Search

This article was based on indoor simulated marine environment erosion tests using concrete, by analyzing the compressive strength, coating bonding strength and chloride ion diffusion behavior of marine cast-in-place concrete after erosion, and the effects of anti-corrosion coating / treating ages on the mechanical properties and durability evolution of marine cast-in-place concrete were studied. The results show that compared with concrete without anti-corrosion coating / treating, anti-corrosion coating / treating concrete using silane and polyurethane coating shows a decrease in compressive strength fc after erosion in marine tidal areas, and concrete fc gradually decreases with the increase of curing age tc . Anti-corrosion coating / treating can effectively improve the long-term chloride ion erosion resistance of marine cast-in-place concrete, and with the increase of curing age tc, the effects of anti-corrosion coating / treating on reducing the surface chloride ion content Cs and apparent chloride ion diffusion coefficient Da of concrete becomes more significant. Taking into account the development of mechanical properties of cast-in-place concrete in the marine tidal zone environment and the effectiveness of anti-corrosion coating / treating measures, it is recommended to use silane and polyurethane coatings for coating with a curing age of not less than 5 and 10 d, respectively. [ABSTRACT FROM AUTHOR]

The hybrid fiber-reinforced polymer (FRP)–concrete–steel double-skin tubular column (DSTC) is an innovative composite member consisting of an outer FRP tube and an inner steel tube, with the space between them filled with concrete. The incorporation of ultrahigh-performance concrete (UHPC) in the DSTC yields numerous advantages over the DSTC with normal-strength concrete, particularly in terms of enhanced impact resistance. While the lateral impact behavior of UHPC DSTC has been investigated, its residual performance after lateral impact remains unexplored. To comprehend the postimpact behavior of UHPC DSTC, a series of specimens underwent lateral impact, followed by static axial compression. Additionally, intact specimens were subjected to static axial compression for comparative analysis. Subsequently, refined numerical models were developed and validated using the obtained test data. Detailed parametric studies were carried out to investigate the influences of key variables on the postimpact behavior of UHPC DSTCs. Finally, based on the ratio of residual midheight deflection to the column height, a prediction formula was proposed for the rapid damage evaluation of impacted UHPC DSTCs, and threshold values for different damage levels were suggested based on a defined damage index. [ABSTRACT FROM AUTHOR]

The use of externally bonded composite systems is recognized as an effective solution for strengthening existing reinforced concrete (RC) structures. Steel-reinforced grout (SRG) is an attractive option, because of its compatibility with the concrete substrate and mechanical properties. However, a critical aspect is the delamination that might affect the steel textile–mortar and the mortar–concrete substrate interfaces. An experimental and theoretical investigation of the SRG–concrete bond is reported in this paper. In particular, the bond performances of SRG-to-concrete joints, which varies the width of the SRG fabric, the displacement rate, and the applied load eccentricity, are analyzed for the stress that is associated with the bond capacity, slip, and failure modes based on the results that are obtained by direct single-lap shear tests. To assess a data set for model calibration, the findings of this paper and those in the technical literature are collected. Therefore, a machine learning (ML) approach that is based on an artificial neural networks (ANN) algorithm is implemented, and a new analytical formulation for the prediction of the SRG-to-concrete bond capacity is proposed. [ABSTRACT FROM AUTHOR]

The chloride migration coefficient (CMC) of concrete is crucial for evaluating its durability. This study develops ensemble models to predict the CMC of concrete, addressing the limitations of traditional, labor-intensive laboratory tests. We developed three ensemble models: an inverse variance-based model, an Artificial Neural Network (ANN)-based model, and a tree-based model using the random forest regression algorithm. These models were trained on a dataset comprising 843 concrete mix proportions from existing literature. Results indicate that ensemble models outperform single models such as ANN and Support Vector Regression (SVR) in predicting CMC, with the combined random forest and ANN model showing the highest accuracy. Sensitivity analysis using Shapley Additive Explanations (SHAP) reveals that the CMC is most influenced by the water-to-cement ratio and curing age. Additionally, we designed a graphical user interface (GUI) to facilitate the practical application of our models. This research offers a robust methodology for evaluating concrete durability and potential for extending the prediction to other concrete properties. [ABSTRACT FROM AUTHOR]

AbstractThe current study performs an in-depth analysis into the reinforcement of circular concrete columns (CCC) using aramid fiber-reinforced polymers (AFRP), focusing on two distinct techniques: wet-layup wrapping and tube-encasement with manufactured shells. The need for improved forecasting models for AFRP-wrapped concrete structures arises from observed inconsistencies and limited accuracy in existing models, which often fail to fully account for key parameters such as confining stiffness and composite strain. Addressing this gap, our study begins with a comprehensive review of prior experimental research, emphasizing the impact of various parameters on the performance of AFRP-wrapped CCC under monotonic axial compressive loading. To build a robust foundation for this investigation, we compiled an extensive database of experimental outcomes from existing literature, encompassing 180 concrete cylinders wrapped by AFRP. Our analysis meticulously evaluates nine different models designed to predict the ultimate compressive strength and axial strain of circular columns reinforced with AFRP. These models are scrutinized for their accuracy, revealing notable discrepancies, particularly when key parameters are overlooked. In response, we introduce new equations that offer more precise predictions compared to previously proposed models. These novel equations are derived from an extensive statistical analysis, ensuring their reliability and robustness. Prominently, our proposed model for compressive strength demonstrated superior performance with a coefficient of determination (R2 = 0.87), a root means square error (RMSE) of 0.29, and a mean absolute error (MAE) of 22.91. Similarly, the proposed strain model showed a high level of forecasting accuracy, with a coefficient of determination (R2 = 0.82), a root mean square error (RMSE) of 2.79, and a mean absolute error (MAE) of 208.69. To validate our findings, we employed the Taylor diagram and probability density function, which provide a detailed comparison of the forecasting accuracy of our proposed models against existing ones. The outcomes of this study not only enhance the understanding of AFRP-wrapped concrete columns but also offer valuable insights for the development of more effective reinforcement techniques. Our findings are expected to contribute significantly to the advancement of structural engineering practices, particularly in the application of AFRP for strengthening concrete structures. [ABSTRACT FROM AUTHOR]

The study explored an innovative scheme to improve the load‐bearing capacity of bamboo by infilling it with concrete to form a composite column as a compression member for low‐cost buildings. A total of four samples of bamboo of five different lengths (0.2, 0.4, 0.6, 0.8, and 1 m) were prepared with concrete (concrete infilled) and without concrete for three different commonly used concrete grades (C12/15, C15/20, and C20/25) and the load‐carrying capacity tested to determine its resistance under compression. Generally, there was a considerable improvement in structural performance in terms of load‐carrying capacity when the bamboo culms were infilled with plain concrete. The failure load capacity of plain bamboo (no concrete infill) was reduced by 26.4% with an increase in the length of the bamboo from 0.2 to 1 m. Concrete‐infilled bamboo with C15, C20, and C25 led to an increment of 58.7%, 103%, and 139.1% of the failure load capacity relative to that of the plane bamboo culms sample test length at 1 m, respectively. Bamboo infilled with concrete grade C25 had a larger failure load as compared to those infilled with concrete grade C20 and C15. The study concludes that concrete in‐filled bamboo increases the buckling resistance as well as the load‐carrying capacity and it can be an alternative composite building material to steel and concrete for building under light loads. [ABSTRACT FROM AUTHOR]

