Strength Of Self-compacting Concrete Research Articles

Exploring the interrelationships between composition, rheology, and compressive strength of self-compacting concrete: An exploration of explainable boosting algorithms

This study introduces a novel methodology for enhancing the compressive strength of self-compacting concrete (SCC) via the use of the Explainable Boosting Machine (EBM), a sophisticated and interpretable machine learning algorithm. It presents a data-driven model that aims to accurately predict the strength of SCC by considering the intricate interactions among its various elements. Additionally, the model provides insights into the variables that influence SCC's compressive strength. By using EBM in conjunction with XGBoost and CatBoost algorithms, this study conducts a comparative examination of predictive abilities using datasets related to composition and rheology. The findings reveal that CatBoost has greater predictive performance using rheology dataset, as shown by an R2 value of 0.977. Conversely, XGBoost exhibits a higher predictive capability using the composition dataset, as indicated by an R2 value of 0.947. The EBM can provide comprehensive explanations at both global and local levels. It effectively identifies the key factors that have a significant influence on compressive strength. These factors include the coarse aggregate content, cement content, water content, viscosity, and V-funnel flow time. The study findings provide more evidence to support the notion that including rheological data into the model leads to a notable improvement in its accuracy. This outcome further confirms the existence of a direct correlation between rheological properties and compressive strength. The explanatory insights provided by EBM give practical instructions for customising SCC mixes to attain desired strengths. This facilitates quality control and enables personalised concrete design in the field of construction. This study highlights the potential of interpretable machine learning algorithms in improving the predictive modelling of SCC features. This advancement may lead to the development of more durable, efficient, and customised building materials.

Influence of recycled steel fiber on the properties of self-compacting concrete with high fly ash content


 
 
 The recycled steel fibers from waste steel cables have properties suitable for enhancing the load-bearing capacity, impact resistance, and shrinkage reduction of Self-Compacting Concrete (SCC). The use of a high content of fly ash in SCC reduces production costs and is environment friendly by minimizing the amount of cement in the mix. This article presents the experimental research results on the influence of the content of recycled steel fibers on the properties of high-fly-ash SCC. The research utilized recycled steel fibers from elevator steel cables with volume percentages of 0%, 0.5%, 1% and 1.5% respectively in SCC, with a fly ash content in the SCC mix of 50% by volume of powder. The evaluated properties included workability, plastic shrinkage, drying shrinkage compressive strength, and tensile strength. The results indicate that using recycled steel fibers with a maximum content of 1% enables the production of SCC that meets European standards for workability. In addition, when comparing SCC with 0.5% and 1% recycled steel fiber content to control SCC samples (without steel fibers), the compressive strength of SCC increased by 5.04% and 7.56% respectively, the tensile strength increased by 2.99% and 5.97% respectively, plastic shrinkage decreased by 27.34% and 30.47% respectively, and 28-day drying shrinkage decreased by 3.2% and 4.6% respectively. Using recycled steel fibers combined with high-volume fly ash is a reasonable solution to save production costs and be environmentally friendly. Additionally, this solution not only improves the compressive and tensile strength but also limits plastic and drying shrinkage deformations of SCC. As a result, the durability of SCC is enhanced.
 
 

Influence of Glass Microfibers on the Control of Autogenous Shrinkage in Very High Strength Self-Compacting Concretes (VHSSCC)

High-performance concrete (HPC) is widely used in infrastructure for its durability and sustainability benefits. However, it faces challenges like autogenous shrinkage, leading to potential cracking and reduced durability. Fiber reinforcement offers a solution by mitigating shrinkage-induced stresses and enhancing concrete durability. In this sense, this study investigates the use of glass microfibers to mitigate autogenous shrinkage and early-age cracking in high-strength self-compacting concrete. Samples were prepared with two water-to-binder ratios (w/b): 0.25 and 0.32; and three glass microfiber contents: 0.20%, 0.25%, and 0.30 vol.%. The concrete mixtures were characterized in the fresh state for slump flow and in the hardened state for compressive strength, static, and dynamic Young’s modulus. Unrestrained and restrained shrinkage tests were also conducted in the seven days-age. The findings revealed that glass microfibers reduced the workability in mixtures with lower slump flow values (w/b of 0.25), while less viscous mixtures (w/b of 0.32) exhibited a slight improvement. Compressive strength showed a proportional enhancement with increasing fiber contents in concretes with a w/b ratio of 0.32. A contrasting trend emerged in concretes with a w/b ratio of 0.25, wherein strength diminished as fiber additions increased. The modulus of elasticity improved with fiber additions only in the matrix with a w/b ratio of 0.25, showing no correlation with compressive strength results. In shrinkage tests, the addition of glass microfibers up to specific limits (0.20% for a w/b ratio of 0.25 and 0.25% for w/b of 0.32) demonstrated improvements in controlling concrete deformation in unrestrained shrinkage analyses. Concerning cracking reduction in restrained concrete specimens, the mixtures did not exhibit significant improvements in crack prevention.

