Concrete Structures Research Articles

An efficient deep learning approach for ultimate bond strength estimations of corroded bar and concrete

Abstract The corrosion behavior of the reinforced concrete structure depends heavily on the interfacial bond behavior between steel and concrete. Over the years, the deterioration of integrated reinforced steel has weakened this bond and potentially led to structural problems. Conventional methods of bond strength evaluation, such as pullout and bond beam tests, is frequently intrusive and tedious. Therefore, there is a growing need for non-intrusive, effective, and reliable forecasting algorithms capable of assessing bond deterioration caused by corrosion. Traditional algorithms for predicting bond strength make it difficult to capture the complex nature of steel-concrete bonds. The present study proposes two different deep learning algorithms, i.e., convolutional neural networks (CNN) and long short-term memory (LSTM), for predicting maximum bond strength in the presence of corrosion. The predictive model is based on a comprehensive dataset comprising 218 datasets from previous studies encompassing diverse input and output variables for predicting the models. The models were trained and tested using the given data to improve early predictions of corrosion-induced bond degradations. The predictive model’s effectiveness was assessed by applying various performance metrics. From this study, the CNN model exhibits higher accuracy and efficiency with mean absolute error, root mean square error, and mean absolute percentage error of 0.25, 0.28, and 95.72, respectively, for predicting ultimate bond strength estimations. The findings of this study provide an accurate and robust prediction model to improve the reliability and safety of the concrete structure by enhancing the residual load-bearing capacity of the concrete structure that has undergone corrosion.

Transfer learning for probabilistic localization of hidden cracks in concrete structures

AbstractThe utility of discriminative supervised learning models built using multiple training-data sources is investigated for hidden crack localization in concrete. Feed-forward neural network (FFNN) is chosen as the model architecture, and transfer learning is used to assimilate the information obtained from different sources (computational physics simulations and laboratory experiments). The labeled training data consists of values of a damage index and the known locations of hidden cracks. The classification models need to learn how the presence of damage (hidden cracks) affects the damage index at different sensors for different test conditions. To this end, diagnostic FFNN models are built by sequentially adding and training new hidden layers to assimilate labeled information from computer models (different model geometries, test conditions, crack lengths, crack locations) and laboratory experiments on a plain cement slab. These transfer learning-based models are then used to localize damage in concrete specimens that reflect real-world conditions (i.e., specimens with steel reinforcement and randomly distributed aggregate). The actual damage state in these specimens is determined by extracting cores and performing petrographic studies on the extracted cores. The damage probability estimated by transfer learning-based models is compared with the petrographic damage rating index (DRI) to identify the most suitable approach to train the diagnostic models. The transfer learning-based diagnostic methodology shows promise and could be used in various structural health monitoring applications, where sufficient labeled data are typically not available from a single data source.

Strengthening Effect Evaluation of Developed Stiff-Type Polyurea Sprayed on Masonry Beam Surface Under Static Loading in Experimental and Numerical Tests

Recently, deteriorated masonry structures aged over 30 years have shown serious structural problems. Simple and rapid maintenance plans are urgently needed for aging masonry structures. Polyurea (PU) is an effective retrofitting material for aging structures due to its easy spray application. This process saves time, reduces costs, and allows the structure to remain in use during retrofitting. However, a general PU is not suitable for retrofitting aged masonry and concrete structures due to its low stiffness. In this study, stiff-type polyurea (STPU) was selected as the reinforcement material for masonry structures. It was developed by modifying the chemical mix of general PU to improve stiffness. To evaluate the strengthening effect of STPU on masonry members under static loading, tests were conducted. The flexural load capacity of masonry beams with STPU-sprayed surfaces was assessed. Three different types of STPU applications were used to select the most efficient strengthening method. Reinforcing masonry structures with STPU allows brittle failure modes to achieve ductile behavior. This improves their structural performance under lateral stresses. The experimental data were used to calibrate FEM models for simulation. These models can be used for future parametric studies and masonry structural design.

