Detecting Multiple Damages in UHPFRC Beams through Modal Curvature Analysis.

Curvature-based damage detection has been previously applied to identify damage in concrete structures, but little attention has been given to the capacity of this method to identify distributed damage in multiple damage zones. This study aims to apply for the first time an enhanced existing method based on modal curvature analysis combined with wavelet transform curvature (WTC) to identify zones and highlight the damage zones of a beam made of ultra-high-performance fiber-reinforced concrete (UHPFRC), a construction material that is emerging worldwide for its outstanding performance and durability. First, three beams with a 2 m span of UHPFRC material were cast, and damaged zones were created by sawing. A reference beam without cracks was also cast. The free vibration responses were measured by 12 accelerometers and calculated by operational modal analysis. Moreover, for the sake of comparison, a finite element model (FEM) was also applied to two identical beams to generate numerical acceleration without noise. Second, the modal curvature was calculated for different modes for both experimental and FEM-simulated acceleration after applying cubic spline interpolation. Finally, two damage identification methods were considered: (i) the damage index (DI), based on averaging the quadratic difference of the local curvature with respect to the reference beam, and (ii) the WTC method, applied to the quadratic difference of the local curvature with respect the reference beam. The results indicate that the developed coupled modal curvature WTC method can better identify the damaged zones of UHPFRC beams.

A Deep Learning Approach for Autonomous Compression Damage Identification in Fiber-Reinforced Concrete Using Piezoelectric Lead Zirconate Titanate Transducers.

Effective damage identification is paramount to evaluating safety conditions and preventing catastrophic failures of concrete structures. Although various methods have been introduced in the literature, developing robust and reliable structural health monitoring (SHM) procedures remains an open research challenge. This study proposes a new approach utilizing a 1-D convolution neural network to identify the formation of cracks from the raw electromechanical impedance (EMI) signature of externally bonded piezoelectric lead zirconate titanate (PZT) transducers. Externally bonded PZT transducers were used to determine the EMI signature of fiber-reinforced concrete specimens subjected to monotonous and repeatable compression loading. A leave-one-specimen-out cross-validation scenario was adopted for the proposed SHM approach for a stricter and more realistic validation procedure. The experimental study and the obtained results clearly demonstrate the capacity of the introduced approach to provide autonomous and reliable damage identification in a PZT-enabled SHM system, with a mean accuracy of 95.24% and a standard deviation of 5.64%.

Investigation of innovative designs of high-velocity channels for damping kinetic energy of flows.

In our contemporary world, demanding sustainable resource management, the study focuses on innovative fast flow channel designs. It investigates their efficacy in reducing flow kinetic energy, aiming to optimize water and energy management and diminish flood risks. Employing diverse methodologies, it analyzes and develops these designs, proving their substantial impact on stream energy management. These innovations not only enhance energy efficiency but also mitigate risks associated with excess kinetic energy, promoting safer stream management. This research significantly contributes to fluid dynamics and engineering, deepening the understanding of kinetic energy control in flows and offering potential solutions for water supply, environmental sustainability, and infrastructure safety challenges.

Occupational exposure to fine particulate matter in the reinforced concrete production and its association with respiratory symptoms and lung function.

BACKGROUND: Reinforced concrete production is widespread, but little is known about the occupational exposure to fine particulate matter (PM) in such workplaces, including from metalworking and concrete processing. Therefore, the aim was to characterize exposure to fine PM in the typical workplaces of the whole production cycle and to quantify the risk of respiratory symptoms and lung function in a cohort of reinforced concrete parts production industry. METHODS: At a reinforced concrete parts producing facility in Almaty, we collected 50 personal PM2.5 samples from the main exposure sites and the measured mass concentrations using gravimetric method. Workers also completed questionnaires on a detailed working history, respiratory symptoms (chronic obstructive pulmonary disease (COPD) Assessment Tool (CAT)), followed by spirometry. The association of cumulative dose with CAT score and forced expiratory volume in one second (FEV1)/forced vital capacity (FVC) was tested with multiple regression. RESULTS: The highest PM2.5 concentrations were found in the concrete-mixing unit (median 1180 µg/m3), followed by metalworking (510 µg/m3), armature workshop (375 µg/m3) and molding site (245 µg/m3), different from the concentrations in the office (29.5 µg/m3), Kruskall-Wallis p < 0.001. Cumulative PM2.5 dose, mg/m3-year (beta 0.10 (95% confidence interval (CI) 0.05; 0.15)) was strongly associated with CAT score, whereas production with FEV1/FVC (beta -4.96 (-8.31; -1.61)), independent of smoking and chronic bronchitis and sex. CONCLUSIONS: Mixing concrete and metalworks pose the greatest risk for worker's health in the reinforced concrete production from the inhalational exposure to aerosol, adversely affecting respiratory health.