Compressive Behaviour of Long Steel Tube Columns Filled with Recycled Large Aggregate Self-Compacting Concrete

One of the important directions for green development in the world today is to expand the application methods of recycled concrete, improve the utilization rate of waste aggregates, and slow down the consumption of natural resources. The column structure with a large length and slenderness ratio is the most widely used compression unit in practical engineering, which conforms to the principle of sustainable development. In this paper, we study the mechanical properties and failure modes of long columns fabricated from steel tubes filled with recycled large aggregate self-compacting concrete (RLASCC-ST-LC) under compression load. Moreover, we examine the influence of steel tube thickness, recycled large-aggregate particle size, the strength of self-compacting concrete, and the length-to-diameter ratio on the performance of the members through finite element modelling. The results indicated that RLASCC-ST-LCs exhibited different degrees of buckling damage, and the damage processes were basically the same as that of steel tube concrete. When the thickness of steel pipe increased from 4 mm to 5 mm, the ultimate bearing capacity of the component increased by 12.1%; when the strength of self-compacting concrete increased from 30 MPa to 40 MPa and 50 MPa, the ultimate loads of the component increased by 6.96% and 12.4%, respectively. However, the increase in the aspect ratio weakened the bearing capacity of the component, and the ultimate bearing capacities reduced by 4.78% and 10.51% when the aspect ratios were 8, 10, and 12. Finally, based on the existing design codes, the theoretical calculation formulas are proposed for the ultimate bearing capacities of RLASCC-ST-LCs. These findings have significant implications for the widespread application of RLASCC-ST-LCs.

Modeling the Properties of Sustainable Self-Compacting Concrete Containing Marble and Glass Powder Wastes Using Response Surface Methodology

This study aims to apply the response surface methodology (RSM) to develop a statistical model that predicts and models the performance of both the fresh and hardened properties of self-compacting concrete (SCC). RSM was used to model processes involving three variables: the water/binder ratio, and the percentages of waste marble, and glass powder. Tests, including slump flow diameter, sieve stability, and L-box, were carried out to evaluate the fresh properties of the self-compacting concrete; compressive strength was analyzed at 7, 28, and 90 days. Statistical significance was only observed in the water/binder ratio for both the slump flow and sieve stability tests. Furthermore, these results indicate that the models used in the compressive strength tests demonstrate a high statistical significance for all ages. The findings suggest that incorporating waste marble powder (MP) and glass powder (GP) in SCC necessitates a significant amount of superplasticizer to counteract the workability loss, and it improves the compressive strength of SCC. The coefficients analyzed using the RSM approach validate its effectiveness as a predictive tool for determining the hardened properties of self-compacting concrete.

REVIEW OF THE EFFECT OF INDUSTRIAL WASTE ON SELF COMPACTING CONCRETE

Self-Compacting Concrete (SCC) is an innovation in concrete technology used in building elements with dense and complex reinforcement frameworks. SCC is characterized by its high fluidity and the ability to self-compact without the use of vibrators. Superplasticizer is one of the components applied in the production of SCC. The use of superplasticizer aims to reduce water consumption, accelerate the setting time, improve concrete workability, and also make the concrete impermeable.
 As time progresses, industrial waste continues to increase, highlighting the importance of recycling industrial waste. The purpose of this research is to investigate the influence of industrial waste as a substitute material for fine aggregates on the workability and compressive strength of SCC. This study is the result of a literature review obtained by examining various sources including books, proceedings, as well as international and national journals.
 The results of this literature study involve the use of industrial waste materials such as recycled glass, granite waste, nickel slag, and copper slag as substitutes for fine aggregates. The results of the workability and compressive strength of SCC are influenced by the characteristics of each industrial waste used. This indicates an improvement in the flowability (slump flow) with an increasing percentage of industrial waste, ranging from 0% to 60%, in the concrete composition.
 Compressive strength test results indicate that the optimal percentage of industrial waste addition in the concrete composition lies between 20% and 40%."

