Concrete Structures Research Articles

Progressive collapse resistance of prestressed concrete frame structures with infill walls considering instantaneous column failure

AbstractPrestressed concrete frames with infill walls (IW‐PC frames) are commonly used in civil engineering as a structural element. The possibility of structures undergoing progressive collapse is a cause for concern due to its severe consequences. This has become a significant topic in the academic community in recent years. However, research on the resistance of IW‐PC frame structures to progressive collapse is still insufficient. Therefore, this paper investigated the dynamic effects of progressive collapse on the IW‐PC frame, reinforced concrete frame with infill walls (IW‐RC), prestressed concrete frame (PC), and common reinforced concrete frame (RC). The study was conducted using the finite element software OpenSees. The study revealed that the vertical displacement during stabilization of the IW‐PC frame increased by 92.5% and 71.7% compared to the IW‐RC frame and decreased by 93.9% and 92.6% compared to the PC frame for middle column and side column failure, respectively. Additionally, the IW‐PC frame exhibited the highest dynamic load carrying capacity, which was 7.67 and 7.56 times higher than that of the RC frame, respectively. The mechanical properties of the frame were altered by the coupling effect of prestressed tendons and infill walls, making the IW‐PC frame more monolithic.

Optimization and analysis of hysteretic energy dissipators in reinforced concrete frame structures of five, 10 and 15 stories

Frame structures are especially affected by earthquakes, due to their low lateral resistance. The use of energy dissipators may be a suitable solution for these structures; however, the effectiveness of these devices is questionable in low-rise buildings. We therefore analyze the behavior of these devices in frame buildings with varying numbers of stories (five, 10 and 15 stories). The use of hysteretic dissipators is found to considerably reduce the displacements of the bare structures, in addition to significantly improving their resistance, although the absolute accelerations are not reduced. Taller, more slender structures require less rigid dissipators with lower yield forces than shorter, more rigid structures. The effectiveness of the dissipators is also demonstrated by increasing the number of stories of the structures. The effectiveness of the dissipators increases as the rigidity of the structure decreases, as observed for the highest frame structures considered in this research, or as also occurs with the characteristics of the seismic records, such as their impulsivity. A high level of rigidity of the structures (as in low-rise structures or those with a significant number of stiffening elements, such as walls or braces) may have a significant impact on the behavior of the dissipators, due to the low displacement of the bare structures, which reduces their effectiveness. Finally, given the complexity of the different designs for dissipators in the scientific literature, this research offers a new, simple way to design dissipators based on the structural characteristics of the bare frames. To carry out this research, a nonlinear static analysis (push-over) and dynamic analysis are carried out using two different characteristic records, as part of a search for the optimal characteristics of the devices and to analyze how each of them affects the structural behavior in buildings with different numbers of stories.

Cost-oriented fire safety design of urban structures

Providing adequate fire safety for urban buildings is a life-saving goal, and can be addressed in different ways. Reported studies integrating fire safety and life cycle cost (LCC) of conventional urban structures are rare. To address this gap, a cost–performance framework was developed. Four- and seven-storey steel (with and without fireproofing coatings) and reinforced concrete (RC) structures were designed, first for conventional loads and then fireproofed to meet their required fire-resistance rating. A range of sprinkler spacings, from 3200 mm (based on NFPA 13) to 12 000 mm, was configured. Fire was simulated using a fire dynamic simulation tool and the results were used to monitor evacuation plans and structural responses. The steel structures were additionally analysed under the assumption that they were not fireproofed. Human and financial damage costs were then estimated and the LCC for every case/scenario was determined. Interestingly, the results showed that the optimum LCC was not achieved for cases designed based on NFPA 13; instead, a sprinkler spacing of 6000 mm led to the optimum LCC. It was also found that steel structures without fireproofing do not have a higher LCC if the sprinklers are placed over a shorter distance. This result could be particularly beneficial for existing structures that are not fireproofed.