Mechanical Analysis and Optimization of Concrete Structures Based on Advanced Finite Element Method

This article explores the mechanical analysis and optimization problems of concrete structures based on the Advanced Finite Element Method. By integrating advanced numerical techniques with practical engineering cases, this study aims to improve the safety and economy of concrete structure design. Firstly, the paper outlines the limitations of traditional finite element methods in concrete structure analysis, such as insufficient computational accuracy and low computational efficiency. Subsequently, by introducing AFEM, we significantly improved the accuracy and efficiency of the analysis. For example, when simulating a complex bridge structure, AFEM not only reduces the calculation time by about 25%, but also improves the accuracy of stress distribution prediction by more than 10%. In the optimization stage, we utilized the analysis results of AFEM and optimized the material consumption, cross-sectional dimensions, and reinforcement parameters of concrete structures through multi-objective optimization algorithms. A comprehensive data analysis underscores that the optimized concrete structure triumphantly meets all safety performance criteria while achieving a remarkable 12% reduction in material usage. This substantial material savings translates into a substantial 8% decrease in overall construction costs, significantly bolstering the project’s economic feasibility. Moreover, these cost savings not only amplify the project’s profitability but also play a pivotal role in enhancing the structure’s longevity and durability, thereby contributing to its sustainable performance over its entire service life. Furthermore, we delved into the capability of AFEM in simulating intricate phenomena such as the nonlinear behavior of concrete materials, crack propagation patterns, and the intricate interactions between steel reinforcements and concrete. These complex mechanical behaviors are crucial for the safety and stability of structures, and AFEM provides more comprehensive and accurate references for structural design by accurately simulating these behaviors. This article conducts in-depth research on the mechanical analysis and optimization of concrete structures based on AFEM, and demonstrates the significant advantages of AFEM in improving structural safety and economy through specific data. These research results not only provide new theoretical support and practical tools for concrete structure design, but also provide valuable references for future research and application in related fields.

Survey of automated crack detection methods for asphalt and concrete structures

Survey of automated crack detection methods for asphalt and concrete structures

Sustainable lightweight self-compacting concrete with coal bottom ash as aggregate and cement replacement

This study aims to examine the impact of utilizing coal bottom ash (CBA) sourced from an inactive power plant on the fresh and hardened properties of lightweight self-compacting concrete (LWSCC). To evaluate the workability of the LWSCC mixtures, slump flow, L-box, and sieve segregation tests were performed. The mechanical and physical properties were assessed through dry density, compressive strength and ultrasonic pulse velocity (UPV) tests. A total of five concrete mixes were developed: a control mix and four additional mixtures in which natural coarse aggregate was fully substituted with coal bottom ash aggregate (CCBA). Additionally, Portland cement was partially replaced with coal bottom ash powder (CBAP) at levels of 15%, 20%, and 25%. The results indicated that the use of CBA as a coarse aggregate enhanced the workability of LWSCC, though workability decreased as the proportion of CBAP increased. Nonetheless, the workability of all mixes remained compliant with the standards specified by the French Association of Civil Engineering (AFGC). Minimal variations in dry density and compressive strength were observed with the incorporation of CBA; however, these values remained within the acceptable limits for structural lightweight concrete. Furthermore, the UPV test demonstrated favorable durability for all LWSCC mixtures. Strong linear correlations were identified among the various measured properties, reinforcing the conclusion that CBA serves as an effective replacement for natural coarse aggregate. Moreover, the use of 15% CBAP as a partial substitute for Portland cement proved to be a feasible option for producing sustainable LWSCC.

Study of silicon dioxide nanoparticles on the rheological and mechanical behaviors of self-compacting geopolymer concrete

AbstractGeopolymer concrete (GPC) is a rising eco-conscious substitute for traditional cement-based concrete, bringing the construction industry closer to sustainability. Self-compacting geopolymer concrete (SCGC) enhances the concrete flowability and fills the congested reinforced areas without vibrators in concrete structures such as bridges, tunnels and canals. This study aims to analyze the impact of silicon dioxide nanoparticles (NS) on the rheological and mechanical properties of SCGC to optimize the dosage of NS in SCGC. For this purpose, NS (0–6%) blended in partially distributed binders of fly ash and ground granulated blast furnace slag (50:50) with 0.5 alkaline binder ratio, 2% superplasticizers (9 kg m−3) (MasterGlenium SKY 8233) and 12% extra water (54 kg m−3). Sodium silicate solution and sodium hydroxide ratio of 2.5 was used for this study. It is observed that SCGC with 3% NS replacement complied with the guidelines of EFNARC. According to the T50cm slump flow test, V-funnel test, and L-box test results meet the guidelines of up to 4% NS replacement, and 3% NS addition offers excellent mechanical properties in SCGC. This study concluded that the replacement of 3% of NS improved the fresh and hardened properties of SCGC, which can apply to construction.

