Reinforced Concrete Components Research Articles

Autonomous damage segmentation of post-fire reinforced concrete structural components

The existing methods for detecting fire damage in reinforced concrete (RC) structures rely heavily on manual visual inspection, which is time-consuming and challenging due to poor visibility and safety concerns. To address this, an automatic damage segmentation network at the pixel level is proposed. Using a dataset of 403 high-resolution images of fire-damaged RC components, the study employed MobileNetv3 as the encoder in a UNet model, enhanced with three average pooling layers. Among four submodules tested, SPPF demonstrated the best performance in the MB-SPPF-UNet network, achieving an MIoU of 82%, a 7% improvement over the Baseline-UNet. While OCRNet showed the best performance among ten state-of-the-art networks, its MIoU was still 8% lower than MB-SPPF-UNet. The generalization capabilities of the networks were evaluated using four crack datasets, with MB-SPPF-UNet showing a performance decrease of 5%-10%. However, this is acceptable given its primary application in segmenting post-fire concrete spalling and exposed reinforcement. The study aims to establish an efficient damage segmentation network using deep learning for quick assessment and reference in firefighting, rescue, and structural repair efforts.

A layer discrete particle based soft contact model in corroded rebar monitoring using linear and nonlinear ultrasonic guided waves

In offshore reinforced concrete (RC) structures, the phenomenon of rebar corrosion is widespread, seriously threatening the durability of the structures. However, the issue of rebar corrosion detection especially for the early corrosion situation is also challengeable. It is of great significance to use ultrasonic guided waves (UGWs) for monitoring the situation of the rebar corrosion entire process. In this paper, a mechanical model was used to establish the relationship between different rebar corrosion expansion states and layer-surface contact pressures in the layered RC components with radial cracks. Based on this model, a soft pressure-dependent contact 2D model in Abaqus was used to simulate the local corrosion layer. A linear and nonlinear signal joint analysis (LNSJA) method using PZT-based UGWs was proposed to monitor rebar corrosions, and an LNSJA-based rebar corrosion damage index (RCDI) for corroded RC components was proposed. The proposed method which can effectively detect both the micro- and macro-thickness as well as local area of rebar corrosion layer was validated by the relevant experiment and finite element analysis (FEA).

A convergence‐enhanced Timoshenko beam element for the stochastic nonlinear analysis of reinforced concrete components

AbstractAn effective and precise physical model is generally required for the performance assessment of reinforced concrete (RC) components. In this study, a convergence‐enhanced Timoshenko beam element considering finite deformation is proposed for the stochastic nonlinear analysis of RC components. The unified framework of the rank 2 correction matrix is obtained by approximating the iterative matrix through the linearly independent basis vectors, and two types of Broyden–Fletcher–Goldfarb–Shanno (BFGS) operators are constructed. Then, a consistent solution scheme with superior numerical stability and efficiency is proposed based on the second kind of BFGS operator and improved quasi‐Newton method. Accurate discretization of the displacement fields and description of nonlinear sectional behavior are achieved by the high‐order Lagrangian interpolation functions, concrete damage‐plasticity model and J2 plasticity model. In an updated Lagrangian formulation, these shape functions and constitutive relationships are used to develop a complete secant stiffness with higher‐order terms in the strain tensor and residual force vector. To validate the proposed Timoshenko beam element, numerical applications involving material nonlinearity and geometrical nonlinearity are conducted. The numerical stability and efficiency of the solution scheme, the shear‐locking free technique, and the oscillation of axial force are thoroughly examined and discussed by solution of RC columns, cantilever beam and progressive collapse of planar RC frame. Finally, the parametric study considering the geometric and material randomness indicates that the RC column designed for flexural failure mode suffered considerable degradation of loading capacity and a larger scatter of residual load.

