Reinforced Concrete Structures Research Articles

Experimental Evaluation of an Innovative Connection for the Reinforcement of Existing Infilled RC Buildings

The retrofitting of existing reinforced concrete (RC) buildings with cross-laminated timber (CLT) panels presents a promising approach for enhancing seismic performance and overall structural resilience. However, effective integration of CLT with existing RC structures poses significant challenges, particularly concerning the design of connections between CLT panels and the RC structure. This paper introduces a novel connection that addresses these challenges by focusing on both structural and architectural considerations. Structurally, the connection is engineered to provide optimal stiffness, strength, and deformation capacity, ensuring robust performance under seismic and dynamic loads. Architecturally, the design incorporates a predefined weak component that facilitates easy access and rapid replacement of damaged parts, thereby reducing downtime and maintenance efforts. The proposed connection was evaluated through a series of monotonic and cyclic loading tests, demonstrating its structural efficiency and reliability. The results indicate that the new connection system not only meets the necessary structural requirements but also offers practical benefits for maintenance and repair, contributing to the overall sustainability and resilience of retrofitted RC buildings. This innovative approach represents a significant advancement in the field of structural retrofitting, providing a viable solution for integrating CLT panels into existing RC frameworks.

Thin-layer Ultra-High-Strength Engineered Cementitious Composites (UHS-ECC) reinforced with small-diameter FRP bars for structural strengthening

This study proposed a novel strengthening system for reinforced concrete (RC) structures using a thin layer of Ultra-High-Strength Engineered Cementitious Composites (UHS-ECC) reinforced with small-diameter Fiber-Reinforced Polymer (FRP) bars. Experimental investigation, digital image correlation analysis, and numerical simulation were conducted to evaluate the flexural performance and failure mechanism of RC beams strengthened with 20-mm UHS-ECC layers and 3-mm FRP bars. It was found that the 20-mm UHS-ECC layer alone improved the load capacity of RC beams by 8.3 %, though with reduced deflection, whereas incorporating two 3-mm FRP bars increased load capacity by up to 40.4 %, without sacrificing deflection. Failure in all specimens was caused by concrete crushing; however, FRP-reinforced UHS-ECC layers mitigated early crack localization, significantly enhancing both strength and ductility. This study also revealed that cast-in-place FRP-reinforced UHS-ECC layers exhibited higher load capacity and could avoid ECC/concrete interfacial cracks compared to epoxy-bonded prefabricated layers. A three-dimensional finite element model was proposed for the strengthening system, and the flexural behavior was successfully predicted. It is revealed that the FRP-to-UHS-ECC bond had a marginal influence on performance, while the bond at the UHS-ECC-to-concrete interface significantly impacted flexural behavior. Remarkably, the small-diameter FRP bar achieved 75 % of its tensile strength at the ultimate stage. These findings underscore the potential of FRP-reinforced UHS-ECC layers as an effective solution for enhancing the mechanical and durability performance of RC structures.

Corrosion level estimation in reinforced concrete beams by acoustic emission sensing and selective crack measurements

AbstractAccurate corrosion assessment plays an important role in ensuring structural safety of reinforced concrete (RC) structures. However, on‐site assessment of existing concrete structures presents many challenges, including high costs, limited inspection timeframes, and difficult accessibility. To facilitate inspection‐based corrosion assessment, this paper presents a novel approach by combining short‐term acoustic emission (AE) monitoring with selective crack width measurements for corrosion level (CL) assessment. AE sensing is a monitoring technique which can detect ongoing internal degradation mechanisms by analyzing ultrasonic waves emitted by the damage process. Yet, two major challenges arise on site: (1) practical limitations prevent continuous AE monitoring over the structure's entire lifetime and (2) AE can only generate relative results in the absence of reference measurements. This paper addresses both challenges to bridge the gap between laboratory experiments and on‐site monitoring of corroding RC structures. First, short periods of AE monitoring data are analyzed to investigate the potential of AE sensing over limited timeframes. Second, AE data of corroding RC beams are combined with sparse crack width measurements in order to obtain absolute CLs. The proposed methodology is experimentally validated by corroding eight beams with varying dimensions, corrosion zones, and reinforcement layouts. The experimental results prove that the estimated CLs closely match the rebar mass losses, with a mean absolute error of 1.53% CL for beams reaching up to 14% CL, confirming the potential of the combined AE and crack width measurement technique as an efficient and accurate condition assessment approach. Moreover, AE sensing provides detailed spatial variability of the rebar corrosion in the monitored zone, which is challenging to obtain with conventional techniques. By using this dual‐technique approach, shorter monitoring periods prove nearly as effective as continuous AE monitoring in accurately estimating CLs.