Ductility and cracking behavior of UHPC-RC composite beam under bending test based on acoustic emission parameters

Ultrahigh-performance concrete (UHPC) possesses improved mechanical properties due to its high strength, durability, and toughness. Currently, there is a single method for detecting the cracking behavior and evaluating the ductility of the UHPC-reinforced concrete (RC) composite beam. As an important part of structural non-destructive monitoring techniques, the acoustic emission (AE) technique can be used to analyze the damage pattern of the structure by the variation characteristics of its individual parameters. On this basis, four-point bending tests were carried out on the RC beam and UHPC-RC composite beam, and the AE technique was used for ductility analysis and cracking behavior monitoring. The results showed that the UHPC material enhanced the flexural capacity of the UHPC-RC composite beam. The AE ring counts and energy parameters could be used to analyze the cracking process of the UHPC-RC composite beam during flexural damage. The proposed new ductility indices, AE ring counts ductility index (ARD) and AE energy ductility index (AED), differed from the displacement ductility indices within the range of 2 %, providing a new method for calculating ductility indices of concrete structures. During the failure stage, the UHPC-RC composite beam produced more tensile cracks at the early stage of cracking and the stage of damage compared to the RC beam. In addition, the b value of the RC beam had a large range of fluctuations during the failure stage, indicating that the RC beam produced more micro-cracks and macro-cracks and more damage compared to the UHPC-RC composite beam.

Robustness of Dry‐Assembled Precast Wall and Coupled Wall‐Frame High‐Rise Structures With Voided Prestressed Slabs

ABSTRACTPrecast concrete structures are recognized among the structural typologies that may mostly suffer from progressive collapse as a consequence of local unpredicted damage, also due to past accidents occurred in buildings employing old precast technologies neglecting the basic robustness criteria and characterized by poorly detailed and/or executed joints and connections. Although vertical ties may relatively easily be provided in both frame and wall panel structures by employing ductile connection devices, dry‐assembled precast systems avoiding concrete pouring in the whole superstructure typically struggle to provide horizontal ties as strong as required by the current standards concerning progressive collapse, in which they are not fully framed yet. Nevertheless, these systems may exploit alternative sources of robustness to stop collapse progression arising from their structural arrangement, the shape of the slab elements employed, and the slab mechanical connections. This article presents the results of a numerical investigation focused on the structural behavior of a dry‐assembled precast floor system consisting of prestressed box‐section slab elements employed in either wall panel or coupled wall‐frame structural systems following the loss of one or multiple primary load‐bearing vertical elements. Nonlinear static simulations of different loss scenarios occurring in prototypes of 100‐m‐tall high‐rise buildings were carried out based upon spread plasticity modeling of the structural elements and experimentally calibrated nonlinear behavior of mechanical connections.

A novel ultrasonic-velocity-based concrete carbonation monitoring method based on step-wise stretching technique

Carbonation is a common and slow process that occurs in cement-based material, resulting in durability degradation of reinforced concrete structures. In this research, the relative velocity change (d v/ v) of both ultrasonic direct waves and coda waves are extracted based on the step-wise stretching method for concrete carbonation monitoring. To this end, two-dimensional mesoscale models are established to investigate the ultrasonic propagation behaviors and assess the effectiveness of direct waves and coda waves on concrete carbonation monitoring. The numerical results indicate that direct waves could evaluate the initial carbonation, but exhibit limited sensitivity for severe carbonation. Conversely, the d v/ v values of coda waves show a linear correlation with carbonation depth in all conditions. Accelerated concrete carbonation experiments are conducted to validate the numerical findings. The experimental results and numerical findings mutually corroborate, validating the effectiveness of velocity changes of coda waves on all-stage concrete carbonation monitoring. This research contributes a novel method for monitoring concrete carbonation and enhances the understanding of ultrasonic wave propagation in concrete.

