber optic technology, as well as corresponding techniques for bond-slip monitoring is presented. Since piezoelectric sensors and fiber-optic sensors are widely used in bond-slip monitoring, their principles and relevant monitoring methods are also introduced in this paper. Particularly, the piezoelectric-based bond-slip monitoring methods including the active sensing method, the electro-mechanical impedance (EMI) method and the passive sensing using acoustic emission (AE) method, and the fiber-optic-based bond-slip detecting approaches including the fiber Bragg grating (FBG) and the distributed fiber optic sensing are highlighted. This paper provides guidance for practical applications and future development of bond-slip monitoring.

Vision-Based Autonomous Crack Detection of Concrete Structures Using a Fully Convolutional Encoder-Decoder Network.

The visual inspection of massive civil infrastructure is a common trend for maintaining its reliability and structural health. However, this procedure, which uses human inspectors, requires long inspection times and relies on the subjective and empirical knowledge of the inspectors. To address these limitations, a machine vision-based autonomous crack detection method is proposed using a deep convolutional neural network (DCNN) technique. It consists of a fully convolutional neural network (FCN) with an encoder and decoder framework for semantic segmentation, which performs pixel-wise classification to accurately detect cracks. The main idea is to capture the global context of a scene and determine whether cracks are in the image while also providing a reduced and essential picture of the crack locations. The visual geometry group network (VGGNet), a variant of the DCCN, is employed as a backbone in the proposed FCN for end-to-end training. The efficacy of the proposed FCN method is tested on a publicly available benchmark dataset of concrete crack images. The experimental results indicate that the proposed method is highly effective for concrete crack classification, obtaining scores of approximately 92% for both the recall and F1 average.

In-Fiber Optic Salinity Sensing: A Potential Application for Offshore Concrete Structure Protection.

The protection of concrete structures against corrosion in marine environments has always been a challenge due to the presence of a saline solution-A natural corrosive agent to the concrete paste and steel reinforcements. The concentration of salt is a key parameter influencing the rate of corrosion. In this paper, we propose an optical fiber-based salinity sensor based on bundled multimode plastic optical fiber (POF) as a sensor probe and a concave mirror as a reflector in conjunction with an intensity modulation technique. A refractive index (RI) sensing approach is analytically investigated and the findings are in agreement with the experimental results. A maximum sensitivity of 14,847.486/RIU can be achieved at RI = 1.3525. The proposed technique is suitable for in situ measurement and monitoring of salinity in liquid.

Concrete Crack Identification Using a UAV Incorporating Hybrid Image Processing.

Crack assessment is an essential process in the maintenance of concrete structures. In general, concrete cracks are inspected by manual visual observation of the surface, which is intrinsically subjective as it depends on the experience of inspectors. Further, it is time-consuming, expensive, and often unsafe when inaccessible structural members are to be assessed. Unmanned aerial vehicle (UAV) technologies combined with digital image processing have recently been applied to crack assessment to overcome the drawbacks of manual visual inspection. However, identification of crack information in terms of width and length has not been fully explored in the UAV-based applications, because of the absence of distance measurement and tailored image processing. This paper presents a crack identification strategy that combines hybrid image processing with UAV technology. Equipped with a camera, an ultrasonic displacement sensor, and a WiFi module, the system provides the image of cracks and the associated working distance from a target structure on demand. The obtained information is subsequently processed by hybrid image binarization to estimate the crack width accurately while minimizing the loss of the crack length information. The proposed system has shown to successfully measure cracks thicker than 0.1 mm with the maximum length estimation error of 7.3%.

Distributed Long-Gauge Optical Fiber Sensors Based Self-Sensing FRP Bar for Concrete Structure.