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.

Behavior of Reinforced Concrete Circular Columns Subjected to Double Curvature Buckling Moment

This paper investigates experimentally and numerically structural performance of reinforced concrete (RC) circular column under double curvature buckling moment. RC circular columns are popularity embraced in RC structures not only owing to its esthetic, attractive appearance and easy for construction, but also for its better contribution for stress resistance. However, the corner or edge columns may be exposed to edge moment (EM) transferred from beam–column joint with double curvature effect. The current investigation proposes to understand the structural exhibition of such columns subjected to double curvature buckling edge moment generated at beam–column joint considering some parameters. A total number of 11 full-scale RC circular columns were constructed, installed, and then tested up to failure studying four parameters. These parameters were: clear height-to-diameter ratio (λ), longitudinal and lateral steel ratios (μ and ρ, respectively) and concrete type. The λ ratio has been introduced with: 4.89, 4.51 and 4.14. The μ ratio has been presented by: 2.2%, 2.3%, 2.6%, and 3.1%, while the ρ ratio was employed with: 0.84%, 0.93%, 1.00% and 1.18%. Three concrete types were studied; one of them was normal concrete (NC), while the other ones were high-performance concretes (HPCs) which were engineered cementitious composite (ECC) and high-strength fiber reinforced concrete (HSFRC). Experimental outcomes revealed that all studied parameters could affect and upgrade the structural performance; however, both longitudinal reinforcement and height-to-diameter ratio provided significant enhancement (about 51%–64%). Parallel to those exploited experimentally, nonlinear three-dimensional finite element models (FEMs) were installed, executed and established considering experimental outcomes producing an acceptable model with an under/over-estimation of about 4%.

Numerical Investigation on the Hysteretic Performance of Self-Centering Precast Steel–Concrete Hybrid Frame

To improve the construction performance and seismic resilience of precast reinforced-concrete frame structures, an innovative self-centering precast steel–concrete hybrid frame has been proposed and subjected to cyclic loading tests. In this paper, a comprehensive numerical analysis was conducted to further investigate the frame’s hysteretic behavior. Initially, a numerical model was developed using the finite element software OpenSees. Numerical analyses of two frame specimens were conducted, demonstrating good agreement between the numerical and experimental hysteretic characteristics, thus validating the model’s accuracy. Subsequently, based on the numerical simulations, a quantitative comparison of hysteretic performance between a novel frame and a traditional reinforced-concrete frame of the same scale was performed. While the proposed frame exhibited slightly lower initial stiffness and energy dissipation capacity than the traditional frame, it outperformed in terms of load-carrying capacity and self-centering ability. Finally, parametric analyses were carried out to assess the influence of various design parameters on the hysteretic performance, including friction force in the web frictions devices, initial post-tensioned force of the prefabricated steel–concrete hybrid beams, the steel arm length, and the column longitudinal reinforcement ratio. The results showed that increases in these four parameters improved the load-carrying capacity and initial stiffness of the proposed frame. Additionally, an increase in the friction force, steel arm length, or column longitudinal reinforcement ratio enhanced the frame’s energy dissipation capacity, while an increase in the initial post-tensioned force or a decrease in the friction force enhanced the frame’s self-centering capacity.