Concrete-filled fiber tubes (CFFT) are gaining prominence as a feasible alternative to traditional materials for a variety of structural applications. However, research on structural performance of CFFT beams is still scarce. This paper presents a finite-element (FE) analysis of CFFT beams validated by experimental results from literature. Then, a parametric study investigating structural performance of post-tensioned (PT CFFT) beams was conducted using 34 FE models using ANSYS nonlinear FE software program. The parametric study results showed that both normal-strength concrete (NSC) and high-strength concrete (HSC) filled PT CFFT exhibit identical nonlinear responses. Increasing the prestressed and non-prestressed reinforcement ratio significantly improved the overall performance of PT CFFT beams. Placing the PT tendons at the bottom of PT CFFT beams enhanced the cracking, yielding, and ultimate load-carrying capacities by 7.98%, 12.32%, and 9.03% for NSC-filled PT CFFT beams, respectively. Doubling the axial stiffness of the tube laminate structure increased the ultimate load, energy absorption capacity (EAC), pre-yielding stiffness (Kpre), and post-yielding stiffness (Kpos) by 18.5%, 12.15%, 9.21%, and 8.2%, respectively for NSC-filled PT CFFT beams. Beams with straight PT tendons exhibited increased cracking, yielding, and ultimate load capacity by 10.85%, 14.60%, and 13.58% more than those with curved-profile tendons. The ductility of PT CFFT beams is more sensitive to the amount of prestressed reinforcement ratio and concrete strength has a minimal effect on the structural performance of PT CFFT beams. [ABSTRACT FROM AUTHOR]

Concrete compressive strength (CS) is a crucial performance parameter in concrete structure design. Reliable strength prediction reduces costs and time in design and prevents material waste from extensive mixture trials. Machine learning techniques solve structural engineering challenges such as CS prediction. This study used Machine Learning (ML) models to enhance the prediction of CS, analyzing 1030 experimental CS data ranging from 2.33 to 82.60 MPa from previous research databases. The ML models included both non-ensemble and ensemble types. The non-ensemble models were regression-based, evolutionary, neural network, and fuzzy-inference-system. Meanwhile, the ensemble models consisted of adaptive boosting, random forest, and gradient boosting. There were eight input parameters: cement, blast-furnace-slag, aggregates (coarse and fine), fly ash, water, superplasticizer, and curing days, with the CS as the output. Comprehensive performance evaluations include visual and quantitative methods and k-fold cross-validation to assess the study's reliability and accuracy. A sensitivity analysis using Shapley-Additive-exPlanations (SHAP) was conducted to understand better how each input variable affects CS. The findings showed that the Categorical-Gradient-Boosting (CatBoost) model was the most accurate prediction during the testing stage. It had the highest determination-coefficient (R2) of 0.966 and the lowest Root-Mean-Square-Error (RMSE) of 3.06 MPa. The SHAP analysis showed that the age of the concrete was the most critical factor in the predictive accuracy. Finally, a Graphical User Interface (GUI) was offered for designers to predict concrete CS quickly and economically instead of costly computational or experimental tests. [ABSTRACT FROM AUTHOR]

Concrete structures are commonly used as secure settlements and strategic shelters due to their inherent strength, durability, and wide availability. Examining the robustness and integrity of strategic concrete structures in the face of superenergy projectiles is of utmost significance in safeguarding vital infrastructure sectors, ensuring the well-being of individuals, and advancing the course of worldwide sustainable progress. This research focuses on forecasting the penetration depth (BPD) through the application of robust models, such as Multilayer Perceptron (MLP), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), and K-Nearest Neighbors (KNN) as ML models. The dataset used consists of 1,020 data points sourced from the National Institute of Standards and Technology (NIST), encompassing various parameters such as cement content (Cp), ground granulated blast-furnace slag (GGBFS), fly ash content (FA), water portion (Wp), superplasticizer content (Sp), coarse aggregate content (CA), fine aggregate content (FAA), concrete sample age (t), concrete compressive strength (CCS), gun type (G-type), bullet caliber (BCali), bullet weight (Wb), and bullet velocity (Vb). Feature selection techniques revealed that the MLP model, incorporating eight input variables (FA, CA, Sp, GGBFS, Cp, t, FAA, and CCS), provides the most accurate predictions for BPD across the entire dataset. Comparing the four models used in this study, KNN demonstrates distinct superiority over the other methods. KNN, a non-parametric ML model used for classification and regression, possesses several advantages, including simplicity, non-parametric nature, no training requirements, robustness to noisy data, suitability for large datasets, and interpretability. The results reveal that KNN outperforms the other models presented in this paper, exhibiting an R² value of 0.9905 and an RMSE value of 0.1811 cm, signifying higher accuracy in its predictions compared to the other models. Finally, based on the error analysis across iterations, it is evident that the final accuracy error of the KNN model surpasses that of the SVM, MLP, and LightGBM models, respectively. [ABSTRACT FROM AUTHOR]

The issue of early-age concrete cracking is challenging and relies on the state of concrete soon after it is placed in the formwork. The concrete state is a function of the strains associated with thermal and other dilatations and the level of in situ strength. Both strain and strength primarily require information on the temperature–time history of the concrete element. For larger elements, the thermal history varies significantly across the thickness and the concrete material itself acts as confinement for discrete elements. Due to complexity of this issue, designers currently rely on mock tests and/or finite-element (FE) modelling for structures that are deemed 'important'. Both approaches are costly and time consuming. It is therefore important to have a robust yet simple model to estimate the temperature variation experienced by concrete elements. The proposed spreadsheet-based model presented in this paper aims to provide a rapid estimate of the temperature profiles within a hydrating concrete element. The model uses the concept of effective thickness and the revised heat compensation technique. It was validated based on the measured temperature development of a concrete block of rectangular section. The proposed model was also successfully compared with output from FE software (TNO Diana). [ABSTRACT FROM AUTHOR]

Ferrochrome slag (FCS), a type of industrial solid waste, has promising properties that make it a viable alternative aggregate for concrete production. This can be a partial solution to reduce the pressure on natural aggregates. However, the usage of this solid waste in concrete or composite structures is not documented well. As a result, the purpose of this study is to evaluate experimentally the effect of employing FCS concrete in the concrete-filled steel tube (CFST) on strength and its fire-resistant capabilities. The influence of heating temperatures ranging from 200 to 800 °C and the level of FCS fine aggregate substitution on load–displacement behaviour, ultimate load, and failure mechanism was examined. The CFST stub columns were filled with three different concrete mixtures: one with 100% natural fine aggregate and the other two with 50% and 100% FCS fine aggregate. The findings demonstrate that the ultimate load and stiffness of the CFST stub columns increased as the amount of FCS fine aggregate in the concrete increased. Similar results were found for residual strength and stiffness after high-temperature exposure. The fine natural aggregate can be fully replaced by the FCS fine aggregate without compromising any strength properties of concrete, as well as CFST columns at room and high temperatures. The Eurocode 4 and ACI codes show a conservative estimation of ultimate strengths for the CFST stub columns at room and elevated temperatures compared to the experimental results for all three concrete mixes. However, Eurocode 4 provides a closer estimation for the ultimate load for all concrete mixes than the ACI recommendation. [ABSTRACT FROM AUTHOR]