Automatic damage detection and segmentation using deep learning algorithms in reinforced concrete structure inspections

AbstractTraditional methods of detecting concrete damage, such as manual inspection, are typically slow, labor‐intensive, and subjective. Integrating deep learning algorithms has automated this process, representing a significant advance in building damage inspection. Semantic segmentation, a technique within deep learning, is increasingly recognized for its capability to identify the location and shape of concrete damage accurately. Unlike basic deep learning approaches like classification and object detection, semantic segmentation not only recognizes damage but also delineates its boundaries, and has great potential in facilitating dimension measurement. However, multi‐level, high‐precision detection for various structural damage assessments remains an area requiring further research. This study created a database of reinforced concrete surfaces with pixel‐level, multi‐category semantic segmentation annotations for various levels of component damage, including cracks and spalling of concrete, exposure, buckling, fracture of reinforcing bars, etc. The performance of advanced deep learning segmentation algorithms, including U‐Net, DeepLab, K‐Net, and FastSCNN, was consequently trained and evaluated in detecting various types of concrete damage. All models show good accuracy of more than 98%, but lower F1 scores around 70%. U‐Net and K‐Net demonstrate relatively stable performance, indicating a certain degree of consistency and a high‐performance peak. In contrast, DeepLabv3's F1 score fluctuates significantly, suggesting the model may suffer from overfitting or other stability issues during training, resulting in a relatively low average performance. FastSCNN exhibits the highest potential, achieving the highest F1 score. In addition, this study also tested the performance of each model on new damage images and explored the impact of the dataset proportion ratio (training set vs. validation set), contributing to providing insights into each algorithm's suitability for various inspection scenarios. Finally, perspectives on current challenges and future directions in the field have also been given.

Prediction of punching shear strength in internal slab-column connections without shear reinforcement in reinforced concrete structures through multiple regression models

Prediction of punching shear strength in internal slab-column connections without shear reinforcement in reinforced concrete structures through multiple regression models

Concrete carbonation depth prediction model based on a gradient-boosting decision tree and different metaheuristic algorithms

Carbonation is a significant factor contributing to the instability of concrete structures. This study proposes two prediction models based on the gradient boosting decision tree (GBDT) to address the challenge of predicting concrete carbonation depth. This study integrates GBDT with two metaheuristic algorithms, particle swarm optimization algorithm (PSO) and sparrow search optimization algorithm (SSA), forming two hybrid models, PSO-GBDT and SSA-GBDT, aimed at enhancing prediction accuracy. Six influencing parameters (FA, t, w/b, B, RH, and CO2) were selected as input features to train and evaluate the hybrid models, with the concrete carbonation depth was used as the output. A database containing 883 groups of cases was established. Finally, three classic models, GBDT, ANN and SVR, were compared against the two hybrid models. To enhance model generalization, all models were subjected to five-fold cross validation. Four evaluation indicators (RMSE, R2, MAE, and VAF) and Taylor diagrams were employed to comprehensively assess these models. The results indicated that the two hybrid models exhibited superior prediction performance in the dataset (training set: R2, VAF>0.98, testing set: R2, VAF>0.96). The SSA-GBDT model achieved the highest prediction performance (RMSE= 2.7008, R2= 0.9639, MAE= 1.7691, VAF= 0.9627). Additionally, the SSA-GBDT model identified exposure time (t) as the feature with the greatest impact. This study demonstrates the applicability of the SSA-GBDT models in predicting concrete carbonation depth, offering a novel approach for improving accuracy in such predictions.

Defects detection in CFRP reinforced concrete structures by digital shearography

Defects detection in CFRP reinforced concrete structures by digital shearography

A systematic review of hybrid passive energy dissipating devices

Once earthquakes occur, they release a considerable amount of elastic strain energy, which is measured by Peak Ground Acceleration (PGA). To reduce the impact of this energy, buildings can be fitted with dampers. These include passive energy dissipation devices, which are effective in mitigating the effects of both high and low PGA. Hybrid dampers, which combine multiple devices, enhance strengths and minimize weaknesses, significantly improving a building’s seismic resilience by reducing roof displacement, drift, inter-story, floor acceleration, and base shear. These dampers are the focus of extensive research, emphasizing their role in seismic performance enhancement. Despite numerous studies, their application in concrete structures remains underexplored, presenting a vast scope for advancement in the design, development, and implementation of hybrid damping devices. These devices are particularly valued for their superior vibration control capabilities, offering a promising avenue for bolstering structural stability.