Integrated Machine Learning and Region Growing Algorithms for Enhanced Concrete Crack Detection: A Novel Approach

In the field of construction engineering, the cracking of concrete structures is a common engineering problem, which has a great impact on the overall stability and service life of the engineered structure. During structural repair, crack detection is the most critical step. Automatic detection significantly reduces the engineering cost and human factor error compared with manual detection. However, due to the changeable environment of the project site and different image specifications, using a single algorithm makes it difficult to balance high efficiency and high accuracy. In this study, we designed a combined recognition method including the region growth algorithm and machine learning regression that can achieve a tradeoff between accuracy and efficiency. Firstly, the regression method learns the image features of the dataset and the specific region growth threshold, and the regression function is trained by using the open-source dataset to determine the region growth threshold using the characteristics of the images included in the tests. The region growth algorithm is used to expand the threshold from the seed points of the image to obtain the crack recognition results. The results show that this method improves the accuracy of SSIM by 7% compared with the traditional region growth algorithm, and does not significantly increase the computational cost, with an increase of 0.78 s per photo process. Compared with the deep learning method, the recognition accuracy of SSIM is decreased by 5.96%, but it takes less resources and has high efficiency.

Finite Element Analysis of Two-Way Reinforced Concrete Slabs Strengthened with FRP Under Flexural Loading

This paper presents a finite element (FE) model of reinforced concrete two-way slab strengthened using fiber-reinforced polymer (FRP) sheets. This model was validated against experimental data from the literature and it showed acceptable prediction accuracy. Although carbon-FRP (CFRP) is the most commonly used composite in repairing and strengthening reinforced concrete structures, it is important to consider other types of FRP composites such as the eco-friendly basalt-FRP (BFRP) and the newly developed polyethylene terephthalate-FRP (PET-FRP). Therefore, the validated FE model was utilized to perform a parametric study for slabs having different values of concrete compressive strength (ranging from 20 to 80 MPa) and strengthened with other types of FRP. The results show that CFRP provides the highest strength enhancement with a 34.5% increase in the ultimate load, while PET-FRP provides the lowest improvement with an increase of 11.2%, compared with unstrengthened slab. The results also show that the concrete compressive strength (fc’) has moderate influence on the ultimate load. For example, increasing fc’ from 20 MPa to 80 MPa increased the predicted ultimate load for CFRP-strengthened slab from 15% to 62%. The FE model provides a suitable prediction for the ultimate strength and deformability of the strengthened two-way slabs that helps in better understanding of the performance of strengthened slabs and allows engineers to optimize design parameters.

Seismic risk estimates for reinforced concrete structures with incorporation of corrosion and aftershock

Numerous reinforced concrete (RC) structures are exposed to aggressive environments, such as chloride, mainshock and aftershock. These environmental and extreme loads have the potential to increase the seismic risks during structure's service life. This study introduces a practical probabilistic methodology to estimate the seismic risk of corroded RC frame subjected to mainshock-aftershock sequences. In this methodology, a time-variant modeling strategy is used to simulate the geometrical and mechanical properties of structural degraded materials. The Bayesian updating theorem is employed to calibrate the demand models, which are then used to estimate the fragilities and confidence intervals considering the model uncertainties. A Copula-based approach is conducted to generate joint probability of mainshock and aftershock intensities and the seismic hazard of the mainshock-aftershock scenario. Finally, the seismic risk and associated confidence intervals are estimated by integrating over all mainshock and aftershock intensities levels. A typical corroded RC frame structure is used to illustrate the proposed methodology. The results show that the contribution of corrosion and aftershock would lead a 10 times higher seismic risks compared to scenarios considering only mainshock damage. It is necessary to account for the influence of both corrosion and aftershock in seismic design.