Carbonation‐induced corrosion of steel in concrete can allow for premature degradation of structures. Corrosion probes in health monitoring systems can assess concrete carbonation and steel corrosion rates. The electrochemical noise (EN) technique has advantages for corrosion sensing. Instrumented concrete columns were fitted with a carbonation chamber for accelerated testing. EN was assessed through statistical evaluation of noise time signatures, noise resistance, and spectral analysis. The mean noise potential for the electrodes showed electronegative potential and correspondingly high rms noise current, indicative of corrosion activation in carbonated concrete. The estimated corrosion rates obtained from the noise impedance were comparable to those resolved from the polarization resistance and noise resistance. The shot noise analysis indicated isolated spontaneous noise events associated with the activation of local steel anodes. The outcomes of the testing indicate that the placement of low‐cost sensors and passive EN measurements can be used to monitor the onset of carbonation‐induced corrosion of steel in concrete and provide estimates on corrosion rates. [ABSTRACT FROM AUTHOR]

To explore the influences of different coarse aggregate replacement ratios on the high-temperature performance of recycled concrete with manufactured sand (RCM), RCM specimens were prepared with varying coarse aggregate replacement ratios (0%, 20%, 40%, 60%, 80%, 100%, mass fraction). The surface morphology, mass loss rate, compressive strength, load-deformation curve, and microscopic morphology of RCM were investigated after exposure to high temperatures. Results indicate that the RCM performs well in high-temperature tests below 600 °C when the coarse aggregate replacement ratio is between 40% and 60%. At 200 °C, the specimens in each group show minimal morphological changes, small mass loss rate, and a slight increase in compressive strength. At 400 °C, cracks begin to appear on the surface of the specimens, with mass loss rate ranging from 4. 35% to 6. 47%, and compressive strength starts to decline. At this point, the coarse aggregate replacement ratio has a minor impact. At 600 °C, significant changes in surface morphology are observed, with mass loss rate increasing to 6. 42% ~ 8.70%, and compressive strength eventually decreasing to around 80% of the baseline strength. Specimens with coarse aggregate replacement ratio of 40% ~ 60% perform well under these conditions. At 800 °C, the surface morphology of specimens in all groups intensifies further. The impact becomes particularly pronounced for coarse aggregate replacement ratio exceeding 60%. Simultaneously, various performance parameters indicate that the concrete has experienced a complete loss of effectiveness at this temperature. [ABSTRACT FROM AUTHOR]

In the internal support system of foundation pit engineering, concrete support has been widely used in foundation pit engineering due to its advantages of high bearing capacity, unrestricted layout, cast-in-place end nodes and good integrity. However, the disadvantages of concrete support in design and construction are also very obvious, with problems such as large design section, long construction time, heavy pollution, high cost and serious waste. Therefore, in view of the advantages and disadvantages of concrete support in foundation pit engineering and combined with the characteristics of deep foundation pit engineering of metro open cut station, the prefabricated composite concrete filled steel tube internal support is designed in this paper. From the perspective of improving the stiffness and strength of end joints and achieving fixed connection at the end, a fixed end composed of long bolts and mold bag concrete is proposed, which is suitable for the prefabricated composite steel tube concrete internal support of metro foundation pit. The design scheme, structural characteristics and working principle of the fixed end are explained and compared with the cast-in-place concrete support node, which shows that the fixed end plays the role of fixed support in the support system, which is composed of special components such as embedded steel plate, hollow flange, bag concrete and long bolt. In order to establish the theoretical basis for the design and check of the fixed end, the analytical method of the compression and bending load-bearing capacity of the fixed end is obtained by analyzing the mechanical mechanism, failure mode and evolution law of the fixed end. In order to verify the feasibility of the theoretical analysis method of the bearing capacity of the fixed end, the theoretical and numerical results are verified and analyzed by numerical simulation, and the main factors affecting the bearing capacity of the fixed end are parameterized. [ABSTRACT FROM AUTHOR]

This paper comprehensively investigates the dynamic mechanical properties of concrete by employing a 75 mm diameter Split Hopkinson Pressure Bar (SHPB). To be detailed further, dynamic compression experiments are conducted on coral aggregate seawater concrete (CASC) to unveil the relationship between the toughness ratio, strain rate, and different strength grades. A three-dimensional random convex polyhedral aggregate mesoscopic model is also utilized to simulate the damage modes of concrete and its components under varying strain rates. Additionally, the impact of different aggregate volume rates on the damage modes of CASC is also studied. The results show that strain rate has a significant effect on CASC, and the strength grade influences both the damage mode and toughness index of the concrete. The growth rate of the toughness index exhibits a distinct change when the 28-day compressive strength of CASC ranges between 60 and 80 MPa, with three times an increment in the toughness index of high-strength CASC comparing to low-strength CASC undergoing high strain. The introduction of pre-peak and post-peak toughness highlights the lowest pre-to-post-peak toughness ratio at a strain rate of approximately 80 s−1, which indicates a shift in the concrete's damage mode. Various damage modes of CASC are under dynamic impact and are consequently defined based on these findings. The LS-DYNA finite element software is employed to analyze the damage morphology of CASC at different strain rates, and the numerical simulation results align with the experimental observations. By comparing the numerical simulation results of different models with varying aggregate volume rates, it is reported that CASC's failure mode is minimized at an aggregate volume rate of 20%. [ABSTRACT FROM AUTHOR]

Reinforced concrete (RC), renowned for its amalgamation of strength and durability, stands as a cornerstone in modern engineering, extensively employed in various structures such as buildings, bridges, and pipe culverts. However, prevalent issues of concrete spalling and exposed steel bars within RC structures pose significant challenges. An automated identification methodology is proposed to detect concrete spalling and exposed steel bars, leveraging machine vision technology and deep learning algorithms. Initially, a classifier is utilized to discern concrete spalling areas within the image domain at the image level. Subsequently, a semantic segmentation algorithm is applied to precisely delineate the contours of both concrete spalling areas and exposed steel bars at the pixel level. The efficacy and feasibility of the proposed method are validated through training and testing on both a publicly available dataset and actual RC structure images. The results illustrate that the average detection precision, Intersection over Union (IOU), recall, and F1-score for concrete spalling areas are 0.924, 0.872, 0.937, and 0.925, respectively, while for exposed steel areas, the corresponding values are 0.905, 0.820, 0.899, and 0.855. This method demonstrates promising prospects for wide-ranging applications in defect detection within RC structures. [ABSTRACT FROM AUTHOR]