Real collapse responses of an RC flat plate structure under extreme overloading condition: Reversing load redistribution

Progressive collapse research of concrete flat plate structures, typically investigated through the alternate path (AP) method with a focus on column failure (removal), has often neglected the facts of real-life collapse events which are caused by slab overloading, leading to initial punching shear failure at slab-column joints. Progressive collapse of the structural systems triggered by slab-column joint failures exhibits distinct failure modes and resistance mechanisms from those triggered by column failures. Building upon our initial tests on the overloaded slab specimen under quasi-static loading regimes, this paper presents an innovative experimental study that closely mirrors the load-resistant mechanisms of the most common collapse incidents in flat plate structure systems in the real world. By successively stacking up gravity loads without additional interventions, the test accurately simulates the spontaneous failure conditions typical of flat plate structures. A key discovery of this research is the identification of a unique reversing load redistribution mechanism, a phenomenon not previously observed in our slab overloading tests or in studies focused on column removal scenarios. This discovery highlights the necessity of enhancing the post-punching capacity at the slab-column joints as a critical factor in preventing collapses due to slab overloading. In addition, this study provides an in-depth examination of the dynamic effects inherent in the progressive collapse of flat plate structures. Evaluating the dynamic increase factor and load amplification factor offers supplementary data pertinent to real-world collapses, providing essential insights for improving the resilience of flat plate structures against initial joint failure due to slab overloading.

Economical design strategies for reinforced concrete one-way slabs: a parametric approach considering material strength and rebar size

Despite the rapid advancements in optimization algorithms for economical reinforced concrete (RC) structures, their application to RC slabs has been significantly limited. The primary constraint is slab depth, governed only by deflection control criteria, which impedes further depth reduction—a key factor in cost optimization. The deflection check relies solely on one design parameter—span length, which is often fixed. This constraint limits the effectiveness of even advanced algorithms, which have consistently failed to deliver substantial cost savings in slab design. To address this issue, this study presents a detailed parametric investigation of three key cost-reducing factors in RC one-way slabs: steel strength, concrete strength, and reinforcing bar sizes, in accordance with the ACI 318–19 code. By bypassing algorithmic constraints, the study formulates optimal design strategies through extensive manual design iterations, providing novel trend-based solutions for cost-effective slab design. The findings indicate that for lower live loads, grade 280 steel offers a 20–30% cost reduction compared to higher-grade steel, despite the latter's strength advantage. However, for higher loads, grade 420 or 500 is recommended. Additionally, optimal bar sizes and concrete strengths are recommended for varying load conditions, offering practical design strategies. This approach provides designers with actionable insights to achieve cost-efficient designs without relying on traditional optimization algorithms.

Post-fire performance of hybrid GFRP-steel reinforced concrete columns

The fire safety requirements and knowledge on the use of Glass Fiber Reinforced Polymer (GFRP) bars as an alternative to conventional steel in reinforced concrete structures have been progressively evolving. This study investigates the post-fire axial performance of square GFRP reinforced concrete (RC) columns, addressing a significant gap in current knowledge. Five column specimens of the same cross-section and length were tested, where the main parameters considered are the clear cover depths (40, 50, and 65 mm) and the use of a 40-mm insulation on one column. The columns were subjected to a long 3-hour fire exposure per ASTM E119 standard without sustained loading. The columns were tested upon cooling under concentric axial loading. Despite the notable temperature differences between columns with 40 and 65 mm concrete cover (300 and 500 °C after 3 hours), the post-fire residual axial capacity appeared unaffected. This is primarily attributed to the minimal differences in the retained mechanical properties of GFRP bars at temperatures exceeding 200 °C. The study's findings indicate that GFRP longitudinal bars do not significantly influence the performance of columns in fires. Consequently, future research should focus on columns reinforced with GFRP ties as well. The predicted values of post-fire axial capacity closely match the experimental data for non-insulated columns, indicating a high degree of accuracy. The study provides design recommendations to enhance the fire performance of such elements. The use of insulation results in a substantial reduction of temperature at various depths of the column. Post-fire residual capacities for non-insulated columns were approximately 30 %, whereas insulated columns demonstrated a 60 % residual capacity.