Analytical model for RC beams with embedded prestressed Fe-SMAs

Prestressed iron-based shape memory alloy (Fe-SMA) strengthening is an effective means to enhance the performance of reinforced concrete (RC) structures. The applied prestress level significantly influences the strengthening efficiency. However, the absence of a reliable model predicting the applied prestress hinders the efficient design of prestressed Fe-SMA strengthening solutions. To fill this gap, the current study proposes an analytical model, with which a constant zone and a transfer zone are identified in strengthened RC beams. The constant zone, with zero interfacial shear stress, sustains a constant prestress level, while the transfer zone, experiencing non-zero shear stress, gradually reduces the Fe-SMA tensile stress from the prestress level to zero. Validated through experiments, the proposed model accurately predicts the applied prestress levels and prestress-induced deflections in RC beams strengthened with Fe-SMAs and more conventional carbon fiber reinforced polymers (CFRPs). Further analysis reveals that the transfer zone length ranges from 4 to 7 characteristic lengths, and it is proportional to the logarithm of the applied prestress level. Simplifying a constant prestress level within the beam span yields sufficiently accurate prestress analysis.

Absorption and Release mechanism of superabsorbent polymers and its impact on shrinkage and durability of internally cured concrete – A review

Superabsorbent polymers (SAP) are considered as a promising assortment of chemical admixtures which present novel prospects for influencing the properties of cementitious materials in the fresh, setting, and hardened states. However, the diversity of commercial SAP, especially in terms of monomers, crosslinking agents, and synthesis methods, still poses many obstacles in their large-scale application. Therefore, this review aims to shed light on intrinsic absorption and release mechanism of various SAP and its impact on rheological properties, mechanical properties, shrinkage properties, and durability for cementitious materials. Our research findings indicate significant differences in the water absorption and release mechanisms between ionic and non-ionic SAP. The swelling of non-ionic SAP is dependent on the osmotic pressure formed between the polymer network and the solution. In contrast, ionic SAP not only exhibits osmotic properties but also generates charged hydrophilic groups in solution, which is a crucial factor contributing to polymer swelling. The water absorption characteristics of SAP facilitate the regulation of early cement matrix hydration and ensure the long-term stability of the concrete structure. These attributes are crucial for maintaining stable development in cement hydration and ensuring the overall structural integrity over time. The knowledge gleaned from this review offers a robust framework for the further research work on SAP and serves as support for practical application of internally cured concrete in construction engineering that need to withstand challenging environmental conditions.

Life Prediction and Reliability Evaluation of Reinforced Concrete Frame Structures Considering the Collision Response Under Earthquake Conditions

Firstly, in this study, we utilize the high-order element model (truss link model) simulation method in OpenSees (3.7.0) software and verify the feasibility of this method by comparing it with the shaking table test. Secondly, the structural dynamic response of adjacent structures with different performance levels, spacing, and layout forms under large earthquakes is analyzed, and the corresponding structural failure probability is studied. Furthermore, the life distribution within the design service life of the structure is predicted according to the nonparametric Kaplan–Meier estimation model. Finally, the reliability of adjacent structures is evaluated by using the joint engineering demand parameters. The analysis method of replacing the theoretical analysis based on engineering experience and certainty in the current specification with probability analysis is proposed, which provides a more reliable theoretical basis for decision-making regarding the reinforcement, maintenance, or demolition of structures in the later stage.

Advancement of Finite Element Method Solver Used in Dam Safety Monitoring System by Interpolation of Pore Pressure and Temperature Values

In this paper, we focused on the advancement of Dam Monitoring Software that incorporates the Finite Element Method (FEM), as these large infrastructure constructions are crucial for ensuring a dependable water supply, irrigation, flood control, renewable electric energy generation, and safe operation, which is of utmost importance to any country. However, the material properties and geotechnical environments of dams can change (deteriorate) over time, while the standards and legal norms that govern them become more and more rigorous, so in order to accurately assess the state of a dam and detect any concerning behavior, the software must be updated as well. The custom-developed FEM solver, unlike many commercial alternatives, is adaptable and can be reconfigured to function within a Dam Monitoring System. In this paper, we present the procedure for interpolating numerical values at measurement points, when the position of the measurement point does not align with the node of the element, allowing for additional instrument locations to be added to the monitored system without the need for remeshing the numerical model. This procedure is used to compare the actual pore pressures and temperature values of the concrete dam structure with the prediction of the numerical model, and the agreement is much greater with the new interpolation algorithm in comparison to the nearest nodal values, with the average relative difference for pore pressure reduced from 8.89% to 8.10%, justifying this implementation.