Brillouin scattering-based distributed optical fiber (OF) sensing technique presents advantages for concrete structure monitoring. However, the existence of spatial resolution greatly decreases strain measurement accuracy especially around cracks. Meanwhile, the brittle feature of OF also hinders its further application. In this paper, the distributed OF sensor was firstly proposed as long-gauge sensor to improve strain measurement accuracy. Then, a new type of self-sensing fiber reinforced polymer (FRP) bar was developed by embedding the packaged long-gauge OF sensors into FRP bar, followed by experimental studies on strain sensing, temperature sensing and basic mechanical properties. The results confirmed the superior strain sensing properties, namely satisfied accuracy, repeatability and linearity, as well as excellent mechanical performance. At the same time, the temperature sensing property was not influenced by the long-gauge package, making temperature compensation easy. Furthermore, the bonding performance between self-sensing FRP bar and concrete was investigated to study its influence on the sensing. Lastly, the sensing performance was further verified with static experiments of concrete beam reinforced with the proposed self-sensing FRP bar. Therefore, the self-sensing FRP bar has potential applications for long-term structural health monitoring (SHM) as embedded sensors as well as reinforcing materials for concrete structures.

Image enhancement for on-site X-ray nondestructive inspection of reinforced concrete structures.

The use of portable and high-energy X-ray system can provide a very promising approach for on-site nondestructive inspection of inner steel reinforcement of concrete structures. However, the noise properties and contrast of the radiographic images for thick concrete structures do often not meet the demands. To enhance the images, we present a simple and effective method for noise reduction based on a combined curvelet-wavelet transform and local contrast enhancement based on neighborhood operation. To investigate the performance of this method for our X-ray system, we have performed several experiments with using simulated and experimental data. With comparing to other traditional methods, it shows that the proposed image enhancement method has a better performance and can significantly improve the inspection performance for reinforced concrete structures.

Influence of Polypropylene Fiber on Concrete Permeability under Freeze-Thaw Conditions and Mechanical Loading.

Polypropylene fiber reinforcement is an effective method to enhance the durability of concrete structures. With the increasing public interest in the widespread use of polypropylene fiber reinforced concrete (PFRC), the necessity of evaluating the mechanism of polypropylene fiber (PF) on the permeability of concrete has become prominent. This paper describes the influence of PF on the concrete permeability exposed to freeze-thaw cycles under compressive and tensile stress. The permeability of PFRC under compressive and tensile loads is accurately measured by a specialized permeability setup. The permeability of PFRC under compressive and tensile loads, the volume change of PFRC under compressive load, and the relationship between compressive stress levels at minimum permeability and minimum volume points of PFRC are discussed. The results indicate that the addition of PF adversely affects the permeability of concrete without freeze-thaw damage and cracks. However, it decreases the permeability of concrete specimens exposed to freeze-thaw cycles and cracking. Under compressive load, the permeability of PFRC initially decreases slowly and follows by a significant increase as the compressive stress level increases. This phenomenon correlates with the volume change of the specimen. The compressive stress level of the minimum permeability point and compressive stress level of the minimum volume point of PFRC exhibit a linear correlation, with a fitted proportional function parameter γ ≈ 0.98872. Under tensile load, the permeability of PFRC increases gradually with radial deformation and follows by a significant increase. The strain-permeability curves of PFRC under loading are studied and consist of two stages. In stage I, the permeability of PFRC gradually decreases with the increase of strain under compressive load, while the permeability increases with the increase of strain under tensile load. In stage II, under compressive load, the permeability of PFRC increases with the increase of freeze-thaw cycles, whereas under tensile load, the permeability gradually decreases with the increase of freeze-thaw cycles. The reduction of PF on the permeability of PFRC under tensile load is greater than that under compressive load. In future research, the relationship between strain and permeability of PFRC can be integrated with its constitutive relationship between stress and strain to provide a reference for the application of PF in the waterproofing of concrete structures.

Modelling of Resinous Material Filling Expansion Joints in Reinforced Concrete Structures.

This paper is a continuation of the research and analysis to estimate hyperelastic material constants when only uniaxial test data are available. The FEM simulation was expanded and the results obtained from three-dimensional and plane strain expansion joint models were compared and discussed. The original tests were carried out for a gap with a width of 10 mm, whereas in the case of axial stretching, the stresses and internal forces caused by the leading deformations were recorded for a smaller gap, and the axial compression was also recorded. The differences in the global response between the three- and two-dimensional models were also considered. Finally, using FEM simulations, the values of stresses and cross-sectional forces in the filling material were determined, which can be the basis for the design of expansion joints geometry. The results of these analyses could form the basis of guidelines for the design of expansion joint gaps filled with material, ensuring the waterproofing of the joint.