A Mesoscopic Approach for the Numerical Simulation of a Mass Concrete Structure Construction Using Post-Cooling Systems

This study introduces an innovative numerical approach to simulate the construction of large concrete structures incorporating post-cooling systems employing the finite element method (FEM). The proposed methodology integrates critical construction parameters, including temperature control mechanisms, while accounting for concrete hydration and environmental conditions. Compared to traditional discrete models, this approach provides similar accuracy with substantially reduced computational costs, enhancing predictive capabilities in the thermal analysis of mass concrete. The method was applied to simulate the construction of a water intake pillar at the Tocoma hydroelectric plant in Venezuela, where the simulated results closely matched in situ temperature measurements. The findings highlight the method’s efficiency and accuracy in simulating post-cooling systems, offering a practical solution for improving the safety and cost-effectiveness in large-scale concrete construction projects.

Analysis of Capillary Water Absorption within Unsaturated Concrete Based on the Principle of Stationary Action

Capillary water absorption plays a critical role in the ingress of corrosive elements during the construction of concrete structures in corrosive environments. This study presented a novel approach for analyzing capillary water flow within unsaturated concrete based on the principle of stationary action. The flow of water within the concrete capillary pores can be regarded as a variational problem, while the principle of stationary action provides a method for determining the path solution. The evolution and distribution characteristics of water content and wetting front were explicitly determined using the exponential and power hydraulic functions. A simplistic yet effective approach for determining these hydraulic parameters was put forward based on the relationship between the position of the wetting front and the diffusivity parameters. The proposed approach exhibited enhanced theoretical robustness and entailed fewer hypotheses compared to existing methodologies. Furthermore, the material hydraulic parameters in the proposed approach can be determined explicitly. The governing equations for capillary water flow were derived in accordance with the principle of stationary action. Numerical simulations were carried out to verify the effectiveness of the proposed approach. The results demonstrated that the proposed approach can accurately predict capillary water flow and diffusivity parameters within unsaturated concrete. The findings of this study contribute to developing more effective strategies to mitigate moisture-related damage in concrete structures.

Seismic Behavior of Concrete Beam-Column Joints Reinforced with Steel-Jacketed Grouting

Joints in frame structures often fail before beams and columns in an earthquake and are a key part of reinforcement. In this study, to enhance the seismic performance of concrete frame structures, a steel-jacketed grouting composite reinforcement method is proposed by combining reinforcement technology, steel cladding technology, and eco-efficient materials from grouting technology. This method effectively utilizes the advantages of various materials, avoids major demolition and construction, and reduces waste and resource consumption. In order to verify the feasibility and effectiveness of the reinforcement method, one of the original joint specimens with a scale of 1:3 and one of the reinforced joint specimens were designed and tested. The experiments involved reversed cyclic testing of beam–column to measure its seismic behavior. The seismic performance indexes such as failure characteristics, hysteretic properties, and the energy dissipation capacity of the specimens were analyzed, and the corresponding finite element model was established. The influence of key parameters such as reinforcement range, steel plate thickness, and grout strength on its seismic performance was explored. The research shows that the method can effectively improve the seismic performance of the joints, and seismic performance indexes such as bearing capacity, ductility, and energy consumption of the specimens are significantly improved. The test results of the established finite element model are in good agreement. The variable parameter analysis of the finite element shows that the thickness of the steel plate has little influence on its bearing capacity. With the increase in the reinforcement range of the clad steel and the strength of the grouting material, the bearing capacity of the specimen increases. The research results can provide a reference for the reinforcement of frame structure joints.