Ensuring precise estimation of creep characteristics is critical for the efficient design of structures prone to creep deformation. Creep is an indicator of permanent and gradual deformation that transpires within a material when it is continuously subjected to a burden for an extended duration. In addition to the temperature and duration of load application, the degree of creep in concrete is also affected by the material's inherent properties and the length of time it has been exposed to the load. The enduring consequences of creep might result in significant distortions that give rise to structural deficiencies inside the edifice. Multiple factors, including the concrete's mixing ratios and compressive strength, affect the initial hardening of the material. The data pertaining to the early creep behaviour of high-strength concrete were gathered from relevant literature sources for the purposes of this study. The research utilised artificial neural network methodology and relative importance analysis to ascertain the most influential parameters on the early creep behaviour of high-strength concrete. Understanding the components that contribute to creep is crucial in mitigating the detrimental effects of creep on concrete and concrete structures. [ABSTRACT FROM AUTHOR]

In the present study, the compressive strength of binary and ternary blended high-performance concrete mixtures was modeled using different machine learning tools, such as artificial neural network (ANN), gradient tree boosting (GTB), and multivariate adaptive regression splines (MARS). The compressive strength of binary and ternary blended concrete at 28 days of curing period was modeled using seven quantitative input parameters such as cement, blast furnace slag, fly ash, superplasticizer, water, coarse aggregate, and fine aggregate based on three different training and testing (Tr–Te) scenarios. The performance of the models developed was analyzed based on several statistical evaluation metrics, of error and efficiency. During the testing phase, the compressive strength estimates obtained via modeling using ANN, GTB, and MARS had Kling–Gupta efficiency (KGE) values of 0.8389, 0.8602, and 0.8423, respectively, for the first Tr–Te scenario; similarly, the KGE values were 0.8025/0.8830, 0.8541/0.8901, and 0.8434/0.8582, respectively, for the second/third Tr–Te scenarios. The estimation accuracy of the GTB model was relatively superior to that of the ANN and MARS models, taking into consideration both the error and efficiency indices. All three models perform relatively well for the first I/O combination compared to the other two combinations. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the effectiveness of machine learning (ML) models in predicting concrete damage using electromechanical impedance (EMI) data. From numerous experimental evidence, the damaged mortar sample with surface-mounted piezoelectric (PZT) material connected to the EMI response was assessed. This work involved the different ML models to identify the accurate model for concrete damage detection using EMI data. Each model was evaluated with evaluation metrics with the prediction/true class and each class was classified into three levels for testing and trained data. Experimental findings indicate that as damage to the structure increases, the responsiveness of PZT decreases. Therefore, we examined the ability of ML models trained on existing experimental data to predict concrete damage using the EMI data. The current work successfully identified the approximately close ML models for predicting damage detection in mortar samples. The proposed ML models not only streamline the identification of key input parameters with models but also offer cost-saving benefits by reducing the need for multiple trials in experiments. Lastly, the results demonstrate the capability of the model to produce precise predictions. [ABSTRACT FROM AUTHOR]

This study aims at utilizing machine learning (ML) in predicting the fire resistance and spalling degree of reinforced concrete (RC) columns with improved accuracy and reliability. A database with 119 test specimens was created for the development of ML-based regression models, and a database with 101 test specimens was created for the development of ML-based classification models. Six ML algorithms—support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), and light gradient boosting machine (LightGBM). The hyperparameters of the ML-based models were optimized through Bayes optimization search (BayesSearchCV) with ten-fold cross-validation. The results indicated that the AdaBoost not only accurately predicted the spalling degree of RC columns with an accuracy of 87%, but also performed best in predicting the fire resistance of RC columns with R2 = 0.96 and RMSE = 16.58. The AdaBoost model achieved high accuracy without significant bias, surpassing existing design equations. SHAP method was utilized to produce global explanations for the predictions. The results revealed that concrete compressive strength, loading ratio, slenderness ratio, and column width were the most critical features for spalling degree identification. Meanwhile, those were slenderness ratio, concrete cover, loading ratio, part of the fired column, and longitudinal reinforcement for fire resistance prediction. The parametric study demonstrated that the fire resistance of RC columns is positively affected by only concrete cover. [ABSTRACT FROM AUTHOR]

In this work, a total of nine ultra-high performance concrete (UHPC) filled steel tubular columns (UHPCFSTs) were fabricated and tested under cyclic loading. The results indicated that UHPCFSTs mainly exhibited flexural failure mode, compression-flexure failure mode and welding line fracture, and it changed from flexural failure mode to compression-flexure failure mode when the axial compression ratios exceeded 0.3. The elastic and ultimate drift ratios of UHPCFSTs were within the ranges of 1.37–2.05% and 2.38–4.32%, respectively, and the equivalent viscous damping coefficients were within the range of 0.197–0.358 when they failed, UHPCFSTs could sever as good choices in high seismic intensity areas. [ABSTRACT FROM AUTHOR]

In recent years, one of the most promising areas in modern concrete science and the technology of reinforced concrete structures is the technology of vibro-centrifugation of concrete, which makes it possible to obtain reinforced concrete elements with a variatropic structure. However, this area is poorly studied and there is a serious deficiency in both scientific and practical terms, expressed in the absence of a systematic knowledge of the life cycle management processes of vibro-centrifuged variatropic concrete. Artificial intelligence methods are seen as one of the most promising methods for improving the process of managing the life cycle of such concrete in reinforced concrete structures. The purpose of the study is to develop and compare machine learning algorithms based on ridge regression, decision tree and extreme gradient boosting (XGBoost) for predicting the compressive strength of vibro-centrifuged variatropic concrete using a database of experimental values obtained under laboratory conditions. As a result of laboratory tests, a dataset of 664 samples was generated, describing the influence of aggressive environmental factors (freezing–thawing, chloride content, sulfate content and number of wetting–drying cycles) on the final strength characteristics of concrete. The use of analytical techniques to extract additional knowledge from data contributed to improving the resulting predictive properties of machine learning models. As a result, the average absolute percentage error (MAPE) for the best XGBoost algorithm was 2.72%, mean absolute error (MAE) = 1.134627, mean squared error (MSE) = 4.801390, root-mean-square error (RMSE) = 2.191208 and R2 = 0.93, which allows to conclude that it is possible to use "smart" algorithms to improve the life cycle management process of vibro-centrifuged variatropic concrete, by reducing the time required for the compressive strength assessment of new structures. [ABSTRACT FROM AUTHOR]