Corrosion Assessment in Reinforced Concrete Structures by Means of Embedded Sensors and Multivariate Analysis—Part 2: Implementation

The economic cost of repairing corrosion-affected reinforced concrete structures (RCSs) means that reliable and accurate assessment and early detection methods must be sought after. Conventional techniques, such as visual inspections, or measuring either cover layer resistivity or the corrosion potential, are methods that require accessibility and involve personnel having to travel to take in situ measurements. Monitoring by embedded sensors is a much more efficient approach that allows early detection by remote sensing. This work presents the implementation of a new measurement protocol regarding the existing monitoring system called INESSCOM (Integrated Sensor Network for Smart Corrosion Monitoring). Along with the corrosion intensity measurement in embedded sensors, it also proposes monitoring the double layer capacity of the sensors’ responses. It aims to determine, along with the rebars’ corrosion rate, the triggering agent of the corrosion process. This study was carried out using three reinforced concrete scaled columns that were exposed to different environments. The results demonstrate with this new protocol that the remote INESSCOM monitoring system can establish the corrosion rate and identify the precursor agent of corrosion (carbonation or chlorides), even when the recorded corrosion rates are similar.

RC dapped-end beams with various reinforcement layouts: An experimental investigation

Reinforced Concrete (RC) discontinuity regions (D-regions), such as dapped ends, can represent vulnerable zones in existing RC structures since they can exhibit a brittle failure mechanism. In common design practice, the ultimate strength of D-regions is assessed through Strut and Tie (S&T) models. Current design codes recommend two resistant mechanisms for dapped ends but do not provide information about the identification of steel bars involved in the resistant mechanism when the reinforcement is formed by distributed bars. An experimental campaign on dapped-end beams with various reinforcement layouts, including configurations without diagonal bars or with one or multiple layers of diagonal bars, has been carried out at the Structures and Material Testing Laboratory of the Department of Civil and Environmental Engineering in Florence. Results allow for identifying the influence of different reinforcement layouts on the crack pattern at the Service Limit State (SLS) and on the post-peak response. Both the strain on bars and the crack opening displacements are investigated until failure. The work highlights that layouts with distributed diagonal bars reduce the crack widths at the SLS compared to other configurations, increase the ductility, and allow for the timely detection of damaged dapped ends during in situ inspections.

Analysis of dynamic response and impact resistance of corrugated plate-reinforced concrete under simulated rockfall

In this study, corrugated plate material is used as the internal mold for the reinforced concrete structure, proposing a combined shelter structure designed to protect against rockfall hazards in mountainous areas. The research focuses on the impact of low-velocity rockfall (below 150 km/h) directly hitting both the reinforced concrete structure and the corrugated plate and reinforced concrete combined shelter structure. The accuracy of the numerical simulation results is validated through comparative analysis with indoor model experiments. Further investigation reveals the impact resistance and failure characteristics of RC (Reinforced Concrete) structures, CPRC(D) (Corrugated plate double-layer reinforced concrete composite structure) structures and CPRC(S)（Corrugated plate single-layer reinforced concrete composite structure）, with the residual resistance index (RRI) introduced for the quantitative analysis of the residual load-bearing capacity of the protective structures post-impact. The results indicate that the shape of impact craters and the maximum impact force obtained through numerical simulations closely match the experimental findings, demonstrating the validity of the simulation parameters. Under the same impact energy, the maximum peak stress at various points in the CPRC structure is reduced by more than 50 % compared to the RC structure. Damage to the RC structure under rockfall impact is primarily characterized by central penetration failure and extensive development of diagonal cracks. In contrast, the CPRC structure effectively suppresses penetration, dent deformation, and crack propagation, significantly reducing the volume of concrete damage and the proportion of rebar damage, with the residual resistance index consistently remaining above 0.27.The corrugated plate and reinforced concrete combined structure outperforms the reinforced concrete structure in several impact resistance factors, including maximum impact force, effective stress extremes, structural damage, and penetration depth. Additionally, after reinforcement with corrugated plates, the CPRC structure maintains a high level of impact resistance in most scenarios, even when the lower layer of rebar is eliminated, providing a theoretical basis and preliminary feasibility proof for the lightweight modification of the structure.