Monitoring and Analysis of the Collapse Process in Blasting Demolition of Tall Reinforced Concrete Chimneys.

Aiming at the problem of displacement of collapse direction caused by the impact of the high-rise reinforced concrete chimney in the process of blasting demolition, combined with the monitoring methods such as high-speed photography observation, piezoelectric ceramic sensor, and blasting vibration monitor, the impact process of the 180 m high chimney was comprehensively analyzed. The results show that the chimney will experience multiple 'weight loss' and 'overweight' effects during the sit-down process, inducing compressive stress waves in the chimney. When the sit-down displacement is large, the broken reinforced concrete at the bottom can play a significant buffering effect, and the 'overweight' effect gradually weakens until the sit-down stops. The stress of the inner and outer sides of the chimney wall is obviously different in the process of collapsing and touching the ground. The waveform of the monitoring point of the piezoelectric ceramic sensor is divided into three stages, which specifically characterizes the evolution process of the explosion load and the impact of the chimney. The vibration induced by explosive explosion is mainly high-frequency vibration above 50 Hz, the vibration induced by chimney collapse is mainly low-frequency vibration below 10 Hz, and the vibration characteristics are obviously different. In the process of blasting demolition and collapse of high-rise reinforced concrete chimney, due to the impact of sitting down, the wall of the support tube is subjected to uneven force, resulting in the deviation of the collapse direction. In practical engineering, the control measures of chimney impact, blasting vibration, and collapse touchdown vibration should be fully strengthened to ensure the safety of the protection target around the blasting demolition object.

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.

Carbon Rovings as Strain Sensor in TRC Structures: Effect of Roving Cross-Sectional Shape and Coating Material on the Electrical Response under Bending Stress.

This study investigated the ability of electrically conductive carbon rovings to detect cracks in textile-reinforced concrete (TRC) structures. The key innovation lies in the integration of carbon rovings into the reinforcing textile, which not only contributes to the mechanical properties of the concrete structure but also eliminates the need for an additional sensory system, such as strain gauges, to monitor the structural health. Carbon rovings are integrated into a grid-like textile reinforcement that differs in binding type and dispersion concentration of the styrene butadiene rubber (SBR) coating. Ninety final samples were subjected to a four-point bending test in which the electrical changes of the carbon rovings were measured simultaneously to capture the strain. The mechanical results show that the SBR50-coated TRC samples with circular and elliptical cross-sectional shape achieved, with 1.55 kN, the highest bending tensile strength, which is also captured with a value of 0.65 Ω by the electrical impedance monitoring. The elongation and fracture of the rovings have a significant effect on the impedance mainly due to electrical resistance change. A correlation was found between the impedance change, binding type and coating. This suggests that the elongation and fracture mechanisms are affected by the number of outer and inner filaments, as well as the coating.

Corrosion Assessment in Reinforced Concrete Structures by Means of Embedded Sensors and Multivariate Analysis-Part 1: Laboratory Validation.

Reinforced Concrete Structures (RCS) are a fundamental part of a country's civil infrastructure. However, RCSs are often affected by rebar corrosion, which poses a major problem because it reduces their service life. The traditionally used inspection and management methods applied to RCSs are poorly operative. Structural Health Monitoring and Management (SHMM) by means of embedded sensors to analyse corrosion in RCSs is an emerging alternative, but one that still involves different challenges. Examples of SHMM include INESSCOM (Integrated Sensor Network for Smart Corrosion Monitoring), a tool that has already been implemented in different real-life cases. Nevertheless, work continues to upgrade it. To do so, the authors of this work consider implementing a new measurement procedure to identify the triggering agent of the corrosion process by analysing the double-layer capacitance of the sensors' responses. This study was carried out on reinforced concrete specimens exposed for 18 months to different atmospheres. The results demonstrate the proposed measurement protocol and the multivariate analysis can differentiate the factor that triggers corrosion (chlorides or carbonation), even when the corrosion kinetics are similar. Data were validated by principal component analysis (PCA) and by the visual inspection of samples and rebars at the end of the study.