Background: Concrete, a fundamental construction material, stands as a significant consumer of virgin resources, including sand, gravel, crushed stone, and fresh water. It exerts an immense demand, accounting for approximately 1.6 billion metric tons of Portland and modified Portland cement annually. Moreover, addressing extreme conditions with exceptionally nonlinear behavior necessitates a laborious calibration procedure in structural analysis and design methodologies. These methods are also difficult to execute in practice. To reduce time and effort, ML might be a viable option. Material and Methods: A set of keywords are designed to perform the search PubMed search engine with filters to not search the studies below the year 2015. Furthermore, using PRISMA guidelines, studies were selected and after screening, a total of 42 studies were summarized. The PRISMA guidelines provide a structured framework to ensure transparency, accuracy, and completeness in reporting the methods and results of systematic reviews and meta-analyses. The ability to methodically and accurately connect disparate parts of the literature is often lacking in review research. Some of the trickiest parts of original research include knowledge mapping, co-citation, and co-occurrence. Using this data, we were able to determine which locations were most active in researching machine learning applications for concrete, where the most influential authors were in terms of both output and citations and which articles garnered the most citations overall. Conclusion: ML has become a viable prediction method for a wide variety of structural industrial applications, and hence it may serve as a potential successor for routinely used empirical model in the design of concrete structures. The non-ML structural engineering community may use this overview of ML methods, fundamental principles, access codes, ML libraries, and gathered datasets to construct their own ML models for useful uses. Structural engineering practitioners and researchers may benefit from this article's incorporation of concrete ML studies as well as structural engineering datasets. The construction industry stands to benefit from the use of machine learning in terms of cost savings, time savings, and labor intensity. The statistical and graphical representation of contributing authors and participants in this work might facilitate future collaborations and the sharing of novel ideas and approaches among researchers and industry professionals. The limitation of this systematic review is that it is only PubMed based which means it includes studies included in the PubMed database. [ABSTRACT FROM AUTHOR]

Summary: This paper summarizes the literature available on the behavior of concrete‐filled steel tubular (CFST) columns to evaluate the effect of geometrical properties such as shape of cross‐section, diameter‐to‐thickness ratio, and length‐to‐diameter ratio of CFST columns under axial loading. Then, the impact of different material composition for core concrete and encasing material is concluded for columns under axial loading. The performance of CFST is evaluated in terms of failure modes, ductility, stiffness, and axial compressive strength. For encasing tube, carbon steel, stainless steel, and aluminum are studied while for core, various concretes such as Normal (NSC) and high strength concrete (HSC), light weight concrete (LWC), recycled aggregate concrete (RAC), expansive concrete (EC), rubber crumb concrete(RuCC), and steel slag concrete (SSC) are covered for review in this paper. Material limitations as provided by various codes for design of composite structures is also mentioned for both tube and core concrete. Failure modes of concrete‐filled tubular(CFT) columns are most affected by geometric properties columns while materials used for concrete and encasing tube do not cause much difference. Though, ductility, axial compressive strength, and stiffness are affected by both geometric and material properties. [ABSTRACT FROM AUTHOR]

Preplaced aggregate concrete (PAC) also known as two-stage concrete (TSC) is widely used in construction engineering for various applications. To produce PAC, a mixture of Portland cement, sand, and admixtures is injected into a mold subsequent to the deposition of coarse aggregate. This process complicates the prediction of compressive strength (CS), demanding thorough investigation. Consequently, the emphasis of this study is on enhancing the comprehension of PAC compressive strength using machine learning models. Thirteen models are evaluated with 261 data points and eleven input variables. The result depicts that xgboost demonstrates exceptional accuracy with a correlation coefficient of 0.9791 and a normalized coefficient of determination (R2) of 0.9583. Moreover, Gradient boosting (GB) and Cat boost (CB) also perform well due to its robust performance. In addition, Adaboost, Voting regressor, and Random forest yield precise predictions with low mean absolute error (MAE) and root mean square error (RMSE) values. The sensitivity analysis (SA) reveals the significant impact of key input parameters on overall model sensitivity. Notably, gravel takes the lead with a substantial 44.7% contribution, followed by sand at 19.5%, cement at 15.6%, and Fly ash and GGBS at 5.9% and 5.1%, respectively. The best fit model i.e., XG-Boost model, was employed for SHAP analysis to assess the relative importance of contributing attributes and optimize input variables. The SHAP analysis unveiled the water-to-binder (W/B) ratio, superplasticizer, and gravel as the most significant factors influencing the CS of PAC. Furthermore, graphical user interface (GUI) have been developed for practical applications in predicting concrete strength. This simplifies the process and offers a valuable tool for leveraging the model's potential in the field of civil engineering. This comprehensive evaluation provides valuable insights to researchers and practitioners, empowering them to make informed choices in predicting PAC compressive strength in construction projects. By enhancing the reliability and applicability of predictive models, this study contributes to the field of preplaced aggregate concrete strength prediction. [ABSTRACT FROM AUTHOR]

The construction industry relies so heavily on concrete that it's crucial to precisely forecast and optimize the strength and workability of concrete mixtures, while reducing costs as much as possible. For this objective, this study tries to predict and optimize the compressive strength and workability (slump) of concrete by using deterministic and robust optimization approaches, so as to determine the optimum concrete mixture proportions, while minimizing cost. Specifically, strength and slump were predicted based on concrete mixture proportions with five different machine learning techniques—support vector machine (SVM), artificial neural network (ANN), fuzzy inference system (FIS), adaptive fuzzy inference system (ANIS), and genetic expression programming (GEP), based on a dataset comprising two hundred concrete mixtures, which has various levels of key ingredients, including cement, water, fine aggregate, coarse aggregate, and size of coarse aggregate, along with their associated measures of strength and workability. These ingredients were used as input parameters, while compressive strength and slump (representing workability) served as output parameters for each mix proportion. Experimental investigations were conducted on fifteen distinct concrete mixes to validate the performance of the five networks, finding that ANFIS can yield the best results both for training and validation. This study provides valuable insights for predicting concrete properties and optimizing concrete mixture proportions, thus helping to maximize strength and workability while minimizing costs. [ABSTRACT FROM AUTHOR]

In the field of Reinforced Concrete Construction, concrete emerges as the predominant and extensively employed construction material. Concrete comprises a solid, chemically inert granular substance called coarse aggregate (CA) bonded with cement and water. Compared to fine aggregate or cement, CA has a larger volume of concrete. By examining the characteristics of the coarse aggregate using various laboratory testing processes, the coarse aggregate may be properly used in concrete. Bangladesh is experiencing significant growth in its infrastructure industry due to the construction of mega projects nationwide. For the building of RCC in Bangladesh, coarse aggregate is mainly procured from two sources in Bangladesh, and another is imported from China. This study aims to develop a clear understanding of aggregate and concrete strength quality for different coarse aggregates and track changes in the appearance of CA from multiple sources in China and Bangladesh. Coarse aggregates were collected from four prominent sources: Jaflong and Bholaganj (Bangladesh), Shandong, and Jiangsu (China). ACV (aggregate crushing value), gradation, voids, and unit weight; AIV (aggregate impact value), absorption, specific gravity, and resistance to abrasion-induced deterioration; and Los Angeles (LA) machines’ impact tests have been conducted for all sources of CA. The concrete cylinder was made and tested for all sources of CA with the same ratio of cement, sand, and water to know the concrete strength for different CAs. [ABSTRACT FROM AUTHOR]