Research on the Estimation Model of Energy Consumption Capacity for Reinforced Concrete Components with Axial Force

The research group utilized the estimation model of energy consumption capacity for reinforced concrete components without axial force to assess the energy consumption capacity of 92-reinforced concrete components from the PEER database, which were subjected to axial force and bending. The study also examined the impact of design parameters, including longitudinal reinforcement ratio, transverse reinforcement ratio, axial compression ratio, and shear-span ratio, on the estimation results. The research findings revealed that when applying the estimation model of energy consumption capacity for reinforced concrete components without axial force to calculate the energy consumption capacity of reinforced concrete components with axial force, there was a significant deviation rate in the estimation of cumulative energy consumption. The relationship between the deviation rate of cumulative energy consumption and longitudinal reinforcement ratio, axial compression ratio, and shear-span ratio remained unclear. However, a more apparent linear relationship was observed with the transverse reinforcement ratio. By conducting a quantitative analysis of the transverse reinforcement ratio, the researchers proposed an modified estimation model of energy consumption capacity for reinforced concrete components with axial force. Nonetheless, the accuracy of the modified estimation model was found to be high within the range of 0–250,000 kN mm of cumulative energy consumption. For cumulative energy consumption exceeding 250,000 kN mm, further experimental and theoretical research is still required to enhance the reliability of the modified estimation model.

Ultrasonic array tomography-oriented subsurface crack recognition and cross-section image reconstruction of reinforced concrete structure using deep neural networks

Manual interpretation from technicians with prior expertise knowledge is still needed for ultrasonic array tomography when detecting subsurface damages in reinforced concrete (RC) structures. Ultrasonic scans were usually utilized in qualitative manners that have not been leveraged for quantification purposes in prior studies and practical engineering. This study presented an ultrasonic array tomography-oriented subsurface crack recognition and pixel-wise cross-section image reconstruction method for RC structures based on deep neural networks (DNN). B-scans were acquired by testing entity RC components with preset artificial cracks using shear-wave low-frequency ultrasonic array. A DNN with the basic encoder-decoder architecture was developed, which was improved by introducing skip connections and residual modules to adapt to semantic structure of ultrasonic B-scans. A supervised generative adversarial DNN was proposed to generate fake but convincing B-scans for data augmentation. Results reported that F-scores were higher than 78 %, and satisfactory agreements were observed between ground truth annotations and reconstructed cross-section images, substantiating the effectiveness of the proposed method. Furthermore, with the physical information-assisted registration, individual local reconstructed cross-section images were combined to global representations indicating crack distribution in the entire component to facilitate convincing structural assessments.

Seismic performance of novel low-damage stair system: Precast reinforced concrete stair isolated with a sliding joint

This research presents a solution that can solve the negative diagonal support effect of the traditional precast reinforced concrete stair with rigid connection (TS) and adverse stress centralization on the fragile reinforced concrete (RC) flight. This innovative low-damage stair system utilizes a sliding joint at the bottom of the precast RC stair (SS). Experimental and numerical investigations were implemented on specimens TS and SS to systematically evaluate the seismic responses of the presented low-damage stair system. Experiment results confirm that specimen SS could effectively eliminate the detrimental diagonal support effect of conventional stair systems and the unexpected overlarge stress concentration of the RC components. Therefore, the RC flight and landing beam of the proposed low-damage stair systems remained undamaged at the inter-story drift ratio up to the final level of 4.00%. Taking advantage of the sliding joint, specimen SS behaves with excellent hysteretic performance and energy dissipation capacity. Furthermore, parametric analysis results reveal that the concrete strength remarkably affects the seismic responses of the innovative stairs, while the impact of the friction factor of the sliding joint on the seismic performance is asymmetric during positive and negative loadings.

FE model to define impacting resistance behavior of RC beams protected by AlSi10Mg buffer interlayer

