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Mechanical Behavior of Concrete Materials and Structures: Experimental Evidence and Analytical Models (Volume II)

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A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Construction and Building Materials".

Deadline for manuscript submissions: closed (20 September 2024) | Viewed by 20767

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Dr. Dario De Domenico

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Department of Engineering, University of Messina, 98166 Messina, Italy
Interests: performance-based seismic design; seismic isolation; earthquake engineering; innovative structural control systems; limit-state behavior of reinforced concrete structures; strengthening techniques of reinforced concrete structures
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Dr. Luís Filipe Almeida Bernardo

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Department of Civil Engineering and Architecture, University of Beira Interior, Calçada Fonte do Lameiro, 6201-001 Covilhã, Portugal
Interests: structural analysis and design; numerical modelling and optimization; concrete structures; structural materials; building systems
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Special Issue Information

Dear Colleagues,

Concrete is one of the most widespread materials in the civil engineering field due to its versatility for both structural and non-structural applications depending on the density range, competitiveness in terms of durability and manufacturing costs, as well as ease in finding raw constituent elements. For this reason, the mechanical behavior of concrete and especially reinforced concrete has been a research theme tackled by many researchers through different approaches for years. Although the relevant literature is full of papers on this topic, ranging from experimental works to theoretical contributions, an accurate and comprehensive description of the actual mechanical behavior exhibited by concrete and reinforced concrete at service and ultimate conditions still remains a challenge in the field of structural engineering. This is due to the several intricate and interconnected phenomena involved, such as tensile cracking, compression crushing, strain softening, interaction between aggregates and matrix, interaction between concrete and reinforcement, stiffness degradation, energy dissipation and ductility exhibited under cycling loading, etc.

This Special Issue aims to collect contributions that deal with the mechanical behavior of ordinary, prestressed, and special concretes, including high-strength, lightweight, recycled, fiber-reinforced, and self-healing concretes, for both structural and non-structural applications. In particular, the desired topics include but are not limited to experimental findings, numerical approaches, and analytical models investigating the mechanical behavior of concrete, reinforced concrete, and prestressed concrete members at service and/or ultimate conditions under different loading states, such as axial loads, bending, shear, torsion, or combined loading states.

Dr. Dario De Domenico
Dr. Luís Filipe Almeida Bernardo
Guest Editors

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Keywords

concrete
reinforced concrete
prestressed concrete
mechanical strength
limit-state behavior
concrete structures
analytical models
experimental findings
service conditions
ultimate conditions

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Mechanical Behavior of Concrete Materials and Structures: Experimental Evidence and Analytical Models in Materials (16 articles)

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Research

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23 pages, 8469 KiB

Open AccessArticle

Sliding Shear Failure of Basement-Clamped Reinforced Concrete Shear Walls

by Harald Schuler

Materials 2024, 17(16), 4111; https://doi.org/10.3390/ma17164111 - 20 Aug 2024

Viewed by 569

Abstract

After an earthquake struck Chile in 1985, sliding shear failure was observed in the clamping zone of stabilising walls. Even houses with only four storeys can fail due to sliding shear below basement ceilings. This type of failure has received little attention in the past; however, it leads to premature failure of the clamping effect in the basement box. Bending deformations cannot fully develop, which significantly reduces the seismic safety; it is thus important to learn more about the sliding shear failure of basement-clamped shear walls. The overall behaviour is analysed based on three large-scale shear wall tests. The deformation states around the sliding shear zone are evaluated and a simple estimate is given of when sliding shear failure can occur. Full article

(This article belongs to the Special Issue Mechanical Behavior of Concrete Materials and Structures: Experimental Evidence and Analytical Models (Volume II))

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22 pages, 3979 KiB

Open AccessArticle

Machine Learning-Based Prediction Models for Punching Shear Strength of Fiber-Reinforced Polymer Reinforced Concrete Slabs Using a Gradient-Boosted Regression Tree

by Emad A. Abood, Marwa Hameed Abdallah, Mahmood Alsaadi, Hamza Imran, Luís Filipe Almeida Bernardo, Dario De Domenico and Sadiq N. Henedy