In engineering applications, concrete crack monitoring is very important. Traditional methods are of low efficiency, low accuracy, have poor timeliness, and are applicable in only a limited number of scenarios. Therefore, more comprehensive detection of concrete damage under different scenarios is of high value for practical engineering applications. Digital image correlation (DIC) technology can provide a large amount of experimental data, and neural network (NN) can process very rich data. Therefore, NN, including convolutional neural networks (CNN) and back propagation neural networks (BP), can be combined with DIC technology to analyze experimental data of three-point bending of plain concrete and four-point bending of reinforced concrete. In addition, strain parameters can be used for training, and displacement parameters can be added for comprehensive consideration. The data obtained by DIC technology are grouped for training, and the recognition results of NN show that the combination of strain and displacement parameters, i.e., the response of specimen surface and whole body, can make results more objective and comprehensive. The identification results obtained by CNN and BP show that these technologies can accurately identify cracks. The identification results for reinforced concrete specimens are less affected by noise than those of plain concrete specimens. CNN is more convenient because it can identify some features directly from images, recognizing the cracks formed by macro development. BP can issue early warning of the microscopic cracks, but it requires a large amount of data and computation. It can be seen that CNN is more intuitive and efficient in image processing, and is suitable when low accuracy is adequate, while BP is suitable for occasions with greater accuracy requirements. The two tools have advantages in different situations, and together they can play an important role in engineering monitoring. [ABSTRACT FROM AUTHOR]

Permeable concrete is a type of porous concrete with the special function of water permeability, but the permeability of permeable concrete will decrease gradually due to the clogging behavior arising from the surrounding environment. To reliably characterize the clogging behavior of permeable concrete, particle swarm optimization (PSO) and random forest (RF) hybrid artificial intelligence techniques were developed in this study to predict the permeability coefficient of permeable concrete and optimize the aggregate mix ratio of permeable concrete. Firstly, a reliable database was collected and established to characterize the input and output variables for the machine learning. Then, PSO and 10-fold cross-validation were used to optimize the hyperparameters of the RF model using the training and testing datasets. Finally, the accuracy of the developed model was verified by comparing the predicted value with the actual value of the permeability coefficients (R = 0.978 and RMSE = 1.3638 for the training dataset; R = 0.9734 and RMSE = 2.3246 for the testing dataset). The proposed model can provide reliable predictions of the clogging behavior that permeable concrete may face and the trend of its development. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of concrete dynamic principal identification, a concrete dynamic principal identification model based on Improved Dung Beetle Algorithm (IDBO) optimized Long Short-Term Memory (LSTM) network is proposed. Firstly, the apparent stress–strain curves of concrete containing damage evolution were measured by Split Hopkinson Pressure Bar (SHPB) test to decouple and separate the damage and rheology, and this system was modeled by using LSTM network. Secondly, for the problem of low convergence accuracy and easy to fall into local optimum of Dung Beetle Algorithm (DBO), the greedy lens imaging reverse learning initialization population strategy, the embedded curve adaptive weighting factor and the PID control optimal solution perturbation strategy are introduced, and the superiority of IDBO algorithm is proved through the comparison of optimization test with DBO, Harris Hawk Optimization Algorithm, Gray Wolf Algorithm, and Fruit Fly Algorithm and the combination of LSTM is built to construct the IDBO-LSTM dynamic homeostasis identification model. The final results show that the IDBO-LSTM model can recognize the concrete material damage without considering the damage; in the case of considering the damage, the IDBO-LSTM prediction curves basically match the SHPB test curves, which proves the feasibility and excellence of the proposed method. [ABSTRACT FROM AUTHOR]

Understanding and predicting process–structure–property–performance relationships for concrete materials is key to designing resilient and sustainable infrastructure. While machine learning has emerged as a powerful tool to supplement empirical analysis and physical modeling, its capabilities are yet to be fully realized due to the massive data requirements and generalizability challenges. To address these limitations, we propose a knowledge‐informed machine learning framework for concrete property prediction that aggregates the wealth of domain knowledge condensed in empirical formulas and physics‐based models. By integrating the knowledge through data augmentation, feature enhancement, and model pre‐training, we demonstrate that this framework has the potential to (i) accelerate model convergence, (ii) improve model performance with limited training data, and (iii) increase generalizability to real‐world scenarios (including extrapolation capability to other datasets and robustness against data outliers). The overall improvement of machine learning models by knowledge integration is particularly critical when these models are scaled up to tackle the increasing complexity of modern concrete and deployed in practical applications. While demonstrated for predicting concrete strength, this versatile framework is applicable to a wide range of properties of concrete and other composite materials, paving the way for accelerated materials design and discovery. [ABSTRACT FROM AUTHOR]

Homicide perpetrators can use concrete as a means to conceal their victims. When concrete encasement is encountered in the forensic field, albeit rarely, it is often coupled with postmortem dismemberment. This method of obscuring the evidence presents unique investigative obstacles, specifically related to identification. Various approaches to obtaining fingerprints from decedents encased in concrete have been suggested and implemented over the years. The presented case is that of an initially unidentified 44‐year‐old male, who was subject to postmortem dismemberment and concrete encasement. Meticulous excavation techniques facilitated preservation of evidence and an anatomical reconstruction of the body. These techniques enabled inspection of the incision sites of the dismembered remains during the postmortem examination. Identifiable jewelry and tattoos were noted at autopsy. Further, the resulting concrete molds could be utilized to obtain fingerprints. These prints were used to ultimately identify the decedent. [ABSTRACT FROM AUTHOR]

This contribution summarizes the largest available literature data collection on tensile and shear loaded anchor tests, obtained in two independent studies and performed by two different research groups. It was the objective of the two studies to investigate a possible effect that petrographically different coarse aggregate types may have on the tensile and shear load capacity for concrete breakout failure modes. In total, seven normal-strength and four high-strength concretes were tested at two different ages. Structural tests were performed on cast-in (tensile and shear tests) and post-installed adhesive anchors (shear tests). Parallel to the structural tests, each concrete was characterized in terms of compressive and tensile strength. Finally, the combined experimental data offer novel insights into the predictive quality of available design models for concrete cone capacity in tension and edge breakout in shear with respect to a potential aggregate effect. Systematic analyses indicate only minor aggregate effects after normalization by compressive strength (less than 7% difference between normalized values). However, the study reveals potential curing and concrete age effects where a 9% increase in predicted values is shown when concrete cures longer. The predictive equations remain conservative in comparison to all the investigated properties and their validity is shown in this study. [ABSTRACT FROM AUTHOR]

The design process for pile foundations necessitates meticulous deliberation of the calculation pertaining to the bearing capacity of the piles. The primary objective of this work was to investigate the potential use of Coot bird optimization (CBO ) techniques in predicting the load-bearing capacity of concrete-driven piles. Despite the availability of several suggested models, the investigation of Coot bird optimization (CBO ) for estimating the pile-carrying capacity has been somewhat neglected in this research. This work presents and validates a unique approach that combines the Coot bird optimization (CBO ) model with the Multi-layered perceptron (MLP ) neural network and adaptive neuro-fuzzy inference system (ANFIS ). The findings of 472 different driven pile static load tests were put in a database. The proposed framework's building, validation, and testing stages were each accomplished utilizing the training set (70%), validation set (15%), and testing set (15%) of the dataset, respectively. According to the findings, MLP CBO and ANFIS CBO both offer remarkable possibilities for accurately predicting the pile-bearing capacity of a given structure. The R 2 values for ANFIS CBO during the training stage were 0.9874, while during the validating stage, they were 0.9785, and during the testing stage they were 0.987. After considering various kinds of performance studies and contrasting them with existing literature, it has been concluded that the ANFIS CBO model provides a more appropriate calculation of the bearing capacity of concrete-driven piles. [ABSTRACT FROM AUTHOR]