Inspired by the biological structure of the beetle elytra, the bio-inspired honeycomb column thin-walled structure (BHTS) is designed as lightweight buffer interlayer to prevent the key parts of reinforced concrete (RC) beams of engineering structures under overload impact from destruction. The mechanical properties of the additive manufacturing technology selective laser melting (AM-SLM) BHTS material AlSi10Mg were determined through quasi-static and medium-speed uniaxial compression tests. Based on the drop weight test models, the maximum impact force and duration, maximum displacement and residual displacement, energy proportion and other indicators, the impact of the buffer interlayer on the dynamic response of RC beams were compared under the drop weight test at different heights. The results showed that the proposed BHTS buffer interlayer has a very significant impact protection effect on the RC beam: the average energy absorption (EA) of the BHTS buffer interlayer is 25.65 % of the total internal energy, which greatly reduces the energy input into the RC beam. In addition, based on the study of the drop weight test simulation of BHTS buffer interlayer, an approximate calculation model for predicting the ultimate displacement of BHTS-RC beam is proposed, which can be used as an important method for assessing the damaging degree of reinforced beam under impact load quickly. BHTS can be used as a replaceable structure to avoid impact damage to RC components in structural engineering.

Experimental Study on Wedge Anchorage Performance of Prestressed BFRP Laminates for Flexural Strengthening of Reinforced Concrete Components

Experimental Study on Wedge Anchorage Performance of Prestressed BFRP Laminates for Flexural Strengthening of Reinforced Concrete Components

Research on the Non-Planar Deformation Behavior of Reinforced Concrete Components Based on Size Effect

Research on the Non-Planar Deformation Behavior of Reinforced Concrete Components Based on Size Effect

Data‐driven modeling and prediction on hysteresis behavior of flexure RC columns using deep learning networks

SummaryHysteresis behavior of structural components has been one of the research focus for the structural engineering community for decades, comprehensively determines the structural performance and safety, and plays an important role in structural disaster mitigation. It is of great significance to continuously monitor structural responses and accurately characterize structural hysteresis. Currently, the nonlinear properties of real‐world structural components cannot be obtained before its failure. Thus, a historical database is collected firstly. Then, a data‐driven analysis method is proposed for predicting hysteresis behaviors of reinforced concrete (RC) columns. A bidirectional LSTM (BLSTM) network is developed to model and predict hysteresis curves. The data with unfixed lengths are specially processed to simultaneously guarantee a uniform size and avoid data loss, and the clipping layers are inserted in the model to clip off inferior predictions and improve the accuracy. This methodology is systematically studied and validated by employing a sythetic database generated by numerical simulation and the full‐scale experiment database named PEER database. Result shows that proposed BLSTM can predict the hysteresis curves of the RC components with acceptable accuracy and robustness. Moreover, the interpretability analysis is performed on identifying the learning and prediction principle of the BLSTM model on hysteresis prediction and its accuracy variation under different model architectures.

Assessment on concrete constitutive models for numerical simulations of vehicular collisions with RC bridge piers

Accident statistics showed that vehicular collision caused serious shear damage or failure of reinforced concrete (RC) bridge piers and even resulted in overall collapse of bridge structures. For the resistance evaluation of RC components under impact loads, numerical simulations have been performed by adopting different concrete constitutive models and obtained different results, e.g., Karagozian and Case Concrete (KCC) model and Continuous Surface Cap (CSC) model widely used, as well as Kong-Fang (KF) model recently proposed. This study mainly evaluates the accuracy of the above three concrete constitutive models in numerical analyses of vehicular collisions with RC bridge piers. Firstly, basic mechanical behaviors of these models are systematically compared based on single element simulations. Then, the scope of investigation is extended to component level by simulating a drop hammer test on RC beam and a lateral impact test on RC column, and the dynamic behaviors of impacted RC components are deeply compared. Finally, these models are further compared at structure level through performing the refined numerical simulation of a real truck colliding with a prototype bridge and evaluating its global structural behaviors and damage. The results show that, (i) KF model well reflects the post-peak softening behavior, stable strain-rate effect and independence of mesh size of concrete materials under uniaxial loading conditions at different loading rates; KCC and KF models both capture the brittle to ductile transition of concrete materials with increasing confinement pressure; (ii) KF model yields a better prediction in dynamic responses and damage characteristics of impacted RC beam and column, while KCC and CSC models both overestimate the damage degree; (iii) KF model better simulates the shear failure of RC pier in the real accident and provides the reasonable structural dynamic responses, but KCC and CSC models much underestimates the damage degree of impacted bridge pier and adjacent components.