Materials 2024, 17(16), 3964; https://doi.org/10.3390/ma17163964 - 9 Aug 2024

Viewed by 1124

Abstract

Fiber-reinforced polymers (FRPs) are increasingly being used as a composite material in concrete slabs due to their high strength-to-weight ratio and resistance to corrosion. However, FRP-reinforced concrete slabs, similar to traditional systems, are susceptible to punching shear failure, a critical design concern. Existing empirical models and design provisions for predicting the punching shear strength of FRP-reinforced concrete slabs often exhibit significant bias and dispersion. These errors highlight the need for more reliable predictive models. This study aims to develop gradient-boosted regression tree (GBRT) models to accurately predict the shear strength of FRP-reinforced concrete panels and to address the limitations of existing empirical models. A comprehensive database of 238 sets of experimental results for FRP-reinforced concrete slabs has been compiled from the literature. Different machine learning algorithms were considered, and the performance of GBRT models was evaluated against these algorithms. The dataset was divided into training and testing sets to verify the accuracy of the model. The results indicated that the GBRT model achieved the highest prediction accuracy, with root mean square error (RMSE) of 64.85, mean absolute error (MAE) of 42.89, and coefficient of determination (R²) of 0.955. Comparative analysis with existing experimental models showed that the GBRT model outperformed these traditional approaches. The SHapley Additive exPlanation (SHAP) method was used to interpret the GBRT model, providing insight into the contribution of each input variable to the prediction of punching shear strength. The analysis emphasized the importance of variables such as slab thickness, FRP reinforcement ratio, and critical section perimeter. This study demonstrates the effectiveness of the GBRT model in predicting the punching shear strength of FRP-reinforced concrete slabs with high accuracy. SHAP analysis elucidates key factors that influence model predictions and provides valuable insights for future research and design improvements. Full article

(This article belongs to the Special Issue Mechanical Behavior of Concrete Materials and Structures: Experimental Evidence and Analytical Models (Volume II))

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22 pages, 7467 KiB

Open AccessArticle

Comparative Analysis of Reinforced Concrete Beam Behaviour: Conventional Model vs. Artificial Neural Network Predictions

by Muhammad Mahtab Ahmad, Ayub Elahi and Salim Barbhuiya

Materials 2023, 16(24), 7642; https://doi.org/10.3390/ma16247642 - 14 Dec 2023

Cited by 1 | Viewed by 1160

Abstract

This research aims to conduct a comparative analysis of the first crack load, flexural strength, and shear strength in reinforced concrete beams without stirrups. The comparison is made between the conventional model developed according to the current design code (ACI building code) and an unconventional approach using Artificial Neural Networks (ANNs). To accomplish this, a dataset comprising 110 samples of reinforced concrete beams without stirrup reinforcement was collected and utilised to train a Multilayer Backpropagation Neural Network in MATLAB. The primary objective of this work is to establish a knowledge-based structural analysis model capable of accurately predicting the responses of reinforced concrete structures. The coefficient of determination obtained from this comparison yields values of 0.9404 for the first cracking load, 0.9756 for flexural strength, and 0.9787 for shear strength. Through an assessment of the coefficient of determination and linear regression coefficients, it becomes evident that the ANN model produces results that closely align with those obtained from the conventional model. This demonstrates the ANN’s potential for precise prediction of the structural behaviour of reinforced concrete beams. Full article

(This article belongs to the Special Issue Mechanical Behavior of Concrete Materials and Structures: Experimental Evidence and Analytical Models (Volume II))