To investigate the confining effect and axial compressive behavior of rubberized concrete (RuC) confined by steel tube, axial compression tests were carried out on 12 circular RuC cylinders confined by steel tube. The cylinders considered parameters such as steel tube thicknesses (2 mm, 3 mm, 4 mm) and rubber volume replacement ratios of the fine aggregates (0%, 10%, 20%, and 30%). It was observed that the compressive strength of RuC decreases as the rubber volume replacement ratio increases. However, an increase in steel tube thickness enhances the confining effect of the core RuC, leading to an increase in its compressive strength and the corresponding strain, similar to that of conventional concrete. Moreover, post-peak curves are more likely to exhibit a strengthening part in confined concrete with more rubber content. Furthermore, a model was developed to determine the compressive strength of RuC confined by steel tube. Finally, an axial stress-strain model of steel tube confined RuC was proposed, which was validated against test results. [ABSTRACT FROM AUTHOR]

Ensuring an adequate bond between the steel tube and infilled concrete interface plays an essential role in achieving composite action for concrete-filled steel tubular (CFST) columns. Thus, this study proposes a new type of large diameter CFST column where the steel tube is reinforced by shear stoppers. The bearing strength of the infilled concrete is the decisive factor in evaluating the overall working efficiency between infilled concrete and steel tube. In this paper, we use nonlinear finite element analysis (NFEA) to investigate the bearing strength of the infilled concrete concerning the ratio of the steel tube's diameter to its thickness (D / t) , the number of shear stoppers N , the height of the shear stopper h b , and the concrete compressive strength (CCS) f c ′ . Our results show that the influencing factors on the bearing strength of the infilled concrete were arranged in descending order as follows: the number of shear stoppers, the height of shear stopper, the CCS, and the D / t ratio. We also analyze and highlight some significant parameters related to the bearing strength of infilled concrete. [ABSTRACT FROM AUTHOR]

The determination of mechanical properties for different building materials is a highly relevant and practical field of application for machine learning (ML) techniques within the construction sector. When working with vibrocentrifuged concrete products and structures, it is crucial to consider factors related to the impact of aggressive environments. Artificial intelligence methods can enhance the prediction of vibrocentrifuged concrete properties through the use of specialized machine learning algorithms for materials' strength determination. The aim of this article is to establish and evaluate machine learning algorithms, specifically Linear Regression (LR), Support Vector Regression (SVR), Random Forest (RF), CatBoost (CB), for the prediction of compressive strength in vibrocentrifuged concrete under diverse aggressive operational conditions. This is achieved by utilizing a comprehensive database of experimental values obtained in laboratory settings. The following metrics were used to analyze the accuracy of the constructed regression models: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root-Mean-Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R2). The average MAPE in the range from 2% (RF, CB) to 7% (LR, SVR) allowed us to draw conclusions about the possibility of using "smart" algorithms in the development of compositions and quality control of vibrocentrifuged concrete, which ultimately entails the improvement and acceleration of the construction and building materials manufacture. The best model, CatBoost, showed MAE = 0.89, MSE = 4.37, RMSE = 2.09, MAPE = 2% and R2 = 0.94. [ABSTRACT FROM AUTHOR]

In order to study the strength change of concrete specimens under different loading conditions, based on the principle of minimum energy dissipation, the damage energy per unit area of concrete was studied. By using finite element numerical simulation software for concrete specimens with different failure modes of tension, pressure, bending and torsion, a double-broken line damage constitutive model is adopted. The failure forms of concrete specimens under different loading conditions, as well as the failure area and failure energy of each specimen during loading, are simulated and analyzed. The failure energy per unit area under different failure modes was quantitively calculated, the relationship between the failure area and failure energy consumption under different failure modes was analyzed. The results show that, under different failure modes, the failure area of concrete specimens is different, the energy consumed during failure is different, and the strength is different. However, no matter how the failure mode changes during the failure process, the failure energy W per unit area remains constant and fluctuates in the range of 2.0~6.0 mJ/cm2, which is related to the physical properties of concrete itself. [ABSTRACT FROM AUTHOR]

To promote sustainable growth and minimize the greenhouse effect, rice husk fly ash can be used instead of a certain amount of cement. The research models the effects of using rice fly ash as a substitute for regular Portland cement on the compressive strength of concrete. In this study, different machine-learning techniques are investigated and a procedure to determine the optimal model is provided. A database of 909 analyzed samples forms the basis for creating forecast models. The derived models are assessed using the accuracy criteria RMSE, MAE, MAPE, and R. The research shows that artificial intelligence techniques can be used to model the compressive strength of concrete with acceptable accuracy. It is also possible to evaluate the importance of specific input variables and their influence on the strength of such concrete. [ABSTRACT FROM AUTHOR]

This study investigated the impact of openings on the punching shear strength of 'non-coarse aggregate acrylic polymer concrete' (APC) slabs. Twelve specimens representing scaled models of a flat, with different parameters such as concrete type, opening size, and distance from the column edge. The specimens were divided into five groups, including a control group of normal strength concrete and APC specimen without openings, as well as APC specimens with various opening sizes and distances, along with APC enhancement specimens. Experimental results were validated using Abaqus finite element software, demonstrating good agreement between the experimental and simulated outcomes. The analysis encompassed several aspects, including load-deflection response, ultimate loading capacity, post-ultimate loading capacity, failure patterns, and strain distribution. The absence of coarse aggregate in APC significantly impacted its shear resistance, leading to a reduction in internal friction. Compared to ordinary concrete slabs, the ultimate capacity of APC slabs without openings was approximately 36% lower. The presence of two openings adjacent to the column further decreased the ultimate capacity by approximately 33%. The size and distance of the openings played a crucial role in determining the ultimate capacity of APC slabs. Notably, the APC specimens exceeded the design ultimate load calculated by Egyptian Code of Practice (ECP-203-2020) and ACI-318-19 codes by approximately 1.4 and 1.6, respectively. However, the study advises against relying solely on APC in shear-sensitive constructions without integrating additional enhancements for shear resistance, as the results indicate a significant reduction in the shear resistance compared to ordinary concrete slabs. [ABSTRACT FROM AUTHOR]

Several studies have successfully used fly-ash (FA)-like waste material for the manufacturing of geopolymer concrete (GPC). This study uses gene expression programming (GEP), a type of soft computing approach, to produce an empirical equation that estimates the compressive strength fc0 of GPC using FA. Through a thorough analysis of the published research, a consistent, large, and trustworthy data set is assembled in order to develop a model. 298 fc0 experimental outcomes make up the collected data set. The following are considered explanatory variables: the amount of extra water added as percent FA (%EW), the percentage of plasticizer (%P), the initial curing temperature (T), the specimen's age (A), the curing duration (t), the ratio of fine aggregate to total aggregate (F/AG), the percentage of total aggregate by volume (%AG), the molarity of the NaOH solution, the activator or alkali to FA ratio (AL/FA), the ratio of sodium oxide (Na2O) to water (N/W) for preparing Na2SiO3 solution, and the ratio of Na2SiO3 to NaOH (Ns/No). An empirical GEP equation is put forth to calculate the fc0 of GPC using FA. The suggested model's precision, applicability, and forecasting capacity were assessed using parametric analysis, statistical verification, and a comparison with both linear and non-linear regression equations. [ABSTRACT FROM AUTHOR]