Use of polypropylene fibers extracted from recycled surgical face masks in cement mortar.

The use of personal protective equipment (PPE), particularly single-use surgical face masks (FMs), has increased drastically owing to the ongoing Covid-19 pandemic. This study aimed to demonstrate the feasibility of utilizing recycled FM fibers in cement mortar. For this, FMs were used by removing the inner nose wires and ear loops and cutting them into two different sizes: 10 mm × 5 mm and 20 mm × 5 mm. The FMs were then introduced into five mixtures at 0 (control), 0.10, 0.15, 0.20, and 0.25 % by volume. The following mechanical properties of the mixtures were then tested: workability, density, porosity, water absorption, and the related strengths (compressive, direct tensile, and flexural). In addition, the microstructures of the mixtures were analyzed using a scanning electron microscope. The results revealed that introducing FM fibers, particularly an FM with a 5 mm diameter and 10 mm length, in the mortar increased both the tensile and flexural strengths. Among the various combinations of FMs studied, a mixture containing 0.15 % FMs exhibited the best performance. The findings of this research reveal that FMs can be reused as fibers to enhance the tensile and flexural strengths of cement mortar.

Progress in Sensors for Monitoring Reinforcement Corrosion in Reinforced Concrete Structures-A Review.

Non-destructive monitoring methods and continuous monitoring systems based on them are crucial elements of modern systems for the management and maintenance of assets which include reinforced concrete structures. The purpose of our study was to summarise the data on the most common sensors and systems for the non-destructive monitoring of reinforced concrete structures developed over the past 20 years. We considered systems based on electrochemical (potentiometry, methods related to polarisation) and physical (electromagnetic and ultrasonic waves, piezoelectric effect, thermography) examination methods. Special focus is devoted to the existing sensors and the results obtained using these sensors, as well as the advantages and disadvantages of their setups or other equipment used. The review considers earlier approaches and available commercial products, as well as relatively new sensors which are currently being tested.

Stress Monitoring of Concrete via Uniaxial Piezoelectric Sensor.

The uniaxial piezoelectric sensor was developed to overcome the problem of reflecting the output charge of the piezoelectric element as a combination of vectors in the three stress directions. The work performance of the uniaxial piezoelectric sensor under varying load patterns and load rates was investigated. The sensor was embedded in concrete to monitor stress, and its elastic modulus was used as the intermediate bridge to establish the correlation between the embedded sensor and the external sensor. Furthermore, a correction factor for the charge transformation strain was suggested to overcome the mismatching of the sensor's medium and the concrete. Considering related circumstances, a new stress monitoring method based on a uniaxial piezoelectric sensor was proposed, which can achieve stress whole-process monitoring in concrete and confining stress monitoring in the reinforced concrete column. The results reveal that through the proposed method, the output charge curve of the sensor has a substantial overlap with the stress waveform and high fitting linearity. The work performance of the sensor was stable, and its sensitivity was not affected by loading rate and load pattern. The sensor was embedded in concrete and can coordinate with the concrete deformation. The correction factor of strain obtained by the sensor embedded in concrete was 1.07. The relationship between the charge produced by the embedded sensor and its external calibration sensitivity may be used to implement the whole process of stress monitoring in concrete.

Application of Distributed Optical Fiber Sensing Technology to the Detection and Monitoring of Internal Swelling Pathologies in Massive Concrete Blocks.