Suggestions for repairing various defects in the NSM strengthening mechanism with hybrid rebars for RC beams: Experimental and numerical study

NSM bars enhance flexural strength of RC components but degrade ductility and transform the failure mechanism from ductile flexure to brittle shear. Failure due to debonding and other causes still has an impact on the strengthening system's efficiency. The focus of this research will be on how successful hybrid steel-CFRP/GFRP rebar strengthening is in enhancing the overall behaviour of reinforced concrete (RC) beams under flexure. Six rectangular RC beams were cast and strengthened using various semi-NSM procedures (120 mm width by 300 mm depth by 3000 mm length), then tested to failure in four-point bending tests. The beams were classified into four categories: (I) control beam, (II) strengthening with mild steel bar, (III) strengthening with mild steel-CFRP/GFRP hybrid rebars, and (IV) strengthening with high tensile steel-CFRP/GFRP hybrid rebars. To enhance the overall behaviour of RC beams, particularly beam ductility, and to avoid corrosion, FRP confinement was utilized in association with steel bars. End anchoring with strengthening rebars aims to limit unexpected de-bonding of NSM bars at higher loads, resulting in superior flexural performance of the RC members. In compared to the others, strengthening with mild steel-GFRP rebars enhances ductility and beam stiffness. This study also suggests and validates a 3D numerical model to simulate the performance and failure of NSM Strengthening mechanism with hybrid rebars for RC beams using the finite element (FE) software “ANSYS”. Regarding the behaviour of load-deflection, moment-curvature, load-crack width, and mode of failure, the FE results were accurate representations of the experimental results. An additional FEM specimen was examined to corroborate the effect of the experimental parameters.

Image-based reinforced concrete component mechanical damage recognition and structural safety rapid assessment using deep learning with frequency information

Safety assessment of post-event damaged structures is vital and significant because it directly affects life security, structural repair, and economic loss, especially in earthquakes. This study applies deep learning (DL) approaches to structural health monitoring and post-earthquake reconnaissance. An image-based procedure for reinforced concrete (RC) component damage recognition and structural safety rapid assessment is established. An RC component mechanical damage image dataset is built first, and comprehensive component damage recognition tasks (CDRTs) are proposed. EfficientNet-V2 is selected as the baseline model to perform CDRTs. The discrete wavelet transform module that integrates frequency information is then proposed to improve the performance and interpretability of the baseline model. Finally, component and structural damage are linked and structural safety rapid assessment methods based on the results of CDRTs are proposed. This study overcomes the limitations of manual inspections and advances the applications of DL techniques in civil engineering by incorporating frequency information.

Hybrid physics-BP neural network-based strength degradation model of corroded reinforcements under the simulated colliery environment

This study proposes a hybrid physics-artificial neural network (ANN)-based framework for developing a physics-based strength degradation model of corroded reinforcements taken from deteriorating reinforced concrete (RC) beams respective under a single harsh colliery environment and coupled action of corrosive environment and sustained load. This proposed degradation model can well account for the impacts of random variables (e.g., the diameter of reinforcing steel bars) on the strength degradation behavior of corroded reinforcements. The main contributions of this study include the following aspects: Firstly, based on the experimental results, a BP-ANN surrogate model is established to substitute the experiment method for generating the residual yield and ultimate strengths of corroded reinforcements under different exposure conditions. In addition, a new data sample is formed with the experimental and simulated data obtained by this trained surrogate model. Applying the Bayes theorem, the physics-based strength degradation model respecting to corrosion degree and diameter of corroding reinforcements is constructed. The posterior probability distributions of unknown model parameters are developed, which is beneficial for representing the epistemic uncertainty in assessing the service reliability of deteriorating RC components and structures. Finally, a sample from experimental data is randomly selected to verify the accuracy of this physics-based strength degradation model. Analytical results indicate that the diameter of reinforcements as well as the coupled action of corrosive simulated colliery environment and sustained load have considerable influences on the strength degradation of corroded reinforcing steels. This study provides an effective approach to developing a physics-based strength degradation model of corroding reinforcements and highly improves the use efficiency of experimental results.