Green Compression-Cast Concrete Material and Its Fiber-Reinforced Polymer (FRP)-Reinforced Concrete Structures

Concrete manufacturing is a significant contributor to carbon emission. This paper presents an extensive study on compression-casting, which is a newly developed and highly efficient concrete manufacturing technology. Compression-casting is a mechanical process that improves the properties of all types of concrete materials without using chemical additives or mineral admixtures. It is particularly suitable for producing high-quality concrete from secondary/substandard or waste/recycled aggregate materials, thereby promoting green, low-carbon, and sustainable construction. This paper introduces compression-casting and presents the experimental results of the material and relevant fiber-reinforced polymer (FRP)-reinforced structures. Various applications of the compression-casting technology on inferior concrete materials, including rubber, recycled aggregate, desert sand, and recycled powder concrete, have also been introduced. The behavior of FRP-reinforced structural members of compression-cast concrete (CCC) is evaluated and compared with that of normal concrete (NC). It is concluded that compared to NC, CCC has the following advantages: improved mechanical properties, microstructure, durability, cement saving, economy, and reduced carbon dioxide emissions. Finally, we provide recommendations for addressing CCC structure brittleness. As a result, this method can reshape the future of concrete manufacturing.

Corrosion Behavior of Steel Rebars in Historical Reinforced Concrete Structures Submitted to Chlorides in the Simulated Concrete Pore Solution

Corrosion Behavior of Steel Rebars in Historical Reinforced Concrete Structures Submitted to Chlorides in the Simulated Concrete Pore Solution

Biogalvanic cathodic protection applied to a large-scale laboratory pilot concrete pier: influence of the bioanodic surface, tidal variations and temperature

The management of corrosion in reinforced concrete (RC) structures is crucial for addressing the challenges posed by aging infrastructure, particularly in marine environments where the aggressiveness of seawater can severely impact durability. This study explores a novel approach known as BioGalvanic Cathodic Protection (BGCP), inspired by Benthic Microbial Fuel Cells, for the electrochemical maintenance of RC exposed to marine corrosion. BGCP utilizes electroactive microorganisms naturally present in marine sediments to form bioanodes on conductive materials, which provide protective electrical currents to partially submerged RC structures. A pilot study involved a 3-meter-high concrete pier, which was partially immersed in natural seawater and sediments with embedded bioanodes. The current distribution from the BGCP system to the steel was monitored for over a year under various configurations, including changes in steel surface area and the number of bioanodes, while simulating tidal variations and monitoring ambient temperature. Results indicated that BGCP effectively demonstrated cathodic prevention for passive steel, with improved performance observed when multiple bioanodes were utilized. Indeed, the total current density received by the steel was in the range of [-0.2;-2 mA/m2] at all times. The current distribution varied with tidal changes, peaking at the air/water interface. A notable correlation emerged between temperature and current output, suggesting better performance at elevated temperatures. Although CP has not yet been achieved on actively corroding steel, BGCP offers significant potential for delaying corrosion initiation through the development of a self-sustaining and environmental-friendly technology.

Machine learning techniques for predicting the peak response of reinforced concrete beam subjected to impact loading

To meet the growing need for resilient structures in seismic and high-impact zones, accurate prediction of the response of reinforced concrete (RC) beams under impact loads is essential. Traditional methods, such as experimental testing and high fidelity finite element models, are often time consuming and resource intensive. To address these challenges, this study investigates various ensemble and non-ensemble machine learning techniques—including support vector machine, gaussian process regression (GPR), k-nearest neighbor (KNN), gene expression programming, random forest, decision tree, boosted tree, adaptive boosting tree, gradient boosting algorithm, stochastic gradient descent, and artificial neural network—for predicting the peak response of RC beams under impact loads. A set of 145 experimental data points from 12 different sources is used to train and evaluate these machine learning models. Key parameters in the data include beam width and depth, span, reinforcement ratios, concrete strength, steel yield strength, deflection, and impact characteristics. Except for KNN, all models showed satisfactory generalization capabilities with R2 values over 0.8. Statistical errors such as RMSE, a-10 index, MAE, and a-20 index are within acceptable limits. The GPR model is the most effective with R2 value of 0.95. Moreover, Shapely analysis identified beam depth, impact velocity, and beam breadth as critical factors. Overall, this study demonstrates the efficacy of machine learning in accurately predicting the behavior of RC structures under impact loads, providing valuable tools for civil engineers in design and analysis.