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26 pages, 6192 KiB

Open AccessArticle

Long-Term Shrinkage Measurements on Large-Scale Specimens Exposed to Real Environmental Conditions

by Wolfgang Bachofner, Dominik Suza, Harald S. Müller and Johann Kollegger

Materials 2023, 16(23), 7305; https://doi.org/10.3390/ma16237305 - 24 Nov 2023

Cited by 1 | Viewed by 933

Abstract

This article presents an experimental testing campaign on large-scale concrete specimens with cross-sectional areas of up to 1 m² and a specimen length of 3 m. The primary goal of the testing campaign was to study the shrinkage behaviour of large-scale specimens exposed to real environmental conditions. Large-scale prismatic concrete specimens were equipped with vibrating wire strain gauges to monitor the strain evolution inside the specimens. To analyse the shrinkage behaviour of the specimens, the thermal strain had to be deducted from the measured strain. To study the influence of seasonal environmental conditions, different specimen production dates (in summer and winter) were examined. The measured shrinkage strains of the large-scale specimens are compared with the results of shrinkage models developed by two engineering entities (fib (Fédération Internationale du Béton) and RILEM (International Union of Laboratories and Experts in Construction, Materials, Systems and Structures)). The comparison shows a poor agreement of the measurements with the models, even though the results from the model for small specimens tested in the laboratory under constant environmental condition agree well with the experimental results. This leads to the conclusion that the poor agreement between the measurements and the shrinkage models must be due to the seasonally changing environmental conditions. The comparison of the results from specimens with different production dates shows that different shrinkage behaviour occurs, especially in the first year of measurements. Full article

(This article belongs to the Special Issue Mechanical Behavior of Concrete Materials and Structures: Experimental Evidence and Analytical Models (Volume II))

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20 pages, 10567 KiB

Open AccessArticle

Mesoscopic Analysis of Rounded and Hybrid Aggregates in Recycled Rubber Concrete

by Mahmoud M. A. Kamel, Yu Fu, Xiaowei Feng and Yijiang Peng

Materials 2023, 16(19), 6600; https://doi.org/10.3390/ma16196600 - 8 Oct 2023

Viewed by 1250

Abstract

Recycled rubber concrete (RRC), a sustainable building material, provides a solution to the environmental issues posed by rubber waste. This research introduces a sophisticated hybrid random aggregate model for RRC. The model is established by combining convex polygon aggregates and rounded rubber co-casting schemes with supplemental tools developed in MATLAB and Fortran for processing. Numerical analyses, based on the base force element method (BFEM) of the complementary energy principle, are performed on RRC’s uniaxial tensile and compressive behaviors using the proposed aggregate models. This study identified the interfacial transition zone (ITZ) around the rubber as RRC’s weakest area. Here, cracks originate and progress to the aggregate, leading to widespread cracking. Primary cracks form perpendicular to the load under tension, whereas bifurcated cracks result from compression, echoing conventional concrete’s failure mechanisms. Additionally, the hybrid aggregate model outperformed the rounded aggregate model, exhibiting closer peak strengths and more accurate aggregate shapes. The method’s validity is supported by experimental findings, resulting In detailed stress–strain curves and damage contour diagrams. Full article

(This article belongs to the Special Issue Mechanical Behavior of Concrete Materials and Structures: Experimental Evidence and Analytical Models (Volume II))

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20 pages, 3621 KiB

Open AccessEditor’s ChoiceArticle

Prediction of Ecofriendly Concrete Compressive Strength Using Gradient Boosting Regression Tree Combined with GridSearchCV Hyperparameter-Optimization Techniques

by Zaineb M. Alhakeem, Yasir Mohammed Jebur, Sadiq N. Henedy, Hamza Imran, Luís F. A. Bernardo and Hussein M. Hussein