Degradation law of bond strength of reinforced concrete with corrosion-induced cracks and machine learning prediction model

During long term operation service, reinforced concrete (RC) structures are subjected to corrosive media such as chloride salts, which results in the degradation of interface bond performance, subsequently reducing the load bearing capacity and service life of the structures. To accurately assess the durability of RC structures in environments such as oceans and salt lakes, this study combines experimental methods with machine learning (ML) techniques to explore the degradation patterns and predictive models of bond performance under chloride salt corrosion. Accordingly, a series of central pull-out tests were conducted based on an electrochemical accelerated-dry-wet cycle corrosion regime to investigate the degradation patterns of bond performance between corroded steel bars and concrete under different corrosion degree and corrosion-induced crack widths. By comparatively analyzing the evolution patterns of cracks and the morphology of the steel bars in reinforced concrete specimens and their failure modes, and integrating the mechanical degradation mechanism with chemical and microscopic corrosion mechanisms, an in-depth analysis of the corrosion-induced degradation patterns at the bond interface was performed. The internal relationships between corrosion degree, corrosion-induced crack width and bond strength were discussed, and the variation patterns of bond strength with corrosion-induced crack width and corrosion degree were explored. Moreover, the bond strength patterns were analyzed from an energy perspective. The results indicate that bond strength and bond energy decrease with an increase in the corrosion-induced crack width of the concrete. Additionally, based on the experimental data from this study, ML predictive models for bond strength were established using corrosion-induced crack width as the control variable. The results show that the ML-based bond strength predictive models fit well with the experimental data, offering higher accuracy and reliability compared to traditional bond strength models. The research findings provide significant theoretical basis and practical guidance for safety assessment, maintenance, reinforcement, and full-life design of corroded RC structures.

On the ductility of RC beam section: A revision and up to date of conclusions

In the context of reinforced concrete (RC) structures, standards aim to relate the ductility of these structures to the ductility of the steels used in their construction. Among other requirements, EC8 requires the use of special ductility steels to achieve high ductility in structures. After reviewing the relevant literature, this study aims to evaluate the impact of various parameters on the ductility at the cross-section level of RC elements subjected to bending. The study concludes that there is no consensus on the assessment of ductility, nor a standardized framework for the various alternatives. Additionally: i) Some contradictions exist regarding the impact of fundamental parameters on the assessment of ductility; ii) A one-size-fits-all methodology is impractical given the diverse factors influencing ductility in RC elements. It is proposed to consider the ductility of RC element sections as a function of the mechanical ratio of tensioned reinforcement. A proposal is also made to enhance the ductility of cross-sections without significantly increasing their strength: by increasing the width of the section while keeping other parameters unchanged.

Predictive and experimental assessment of chloride ion permeation in concrete subjected to multi-factorial conditions using the XGBoost algorithm

Chloride ions infiltrate concrete structures, causing corrosion of the steel reinforcement, which creates concrete spalling and decreases load-bearing capacity, ultimately leading to structural failure. Thus, it is essential to comprehend the diffusion process of chloride ions in concrete. Current models used to predict the chloride ion diffusion in concrete are hindered by poor accuracy due to their simplistic consideration of variables and the complex interplay of environmental and material factors affecting key parameters such as surface chloride concentration (Cs) and the chloride diffusion coefficient (D). These influences are challenging to capture with simple linear or non-linear relationships, and traditional machine learning models often overfit training data and underperform with new data. To address these challenges, this study presents physical model experiments conducted to explore the diffusion process of chloride ions under multi-field coupling conditions, examining chloride ion concentrations across different concrete layers under varying environmental temperatures (T), humidities (h), erosion times (t), water-cement ratios (W/C), and volumes of coarse aggregates (v). This study reveals the distribution patterns of chloride ions within concrete layers. An XGBoost machine learning predictive model was developed using environmental temperature, humidity, erosion time, water-cement ratio, and coarse aggregate volume as input variables, with Cs and D as output variables. The results show that when the water-cement ratio reaches 0.5 in high humidity, the SHAP value rises to 0.015, enhancing chloride diffusion. As erosion time exceeds 200 days and temperature surpasses 38 °C, the SHAP value peaks at 0.02. Additionally, when coarse aggregate volume is between 0.45 and 0.60, temperature changes have little effect on Cs. Additionally, a graphical user interface was developed for modeling Cs and D to enhance practical usability.