Development of Fragility Models for Precast Concrete Shear Walls and Columns Connected by Grouted Sleeves

Performance-based seismic design and assessment of precast concrete (PC) structures have been widely studied. Fragility models for PC components have rarely been developed; however, it is a critical foundation for the above-mentioned design and assessment. Hence, this study was focused on developing fragility models for PC shear walls (PCSWs) and columns (PCCs) connected using grouted sleeves. To this end, 46 full-scale PCSWs and 45 full-scale PCCs, connected using grouted sleeves, were collected, and their test results were analyzed. Six damage states (DSs) were recommended based on the typical damage characteristics and with reference to the associated reinforced concrete (RC) components. Subsequently, the fragility models of PCSW and PCC were established for each DS using the drift ratio as the engineering demand parameter. In addition, the influence of the design axial load ratio on the fragility models was revealed. Through the comparison of fragility models between the PC and RC components, differences in parameters and curves of fragility models were quantitatively compared. These study achievements can serve as valuable references for performance-based seismic design and assessment of PC structures connected using grouted sleeves.

Vision‐based seismic damage detection and residual capacity assessment for an RC shaking table test structure

AbstractIn this paper, the existing postearthquake performance assessment framework for reinforced concrete (RC) building structures is improved by adding a new feature of the computer vision‐based damage detection. In this framework, visible seismic damage is classified and quantified from photographs of damaged RC components using the developed deep convolutional network (CNN) Damage‐Net of semantic segmentation, and then the mechanical property degradation factors of components determined from the detected damage states are used to update the numerical model. Pushover analysis of the updated model assesses the residual capacity of the damaged structure. Large‐scale shaking table tests of a three‐story RC building structure, which was heavily instrumented with sensors and recorded with a large volume of photographs, were used as a case study to demonstrate the improved postearthquake performance assessment framework. The vision‐based approach accurately detected multicategory seismic damage of the test structure and effectively estimated the residual crack widths and angles under various lighting, image acquisition, and surface conditions. The updated model, which incorporated the mechanical property degradation of the damaged components, provided accurate estimate on the fundamental vibrational frequencies of the damaged structure after various levels of seismic motion shaking, which matched well with the system identification results. Using the mechanical property reduction factor values recommended by FEMA 306 & Chiu et al., pushover analysis of the updated models provided residual capacity curves that reasonably captured the measured hysteretic responses of the structure. In addition, the damage states of components as estimated by the vision‐based methods were also compared with the measured plastic hinge rotation data. The successful implementation of the vision‐based assessment in this test case indicates its potential for application in the postearthquake evaluation of buildings.

A Review of Durability Issues of Reinforced Concrete Structures Due to Coastal Soda Residue Soil in China

Soda residue soil (SRS) is a man-made engineering foundation soil formed by soda residue; it is mainly distributed in coastal areas in China. SRS is rich in a variety of corrosive salts, among which the concentrations of chloride ions are about 2–3 times that of seawater. These highly concentrated chloride ions migrate and diffuse in reinforced concrete (RC) structures built on coastal SRS through multiple transport mechanisms. However, current research on the durability of RC structures exposed to the coastal SRS environment has not led to the publication of any reports in the literature. SRS may be classified by analyzing the quantitative relationships among the corrosive ions it contains. In this paper, the deterioration of RC structures due to the corrosive saline-soil environment in China is discussed, and advances in RC structure durability under such circumstances are reviewed. Our findings show that a corrosive environment, especially when this is a result of coastal SRS, has a significant influence on the deterioration of RC structures, greatly threatening such buildings. A series of effective measures for enhancing the durability of RC structures in saline soil, including improvements in concrete strength, reductions in the water–binder ratio, the addition of mineral admixtures and fiber-reinforcing agents, etc., could provide a vital foundation for enhancing the durability of RC structures which are at risk due to coastal SRS. Vital issues that must be investigated regarding the durability of RC structures are proposed, including the transport mechanism and a prediction model of corrosive ions, dominated by chloride ions (Cl−), in SRS and RC structures, the deterioration mechanism of RC materials, a long-term performance deduction process of RC components, durability design theory, and effective performance enhancement measures. The findings of this paper provide some clear exploration directions for the development of basic theories regarding RC structure durability in coastal SRS environments and go some way to making up for the research gap regarding RC structure durability under corrosive soil environments.