Materials 2022, 15(21), 7432; https://doi.org/10.3390/ma15217432 - 23 Oct 2022

Cited by 64 | Viewed by 4171

Abstract

A crucial factor in the efficient design of concrete sustainable buildings is the compressive strength (Cs) of eco-friendly concrete. In this work, a hybrid model of Gradient Boosting Regression Tree (GBRT) with grid search cross-validation (GridSearchCV) optimization technique was used to predict the compressive strength, which allowed us to increase the precision of the prediction models. In addition, to build the proposed models, 164 experiments on eco-friendly concrete compressive strength were gathered for previous researches. The dataset included the water/binder ratio (W/B), curing time (age), the recycled aggregate percentage from the total aggregate in the mixture (RA%), ground granulated blast-furnace slag (GGBFS) material percentage from the total binder used in the mixture (GGBFS%), and superplasticizer (kg). The root mean square error (RMSE) and coefficient of determination (R²) between the observed and forecast strengths were used to evaluate the accuracy of the predictive models. The obtained results indicated that—when compared to the default GBRT model—the GridSearchCV approach can capture more hyperparameters for the GBRT prediction model. Furthermore, the robustness and generalization of the GSC-GBRT model produced notable results, with RMSE and R² values (for the testing phase) of 2.3214 and 0.9612, respectively. The outcomes proved that the suggested GSC-GBRT model is advantageous. Additionally, the significance and contribution of the input factors that affect the compressive strength were explained using the Shapley additive explanation (SHAP) approach. Full article

(This article belongs to the Special Issue Mechanical Behavior of Concrete Materials and Structures: Experimental Evidence and Analytical Models (Volume II))

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16 pages, 7427 KiB

Open AccessArticle

Study on Axial Tensile Strain Rate Effect on Concrete Based on Experimental Investigation and Numerical Simulation

by Bi Sun, Rui Chen, Yang Ping, Zhende Zhu and Nan Wu

Materials 2022, 15(15), 5164; https://doi.org/10.3390/ma15155164 - 25 Jul 2022

Cited by 2 | Viewed by 1903

Abstract

The material of concrete is a three-phase composite material composed of an aggregate, a mortar and an interface transition zone (ITZ). Based on this characteristic, the axial tensile test of mortar, the interface and concrete specimens under intermediate strain rate was carried out in this paper. The sensitivity of these three materials to strain rate was compared and analyzed. The numerical simulation of the axial tension of the concrete materials was studied. The following conclusions are drawn: in the axial tension test, the rate of sensitivity of the specimen interface is the strongest. With the increase in strain rate, the tensile strength and elastic modulus of concrete specimens increase but the effect of the ITZ decreases. The low tensile strength of the ITZ leads to its failure in concrete. The parallel bond strain energy and the dissipated energy of specimens increase with the strain rate. When the strain rate is higher (greater than 1 × 10⁻²), the increase rate of the dissipated energy is greater than that of the parallel bond strain energy. The results of this research can provide the corresponding basis for the safety evaluation and the stability analysis of concrete engineering in the range of intermediate strain rate. Full article

(This article belongs to the Special Issue Mechanical Behavior of Concrete Materials and Structures: Experimental Evidence and Analytical Models (Volume II))

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17 pages, 8565 KiB

Open AccessArticle

Grey Correlation Analysis of the Durability of Steel Fiber-Reinforced Concrete under Environmental Action

by Yongcheng Ji, Wenwen Xu, Yichen Sun, Yulong Ma, Qiulin He and Zhiqiang Xing

Materials 2022, 15(14), 4748; https://doi.org/10.3390/ma15144748 - 6 Jul 2022

Cited by 11 | Viewed by 1645

Abstract

The interface performance of steel fiber-reinforced concrete (SFRC) is a critical factor in determining mechanical properties and durability. The degradation of the concrete matrix and micro-structure interface is caused by environmental erosion, which shortens the service life of the structure design. Considering different volume contents of steel fiber (0%, 1%, 2%), the failure mechanism of SFRC under different environmental erosion conditions was studied through a laboratory test scheme. A total of six environmental factors are selected, including water, sodium chloride solution, sodium sulfate solution, dilute sulfuric acid solution, sodium hydroxide solution, and a freeze-thaw cycle. When subjected to different erosion concentrations and periods, micro-structure and axial bearing capacity deterioration laws are compared and analyzed. A durability equation related to fiber mixture ratio and strength is presented based on the experimental data and the numerical simulation method. The influence of different environments on steel fiber-reinforced concrete is analyzed, and the grey correlation degree of axial compressive strength is analyzed. The experimental results show that steel fiber can effectively improve the concrete axial bearing capacity, but different responses are observed under the various erosion conditions. A freeze-thaw cycle environment has the most significant impact on the axial compressive strength of concrete, followed by the sulfuric acid environment, and other environments have a weaker impact. The research results will provide a theoretical basis for predicting the performance deterioration of SFRC concerning other erosion conditions and periods. Full article