Bayesian updating of relationships between crack width and corrosion level in reinforced concrete based on a large set of experimental data

AbstractThe evaluation of rebar corrosion in reinforced concrete (RC) structures presents a significant challenge for structural engineers, with on‐site visual inspections and crack width measurements playing a key role in the preliminary assessment. A general approach is to use corrosion models to predict the corrosion level of existing structures, and subsequently update these predictions by means of on‐site data. In this process, empirical models are often applied that relate the observed damage, such as corrosion‐induced cracks, to the corrosion level. However, these models have large uncertainties and are often not applicable to conditions that deviate from the test setups used to derive the empirical relations. This research aims to expand the applicability of these practical, existing empirical models for on‐site corrosion assessment through Bayesian updating techniques. To this aim, a Bayesian framework is developed to update crack width–corrosion models. The Bayesian updating methodology is illustrated for a simple regression model, and for a more complex relation that accounts for additional variables. A novel online database (KUL‐edCCRC) is used that is published accompanying this paper. The obtained results illustrate that the updated models have better agreement with the experimental results, and it is found that the confidence intervals of the regression parameters decrease for an increasing number of observations, even for a small number of additional datapoints. The results hence prove the efficiency of the approach to integrate information from new observations with prior information to adjust the crack width–corrosion model for specific conditions or cases, resulting in a more relevant model as more information becomes available.

Elevated Temperature Effects on FRP–Concrete Bond Behavior: A Comprehensive Review and Machine Learning-Based Bond Strength Prediction

Because of their improved properties, FRP composites are vastly used in the strengthening of aged concrete infrastructures. However, it has been observed that their performance is highly compromised when exposed to high temperatures, as expected during fire incidents, which critically affects FRP–concrete bond behavior, hence affecting the overall efficiency of the strengthening system. This paper critically presents the available literature concerning the degradation of bond strength between FRP systems with concrete substrates due to increased temperatures. Both analytical and numerical bond–slip models developed for the prediction of bond strength degradation under such conditions are reviewed. A generally confirmed fact is that exposure to high temperatures, especially those reaching glass transition temperature (Tg) for epoxy adhesives, leads to bond degradation. Therefore, cement mortar-bonded CFRP textiles display better performance in fire endurance. This present paper also utilizes machine learning algorithms for the prediction of bond strength under elevated temperatures based on an experimental database of 37 beams. The nonlinear relationships and variable interactions in the developed model provide a reliable method for the estimation of bond strength with reduced extensive experimental testing, where the critical role of temperature in bond behavior is identified. This paper emphasizes the use of advanced predictive models to ensure the durability and safety of FRP-strengthened concrete structures in thermally challenging environments.

Enhancing Compressive Strength of Cement by Indigenous Individual and Co-Culture Bacillus Bacteria

Using a Taguchi experimental design, this research focuses on utilizing indigenous bacteria from the Danube River to enhance the self-healing capabilities and structural integrity of cementitious materials. Bacillus licheniformis and Bacillus muralis were used as individual bacterium or in co-culture, with a concentration of 8 logs CFU, while the humidity variation involved testing wet and wet–dry conditions. Additionally, artificial neural network (ANN) modeling of the compressive strength of cement samples results in improvements in compressive strength, particularly under wet–dry conditions. By inducing targeted bacterial activity, the formation of calcium carbonate precipitates was initiated, which effectively sealed formed cracks, thus restoring and even enhancing the material’s strength. In addition to short-term improvements, this study also evaluates long-term improvements, with compressive strength measured over periods extending to 180 days. The results demonstrate sustained self-healing capabilities and strength improvements under varied environmental conditions, emphasizing the potential for long-term application in real-world infrastructure. This study also explores the role of environmental conditions, such as wet and wet–dry cycles, in optimizing the self-healing process, revealing that cyclic exposure conditions further improve the efficiency of strength recovery. The findings suggest that autochthonous bacterial co-cultures can be a viable solution for enhancing the durability and lifespan of concrete structures. This research provides a foundation for further exploration into bio-based self-healing mechanisms and their practical applications in the concrete industry.