Unified uniaxial compressive constitutive model for C20–C120 concrete based on stochastic damage theory

The concrete uniaxial compressive stress-strain model is essential for analyzing reinforced concrete members and structures. However, models applicable to C20–C120 concrete are still relatively rare. Moreover, the previous empirical model construction methods may not reflect the physical mechanisms and randomness of concrete failure well. To this end, the database was constructed by collecting and analyzing uniaxial compressive stress-strain curve test results from different scholars. The cubic compressive strength in the database ranges from 9.9 MPa to 137.3 MPa. The adaptability of the stochastic damage model was validated using the database of test curves. Subsequently, stochastic damage models for C20–C120 concrete were calibrated based on concrete compressive strength grouping curves, and practical analysis models were suggested. The results indicate that the calculated curves closely match the test curves when the plastic strain evolution parameters η1,c and η2,c in the stochastic models are taken as 0.001–0.30 and 0.19–0.25, respectively. The adaptability of the stochastic damage model is satisfactory, but the plastic strain evolution laws vary for different concrete compressive strengths, influenced by mix proportions and the probability of failure of each component on the meso-scale. When η1,c is calculated by the empirical formula proposed in this paper and η2,c is approximated as 0.22, the calculated curves for C20–C120 concrete are consistent with the mean values of the test curves. The proposed practical analysis models provide comparable accuracy to stochastic damage models, eliminating the need for iterative calculations, making them convenient for engineering applications.

A New Performance-Based Test for Assessing Chloride-Induced Reinforcement Corrosion Resistance of Geopolymer Mortars

The widespread adoption of geopolymer concretes in the industry has been slow, mainly due to concerns over their long-term performance and durability. One of the main causes of concrete structures’ deterioration is chloride-induced corrosion of the reinforcement. The reinforcement corrosion process in concrete is composed of two main stages: the initiation phase, which is the amount of time required for chloride ions to reach the reinforcement, and the propagation phase, which is the active phase of corrosion. The inherent complexities associated with the properties of precursors and type of activators, and with the multi-physics processes, in which different transfer mechanisms (moisture, chloride, oxygen, and charge transfer) are involved and interact with each other, have been a major obstacle to predicting the durability of reinforced alkali-activated concretes in chloride environments. Alternatively, the durability of alkali-activated concretes can be assessed through testing. However, the performance-based tests that are currently available, such as the rapid chloride permeability test, the migration test or the bulk diffusion test, are only focusing on the initiation phase of the corrosion process. As a result, existing testing protocols do not capture every aspect of the material performance, which could potentially lead to misleading conclusions, particularly when involving an electrical potential to reduce the testing time. In this paper, a new performance-based test is proposed for assessing the performance of alkali-activated concretes in chloride environments, accounting for both the initiation and propagation phases of the corrosion process. The test is designed to be simple and to be completed within a reasonable time without involving any electrical potential.

Analysis and evaluation of suitability of high-pressure dynamic constitutive model for concrete under blast and impact loading

Concrete demonstrates complex dynamic mechanical behaviors under the influence of blast or impact loads, and there are inherent limitations in experimental and theoretical methods when dealing with highly nonlinear problems. As computational technologies and mechanics continue to evolve, it is possible to conduct high-fidelity simulations of the transient response of concrete structures subjected to intense dynamic loads. Such simulations play a crucial role in revealing the propagation laws of stress waves, the progression of damage, the mechanisms of structural failure, and in conducting protective engineering design. An accurate concrete material model is essential for conducting numerical studies. This article reviews the development of high-pressure dynamic constitutive models for concrete in recent years from both experimental research and theoretical modeling perspectives, focusing on the analysis and evaluation of the modeling methods and main shortcomings of the equation of state(EOS), strength model, and damage model. Using single-element numerical simulations under a single loading path and numerical calculations of engineering cases under complex loading paths, a systematic analysis and comparison were conducted on the predictive capabilities of the HJC, RHT, KCC, and CSC models, as well as the newly developed Kong-Fang and Yan-Chen models. It pointed out the impact of high-pressure mechanical behavior of concrete and the cumulative effect of damage under hydrostatic pressure on the calculation results. Finally, a discussion was conducted on the inherent flaws, applicability, and research difficulties of local constitutive models of concrete in the finite element method. This provides a reference for the selection and research of constitutive models when conducting numerical analysis of the blast and impact resistance of concrete structures.