Experimental and numerical study on polypropylene fibers reinforced concrete slabs with corroded reinforcement under monotonic loading

The degradation of structural performance due to reinforcement corrosion in reinforced concrete (RC) structures and the subsequent measures to improve the mechanical properties of corroded RC structures have garnered significant interest. This study investigates the structural performance of corroded polypropylene fiber RC slabs through both experimental and numerical approaches. Specimens were subjected to accelerated corrosion via electrochemical methods to simulate rebar corrosion. Key experimental parameters include polypropylene fiber volume fractions of 0% and 3%, design corrosion levels of 0%, 5%, and 10%, and both one-way and two-way constraints. The Digital Image Correlation (DIC) technique was employed for data acquisition. Load, deflection, strain, and crack morphology data were collected during loading. Results indicate that reinforcement corrosion increases the maximum crack width during loading, while a 3% polypropylene fiber volume fraction reduces this width by 20% to 48% at the same load level. Polypropylene fibers also enhance the peak loads and corresponding deflections of unidirectional and bidirectional specimens, with a more pronounced increase in unidirectional specimens of 11.3% and 15.4%, respectively. Although corrosion reduces the load-carrying capacity of the specimens, the inclusion of 3% polypropylene fibers significantly mitigates this reduction. A numerical model, incorporating rebar corrosion and polypropylene fibers through appropriate material constitutive models, was developed and validated against experimental results. The model accurately simulates the mechanical behavior of polypropylene fiber RC slabs with corroded reinforcement.

Perspectives on Adhesive–Bolted Hybrid Connection between Fe Shape Memory Alloys and Concrete Structures for Active Reinforcements

The prestressed active reinforcement of concrete structures using iron-based shape memory alloys (Fe-SMAs) is investigated in this experimental study through three connecting methods: adhesive–bolted hybrid connection, bolted connection, and adhesively bonded connection by activating at elevated temperatures (heating and cooling) and constraining deformation to generate prestress inside Fe-SMAs, through which compressive stress is generated in the parent concrete structures. In tests, the Fe-SMA is activated at 250 °C using a hot air gun, generating a prestress of 184.6–246 MPa. The experimental results show that local stress concentration in the concrete specimen and Fe-SMA plate around the hole is caused by the bolted connection. The adhesively bonded connection is prone to softening and slip of the structural adhesive during the activation process, thereby reducing the overall recovery force of Fe-SMAs. The adhesive–bolted hybrid connection effectively mitigates the local stress concentration problem of concrete and Fe-SMAs at anchor holes, while avoiding the prestress loss caused by the softening and slip of structural adhesive during elevated-temperature activation, achieving good reinforcement effect. This study on the connection methods of an Fe-SMA for reinforcing concrete structures provides both experimental support and practical guidance for its engineering application, offering new perspectives for future research.

Analysis of Reinforced Concrete Structure Subjected to Blast Load

The research focuses on a specific standoff distance of 15m & 20m , a critical parameter in blast analysis, to gain a understanding of the building's behavior under static loading conditions of blast load weight of 150kgs & 100kgs of TNT. To simulate realistic scenarios, the models are subjected to appropriate boundary conditions, including foundation constraints to account for the building's interaction with the ground. The blast load is assigned as joint loads, considering the standoff distance of the blast from the source. The standoff distance significantly influences the building's response to the blast, with profound implications for structural integrity and security. The Analysis of the Bare frame with blast load added as Joint Load, the deflection is controlled by adding Shear walls along various location along the periphery & core for 10 Story RCC Structures. The analysis is conducted using linear time history analysis (Response Spectrum Analysis) within ETABS, capturing the response of the building. Roof Displacement, Base Shear, Story Drift, Stiffness, Time period & Modal participation mass ratios are monitored during the analysis. The outcomes of this study contribute valuable insights into the structural responses of buildings to blast loads at specific standoff distances. This research advances our understanding of blast effects on buildings thereby enhancing the security and resilience of vital infrastructure in the face of evolving threats. Keywords: Blast Load, Response Spectrum Method, Roof Displacement, Base Shear, Story Drift, Stiffness, Time period & Modal participation mass ratios.