This paper presents an experimental application of Distributed Optical Fiber Sensors (DOFS) for the Structural Health Monitoring (SHM) of concrete structures affected by internal swelling pathologies. In the framework of a large research project aiming to assess the possible extension of the operating lifetime of nuclear power plants from 40 to 60 years, massive blocks were cast from reactive concrete mixtures intended to develop delayed ettringite formation and alkali-silica reaction. These blocks were subjected to specific ageing conditions to initiate and accelerate the concrete pathologies. Some of the blocks were instrumented with DOFS bonded to the surface and embedded in the concrete. Using an interrogator device based on Rayleigh backscattering and a suitable procedure to eliminate temperature effects, distributed strain measurements were then performed at different time intervals. The first results of this ongoing study made it possible to demonstrate the feasibility and effectiveness of this sensing technology for detecting and monitoring expansion induced by swelling pathologies in representative-scale concrete structures.

Experimental Study and FEM Simulations for Detection of Rebars in Concrete Slabs by Coplanar Capacitive Sensing Technique.

In the present study, a relatively novel non-destructive testing (NDT) method called the coplanar capacitive sensing technique was applied in order to detect different sizes of rebars in a reinforced concrete (RC) structure. This technique effectively detects changes in the dielectric properties during scanning in various sections of concrete with and without rebars. Numerical simulations were carried out by three-dimensional (3D) finite element modelling (FEM) in COMSOL Multiphysics software to analyse the impact of the presence of rebars on the electric field generated by the coplanar capacitive probe. In addition, the effect of the presence of a surface defect on the rebar embedded in the concrete slab was demonstrated by the same software for the first time. Experiments were performed on a concrete slab containing rebars, and were compared with FEM results. The results showed that there is a good qualitative agreement between the numerical simulations and experimental 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.

Damage-Detection Approach for Bridges with Multi-Vehicle Loads Using Convolutional Autoencoder.

Deep learning has been widely employed in recent studies on bridge-damage detection to improve the performance of damage-detection methods. Unsupervised deep learning can be effectively utilized to increase the applicability of damage-detection approaches. Hence, the authors propose a convolutional-autoencoder (CAE)-based damage-detection approach, which is an unsupervised deep-learning network. However, the CAE-based damage-detection approach demonstrates only satisfactory accuracy for prestressed concrete bridges with a single-vehicle load. Therefore, this study was performed to verify whether the CAE-based damage-detection approach can be applied to bridges with multi-vehicle loads, which is a typical scenario. In this study, rigid-frame and reinforced-concrete-slab bridges were modeled and simulated to obtain the behavior data of bridges. A CAE-based damage-detection approach was tested on both bridges. For both bridges, the results demonstrated satisfactory damage-detection accuracy of over 90% and a false-negative rate of less than 1%. These results prove that the CAE-based approach can be successfully applied to various types of bridges with multi-vehicle loads.

Application of Distributed Fibre Optical Sensing in Reinforced Concrete Elements Subjected to Monotonic and Cyclic Loading.

Distributed fibre optical sensing (DFOS) is increasingly used in civil engineering research. For reinforced concrete structures, almost continuous information concerning the deformations of embedded reinforcing bars can be obtained. This information enables the validation of basic and conventional assumptions in the design and modelling of reinforced concrete, particularly regarding the interaction of concrete and reinforcing bars. However, this relatively new technology conceals some difficulties, which may lead to erroneous interpretations. This paper (i) discusses the selection of sensing fibres for reinforced concrete instrumentation, accounting for strain gradients and local anomalies caused by stress concentrations due to the reinforcing bar ribs; (ii) describes suitable methods for sensor installation, strain acquisition and post-processing of the data, as well as determining and validating structurally relevant entities; and (iii) presents the results obtained by applying DFOS with these methods in a variety of experiments. The analysed experiments comprise a reinforced concrete tie, a pull-out test under cyclic load, and a flexural member in which the following mechanical relevant quantities are assessed: the initial strain state in reinforcing bars, normal and bond shear stresses, deflections as well as forces. These applications confirm the benefit of DFOS to better understand the bond behaviour, but also demonstrate that its application is intricate and the results may lead to erroneous conclusions unless evaluated meticulously.

Measurement of Restrained and Unrestrained Shrinkage of Reinforced Concrete Using Distributed Fibre Optic Sensors.