Experimental Study on a Reinforced Concrete Element to Extract the Durability Index with the Automated Visualization

Reinforced concrete (RC) durability is a crucial feature to estimate long-term quality and structural performance. The degradation model is vital for the resource planning of maintenance projects. This model will extract data by updating the status of structures and trending the components’ state over time in terms of durability. Surface erosion, spalling, cracks, and other defects exposed on RC components lead to increase factors adversely affecting concrete durability in structures. This research presents an approach based on automated visualization for extracting quantitative indexes as well as visual inspection without the subjective interspersion of humans or probable human errors during the inspection. The durability index (Di) will extract according to damage probability and defects growth in order to extract the severity of failure and risk. Measurement operation by automated software has been double-checked by manual measurement tools, and data will verify randomly in this method. The results show that, in this component, the damaged area increases by 24% after a definite time. According to degradation models, this component may pass the relative thresholds for the limit for the state of operations to fail. This significant difference between expected time and designing time determines the Di, equal to 5 out of 10.

Investigation on efficiency of cathodic protection applied on steel in concrete cylinder with CFRP wrap serving as anode

The combination of strengthening and corrosion protection by using multi-functional material is an attractive topic recently. In this paper, carbon fiber reinforced polymer (CFRP) was utilized as a repair material and an anode of impressed current cathodic protection (ICCP) owing to its excellent mechanical and electrochemical properties. The effects of protection methods (non-protection, epoxy coating, CFRP wrapping, CFRP wrapping and ICCP), pre-corrosion degrees (3 %, 6 %, and 12 %), and ICCP current densities (5, 20, and 80 mA/m2) on corrosion performance of reinforced concrete (RC) cylinders were investigated by means of electrochemical measurements. The results showed that the double protection provided by CFRP wrapping and ICCP worked much better than the sole epoxy coating or the sole CFRP wrapping when an appropriate current density was applied. The optimal ICCP current density depended on the pre-corrosion severity of RC specimen. A current density of 20 mA/m2 resulted in the greatest improvements in charge transfer resistance for the 6 % and 12 % pre-corroded specimens while the optimal current density was 5 mA/m2 for the 3 % pre-corroded specimen. Moreover, CFRP wrapping led to greater increases in charge transfer resistance than epoxy coating. It suggested that, besides the contribution of epoxy to isolate harmful substances, confinement provided by CFRP wrapping may benefit corrosion alleviation in a further step by densifying the core concrete and consequently impeding ion transport and oxygen availability. This work provides a basis for further optimization design of ICCP-strengthening technique applied on corroded RC components to achieve higher efficiency.

Seismic performance of reinforced concrete frame with lead viscoelastic damper under mainshock-aftershock sequences

This study proposes a strategy of utilizing novel lead viscoelastic damper (LVD) to improve the seismic performance of the weakened reinforced concrete (RC) frame under mainshock-aftershock sequences. The working mechanism and mechanical behavior of the LVD were experimentally investigated through cyclic loading tests. A six-story LVD frame was designed to have similar structural characteristics to the original RC frame and modeled in OpenSees. Incremental dynamic analyses were carried out on both RC frame and LVD frame under mainshock only scenario and mainshock-aftershock sequences. The mainshock and aftershock seismic performance of the LVD frame and RC frame was assessed at various damage states. Results show that the developed LVD characterizes by high effective stiffness, excellent damping capacity, low-frequency sensitivity, and stable hysteretic performance. Although with weakened RC components, the LVD frame still has about 25.1% higher median PGA demands than the RC frame at various mainshock damage states, indicating that the LVD system plays a significant role in enhancing the mainshock seismic performance of the RC frame. The aftershock IDA results confirm that thanks to the excellent damping capacity of the LVD, the weaker RC frame with LVD systems outperforms the original RC frame by about 21.08%, 10.59%, and 13.33% in median aftershock PGA demands, aftershock collapse capacity, and residual aftershock collapse capacity, respectively.