(This article belongs to the Special Issue Mechanical Behavior of Concrete Materials and Structures: Experimental Evidence and Analytical Models (Volume II))

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18 pages, 6073 KiB

Open AccessArticle

Compressive Strength of Steel Fiber-Reinforced Concrete Employing Supervised Machine Learning Techniques

by Yongjian Li, Qizhi Zhang, Paweł Kamiński, Ahmed Farouk Deifalla, Muhammad Sufian, Artur Dyczko, Nabil Ben Kahla and Miniar Atig

Materials 2022, 15(12), 4209; https://doi.org/10.3390/ma15124209 - 14 Jun 2022

Cited by 48 | Viewed by 3532

Abstract

Recently, research has centered on developing new approaches, such as supervised machine learning techniques, that can compute the mechanical characteristics of materials without investing much effort, time, or money in experimentation. To predict the 28-day compressive strength of steel fiber–reinforced concrete (SFRC), machine learning techniques, i.e., individual and ensemble models, were considered. For this study, two ensemble approaches (SVR AdaBoost and SVR bagging) and one individual technique (support vector regression (SVR)) were used. Coefficient of determination (R²), statistical assessment, and k-fold cross validation were carried out to scrutinize the efficiency of each approach used. In addition, a sensitivity technique was used to assess the influence of parameters on the prediction results. It was discovered that all of the approaches used performed better in terms of forecasting the outcomes. The SVR AdaBoost method was the most precise, with R² = 0.96, as opposed to SVR bagging and support vector regression, which had R² values of 0.87 and 0.81, respectively. Furthermore, based on the lowered error values (MAE = 4.4 MPa, RMSE = 8 MPa), statistical and k-fold cross validation tests verified the optimum performance of SVR AdaBoost. The forecast performance of the SVR bagging models, on the other hand, was equally satisfactory. In order to predict the mechanical characteristics of other construction materials, these ensemble machine learning approaches can be applied. Full article

(This article belongs to the Special Issue Mechanical Behavior of Concrete Materials and Structures: Experimental Evidence and Analytical Models (Volume II))

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Review

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21 pages, 12310 KiB

Open AccessReview

Impact of Corrosion on the Bond Strength between Concrete and Rebar: A Systematic Review

by Amadou Sakhir Syll and Toshiyuki Kanakubo

Materials 2022, 15(19), 7016; https://doi.org/10.3390/ma15197016 - 10 Oct 2022

Cited by 19 | Viewed by 3126

Abstract

Corrosion of the reinforcement affects more than the cross-sectional area of the rebar. The volume of steel also increases due to expansive corrosion products, leading to the cracking, delamination, and spalling of concrete. As a result, the bond capacity between concrete and rebar is affected. Researchers have extensively examined the impact of corrosion on the bond strength between concrete and rebar to propose empirical, theoretical, or numerical predictive models. Therefore, research programs on this topic have increased rapidly in recent years. This article presents a systematic literature review to explore experimental methods, outcomes, and trends on this topic. The Web of Science search collected 84 relevant research articles through a rigorous selection. Key factors that affect bond strength degradation, including concrete cover, concrete strength, and stirrups, have been documented. However, a general model is still unavailable due to discrepancies caused by differences in testing methods to evaluate the effect of corrosion on bond strength. Furthermore, researchers attempted to clarify the degradation mechanism of bond strength affected by corrosion. As a result, new alternatives have been proposed to build a practical model to assess the bond strength deterioration of corroded structures. Full article

(This article belongs to the Special Issue Mechanical Behavior of Concrete Materials and Structures: Experimental Evidence and Analytical Models (Volume II))

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