A practical multiscale analysis for nonlinear RC structures using a smart substructure replacement strategy

This paper presents a novel practical multiscale analysis (MSA) method for simulating local damages in large reinforced concrete (RC) structures during seismic events. Traditional techniques like the finite element (FE) method often struggle to balance computational costs with detailed simulation. The proposed method facilitates sequential local refinement from macroscopic to mesoscopic scales of RC structures, focusing strategically on critical areas prone to damage and cracking. Key features include: (1) 'Correction forces' enable integration and collaboration among models of different refinement levels, keeping models unchanged (i.e., without additions, deletions, or further refinement of elements), streamlining analysis and independent implementation across methods, scales, and platforms. (2) A smart replacement strategy ensures smooth transitions and prevents abrupt force changes between models at different refinement levels. (3) A ‘training process’ before performing the replacement and a semi-explicit method guarantee the accuracy and efficiency of the MSA method. (4) Parallel and distributed computing is seamlessly applied, significantly accelerating the analysis at each level. Implemented in the open-source software OpenSees, this method is illustrated through three examples that efficiently capture both the macroscopic mechanical responses and detailed local damage behaviors. This approach provides a valuable tool for the refined analysis of large-scale RC structures.

Innovative Fiber-Reinforced Polymer Rope-Based Closed-Form Retrofitting Methods Applied in Reinforced Concrete T-Shaped Beams under Torsion.

The fiber-reinforced polymer (FRP) strengthening of reinforced concrete (RC) elements with torsional deficiencies has not yet been extensively studied. Existing studies have primarily focused on rectangular RC beams. The few studies on L or T-shaped beams have used open-form retrofitting methods. However, premature debonding of the retrofitting from concrete surfaces often leads to detachment before achieving enhanced torsional capacity. This study introduces an innovative application of closed-form FRP retrofitting for RC T-beams against torsion. Two novel closed-form torsional upgrading methods were proposed and investigated through a comprehensive experimental program involving eight large-scale T-beams. One method employs FRP ropes embedded in transverse grooves near the surface, while the other combines U-shaped EB-FRP strips with FRP ropes. Additionally, two configurations were examined replicating scenarios where the upper part of the slab is accessible or inaccessible. The results demonstrate that the closed-form methods improve torsional strength by 9% to 25% and twist at failure by 92% to 536% compared to unstrengthened beams, with beams retrofitting through the slab exhibiting superior performance. Step-by-step technical guidelines of the proposed methods are presented to minimize construction defects and ensure effective implementation in real RC structures.

Reinforcement of the Emergency Slope with Reinforced Concrete Structures for Repeated Use

Reinforcement of the Emergency Slope with Reinforced Concrete Structures for Repeated Use

Durability fragility curves for service life prediction of concrete with various supplementary cementitious materials subjected to natural carbonation

This study estimates the service life of concretes containing supplementary cementitious materials (SCM) subjected to carbonation from a probabilistic and reliability-based perspective. The aim is to enhance understanding of the risks associated with degradation and to propose an alternative approach for estimating the durability of reinforced concrete (RC) structures. The inclusion of supplementary cementitious materials (SCMs) is crucial for making concrete more sustainable, as these materials can reduce the carbon footprint of concrete production and improve its durability. Using available natural carbonation data from the literature on samples containing SCM, computational codes were developed for reliability analyses via the Monte Carlo Simulation method. The results indicated that all concrete samples exhibited a 100% probability of failure for coverages of 20 mm and 25 mm at an age of 100 years. For a coverage of 30 mm, all samples maintained a failure probability of over 98% within the same period. Notably, concretes incorporating metakaolin showed a substantial reduction in failure probability across all evaluated time periods, indicating superior durability compared to other samples. These findings significantly contribute to a deeper understanding of concrete degradation processes and provide valuable insights for the design and maintenance of RC structures, particularly in the context of using SCM. By adopting a probabilistic approach, the study underscores the importance of considering variability and uncertainties in predicting concrete durability, ultimately aiding in the development of more resilient and long-lasting infrastructure.