The standard approach for testing ordinary concrete compressive strength (CS) is to cast samples and test them after different curing times. However, testing adds cost and time to projects, and, therefore, construction sites experience delays. Because carbon nanotubes (CNTs) vary in length, composition, diameter, and dispersion, experiment and formula fitting alone cannot reliably predict the strength of CNTs-based composites. For empirical equations or traditional statistical approaches to properly forecast complex materials' mechanical characteristics, various significant parameters, databases, and nonlinear relationships between variables must be considered. Machine learning (ML) tools are the most advanced for accurate predictions of material behaviour. This study employed gradient boosting, light gradient boosting machine, and extreme gradient boosting techniques to forecast the CS of CNTs-modified concrete. Also, in order to explore the influence and interaction of various features, an interaction analysis was conducted. In terms of R2, gradient boosting, light gradient boosting machine, and extreme gradient boosting models proved their accuracy. Extreme gradient boosting had the highest R2 of 0.97, followed by light gradient boosting machine and gradient boosting with scores of 0.94 and 0.93, respectively. This type of research may help both academics and industry forecast material properties and influential elements, thereby reducing lab test requirements. [ABSTRACT FROM AUTHOR]

To enhance the concrete confinement ability of circular-ended aluminum alloy tubes, carbon fiber reinforced polymer (CFRP) was bonded onto the tube surface to form CFRP confined concrete columns with circular ends (RCFCAT). Eight specimens were designed with number of CFRP layers and section aspect ratio as variables. Axial loading test and finite element analysis were carried out. Results showed CFRP delayed buckling of the aluminum alloy tube flat surfaces, transforming inclined shear buckling failure into CFRP fracture failure. Specimens with aspect ratio above 4 experienced instability failures. Under same cross-section, CFRP increased axial compression bearing capacity and ductility by up to 30.8% and 43.4% respectively. As aspect ratio increased, enhancement coefficients of bearing capacity and ductility gradually decreased, the aspect ratio is restrictive when it is less than 2.5. CFRP strengthening increased initial axial compression stiffness of specimens by up to 117.9%. The stiffness decreased gradually with increasing aspect ratio, with most significant increase at aspect ratio of 4. Strain analysis showed CFRP bonding remarkably reduced circumferential and longitudinal strains. Confinement effect was optimal at aspect ratio around 2.0. The rationality of the refined FE model established has been verified in terms of load displacement curves, capturing circular aluminum tube oblique shear buckling, concrete "V" shaped crushing, and CFRP tearing during specimen failure. The parameter analysis showed that increasing the number of CFRP layers is one of the most effective methods for improving the ultimate bearing capacity of RCFCAT. [ABSTRACT FROM AUTHOR]

Long‐term creep compliance is one of the most important mechanical properties for evaluating the long‐term behavior of concrete structures. This paper aims to optimize machine‐learning models to predict this viscoelastic property. The most relevant dataset available in the literature is considered, cleaned, and preprocessed to optimize the outcome. The advanced XGBoost model, which is to be the most effective shallow machine‐learning model for modeling tabular datasets, is employed in this study to maximize model accuracy. Short‐term creep compliances of a given sample at typical ages are used as input features to model the long‐term creep compliance of concrete. This approach outperforms standard machine‐learning approaches that do not include short‐term creep as an input feature. Indeed, the short‐term behavior of concrete strongly influents its long‐term one. The optimized machine model presented herein is accurate and useful for practical applications. It uses input features that are easy to obtain to predict long‐term creep compliance up to several decades, which is difficult and expensive to measure. [ABSTRACT FROM AUTHOR]

High‐strength steel pipe joints are proposed for assembled composite columns. The steel pipe extends into the upper and lower prefabricated columns and is anchored by poured concrete or grout to realize the connection of the prefabricated columns. Bonding performance of high‐strength steel pipe and surrounding concrete/grout is critical to the joint reliability. Steel pipe push‐out test was carried out, considering parameters of steel pipe section specification, bonding length, bonding material, and outer wall treatment of the steel pipe. Comparative analysis of measured load–displacement/slip curves and calculated bond strength were conducted. Bonding length of 600 mm could provide sufficient bond strength. Increasing bonding length could delay the slippage, but the bond strength decreased. High‐strength grout significantly increased the peak load size. The slenderness ratio and diameter–thickness ratio of the steel pipe had no obvious influence on the bonding performance. Steel pipe with D114*25 section obtained higher bonding strength with smaller ratio of bonding area to cross‐sectional area. Spiral ribs welded to the outer wall of the steel pipe could effectively improve the bonding strength and delay the interface slip. With easy construction and reliable performance, spiral ribs were recommended to be applied in prRactical projects. [ABSTRACT FROM AUTHOR]

Much of the current code provisions for designing slab-column connections against punching shear are based on empirically derived formulations based on tests of partial scale isolated slab-column specimens. Although many experiments have been conducted on shear reinforced concrete flat slabs supported on columns, due to cost or time constraints, there are still many parameters that have not been adequately studied in the laboratory. These tests can be supplemented by analytical results of properly calibrated nonlinear finite element analysis (NLFEA) to enhance the existing experimental database and formulate rational design recommendations for future codes. This paper presents a rational approach for calibrating an NLFEA model in ABAQUS using interior and edge slab-column connection specimens. The calibration includes a study to determine how to effectively model the shear reinforcement and shear reinforced area. This further includes a detailed analysis of the modeling of the shear reinforcing elements to ensure appropriate rotational capability of the shear reinforced region of the slab without significantly reducing the predicted capacity of the model. The calibrated models show good agreement with test data based on load-deflection, moment-curvature, and bolt strain behavior. [ABSTRACT FROM AUTHOR]

Aging reinforced concrete (RC) building structures typically experience more severe damage and are prone to collapse during earthquakes, constituting a primary factor in casualties and direct economic losses. To enhance the seismic performance of these old structures, this paper proposes a seismic risk assessment and a micro-concrete restoration method. It applies the process to an existing three-story reinforced concrete structure. A practical framework for mitigating structural vulnerabilities in seismic-prone regions was proposed. Then an as-built survey was conducted to create as-built architectural and structural drawings. Concrete core tests, ferroscans, and rebar tests were also performed. Based on field surveys and test data, nonlinear static and dynamic analyses have been used to evaluate structural safety. Concrete column jacketing was used to strengthen weak existing columns with micro-concrete. In assessing the structural response of retrofitted buildings, a comparison was made to their initial state. The comparison shows that applying concrete column jacketing with micro concrete can reduce other structural elements' demand capacity ratio (DCR), minimize maximum displacements, and enhance overall stiffness. The results indicate that the proposed method effectively evaluates the seismic risk of aging structures and enhances seismic resilience in existing buildings. Moreover, the application to the actual structure demonstrates that micro-concrete is highly durable and compatible with parent-concrete. [ABSTRACT FROM AUTHOR]