Shrinkage is an important component of the behaviour of reinforced concrete (RC) structures, however, the number of variables that affect shrinkage make it a complex time-dependent phenomenon. Additionally, as new concrete materials with lower embodied carbon gain popularity, there is a need for an in-depth understanding into their shrinkage behaviour before they can be widely adopted by industry. Currently, the shrinkage behaviour of concrete is studied using discrete measurements on small-scale unrestrained prisms. Distributed fibre optic sensing (DFOS) potentially provides a method of measuring both restrained (with reinforcement) and unrestrained (without reinforcement) shrinkage in both small-scale specimens and structural elements. In the current study, methods of measuring distributed unrestrained shrinkage strains were developed and evaluated, and the restrained shrinkage strains in different types of structural members were studied. Unrestrained shrinkage strains were measured using fibres optic cables embedded in small concrete prisms, while restrained shrinkage strains were measured with fibres bonded to the longitudinal reinforcement. Unrestrained shrinkage strains were found to be highly variable (as large as 3800 microstrain range) depending on location, but further research needs to be undertaken to account for end effects, early-stage shrinkage, and bond between the fibre optic cable and the concrete. Restrained shrinkage strains from structural members revealed non-uniform shrinkage strain distributions along member length due to functional grading as well as high supplementary cementitious material concretes, suggesting that shrinkage models will need to account for this variability.

Toward a Structural Health Monitoring Methodology for Concrete Structures under Dynamic Loads Using Embedded FBG Sensors and Strain Mapping Techniques.

A data-driven-based methodology for SHM in reinforced concrete structures using embedded fiber optic sensors and pattern recognition techniques is presented. A prototype of a reinforced concrete structure was built and instrumented in a novel fashion with FBGs bonded directly to the reinforcing steel bars, which, in turn, were embedded into the concrete structure. The structure was dynamically loaded using a shaker. Superficial positive damages were induced using bonded thin steel plates. Data for pristine and damaged states were acquired. Classifiers based on Mahalanobis' distance of the covariance data matrix were developed for both supervised and unsupervised pattern recognition with an accuracy of up to 98%. It was demonstrated that the proposed sensing scheme in conjunction with the developed supervised and unsupervised pattern recognition techniques allows the detection of slight stiffness changes promoted by damages, even when strains are very small and the changes of these associated with the damage occurrence may seem negligible.

Wireless Temperature, Relative Humidity and Occupancy Monitoring System for Investigating Overheating in Buildings.

With the climate change we are experiencing today, the number and intensity of heatwaves are increasing dramatically, significantly impacting our buildings' overheating. The majority of the prefabricated concrete panel buildings in Hungary are considered outdated from an energy point of view. These buildings may be at greater risk from extreme weather events. To examine this, long-term monitoring measurements are needed. Therefore, we developed a unique, reliable, and cost-effective wireless monitoring system, which can track in real time the indoor air quality data (temperature, relative humidity, CO2) of the investigated apartment building, as well as users' habits, such as resident presence, window opening, and blind movement. The data were used to analyse and quantify the summer overheating of the dwelling and user habits. The measurements showed that the average temperature in all rooms was above 26 °C, and there were several occasions when the temperature exceeded 30 °C. Overheating in apartment buildings in summer is a significant problem that needs to be addressed. Further investigation of ventilation habits will help develop favourable ventilation strategies, and using these measurements in dynamic simulations will also help improve the models' validity for further studies.

The Smart Nervous System for Cracked Concrete Structures: Theory, Design, Research, and Field Proof of Monolithic DFOS-Based Sensors.

The article presents research on the performance of composite and monolithic sensors for distributed fibre optic sensing (DFOS). The introduction summarises the design of the sensors and the theoretical justification for such an approach. Lessons learned during monitoring cracked concrete are summarised to highlight what features of the DFOS tools are the most favourable from the crack analysis point of view. Later, the results from full-size laboratory concrete specimens working in a cracked state were presented and discussed in reference to conventional layered sensing cables. The research aimed to compare monolithic sensors and layered cables embedded in the same reinforced concrete elements, which is the main novelty. The performance of each DFOS nondestructive tool was investigated in the close vicinity of the cracks-both the new ones, opening within the tension zone, and the existing ones, closing within the compression zone. The qualitative (detection) and quantitative (widths estimation) crack analyses were performed and discussed. Finally, the examples of actual applications within concrete structures, including bridges, are presented with some examples of in situ results.