Influence of coatings on residual strength of geopolymer concrete columns subjected to fire exposure: An experimental investigation

Fires occurring within buildings present a grave concern, as they entail substantial risks to human lives, property, and the environment. Implementing appropriate fire safety measures becomes imperative to mitigate the occurrence of fires and ensure efficient response in the event of an incident. The primary aim of the study is to examine the performance of various coatings on the residual strengths of the reinforced normal strength self-compacting geopolymer concrete (NSGC) and high strength self-compacting geopolymer concrete (HSGC) columns subjected to standard temperature exposure in accordance with ISO 834 guidelines. The design compressive strength of the concretes was 36.12, 57.06 and 52.8 MPa. Two types of coating were employed in the present investigation namely ceramic wool wrapping and high alumina cement. A computerized electric furnace is used to heat the column specimens. Amongst the forty-two columns developed, six specimens tested without temperature exposure and protective coating as a reference specimen. Twelve specimens were heated as per ISO fire curve and tested to assess the residual axial strength performance and twenty-four specimens were coated with protective layers, then heated and tested to assess the post fire performance. It is exemplified that the composite protective coating offers effective resistance to concrete, gaining optimum axial strength performance. The findings show that these fire protecting composites have a high potential for utilizing as new strengthening techniques for reinforced concrete (RC) columns.

XGB-Northern Goshawk Optimization: Predicting the Compressive Strength of Self-Compacting Concrete

XGB-Northern Goshawk Optimization: Predicting the Compressive Strength of Self-Compacting Concrete

Experimental study on the effect of colloidal nano silica in high strength self-compacting concrete containing ground granulated blast furnace slag

Self-compacting concrete (SCC) is profusely utilized in building construction due to its unique design and mechanism of flow without segregation by occupying the corners and preventing the voids to achieve better compaction and mechanical strength. This work addresses the effect of colloidal nano-silica (NS) of size 5-40nm with an active nano solids content of 32% on the high-strength SCC containing Ground Granulated Blast Furnace Slag (GGBFS) and Ordinary Portland Cement as binders in addition to NS. The liquid content of 68% in NS was added to the water content in the mix. The ratios of NS were evaluated at 0%,0.5%,1%,1.5%, 2% and GGBFS as 20% by weight of binder in concrete. The water to binder ratio was 0.33 with a water and binder content of 184kg/m3 and 557.58 kg/m3. The optimum mix was 1.5% NS based on the concrete’s slump and compressive strength at 28 days. The slump was reduced with a rise in NS beyond 1.5% due to the accumulation of NS particles causing the reduction in flowability. The density of SCC for all the mixes was satisfactory. The compressive strength of optimum SCC, M21.5NS20G was decreased by 5.88% and increased by 0.49% and 2.45% at 7, 28 and 91 days to that of the reference mix whereas its split tensile strength was risen by 31.13%, 9.27%, and 12.41% and flexural strength was risen by 5.8% and reduced by 8.17% and 4.06% to that of the reference mix at aforementioned durations. The SEM and XRD analyses were conducted on the optimum hardened SCC mix at 28 days in which the presence of Calcium silicate hydrate compounds of different crystalline structures, viz., Ettringite, and Portlandite were observed. The SCC designed in the work can be used for structural applications such as beam-column joints.

Predicting 28-Day Compressive Strength of Self-Compacting Concrete (SCC) Using Gene Expression Programming (GEP)

SCC is an innovative building material which can flow and compact itself without the use of external vibrations. It is an effective material to enhance the use of industrial waste products such fly ash and silica fume etc in concrete to reduce the carbon emissions from construction industry. Despite the many advantages of SCC over conventional concrete, there are very few methods which can effectively forecast compressive strength of SCC. It is due to the non-linear behaviour of SCC in relation to its mixture components. Thus, an innovative Machine Learning technique called Gene Expression Programming (GEP) is employed to estimate the strength of SCC. For this purpose, a database consisting of 231 datapoints is constructed using extensive literature search. The algorithm resulted in an empirical equation that relates compressive strength with seven most influential parameters: cement, fly ash, silica fume, coarse and fine aggregate, water, and superplasticizer. The dataset is split into two sets called the training and validation datasets having 70% and 30% of the data respectively. The training and validation data will be used to train and validate the algorithm respectively. The algorithm’s accuracy is checked by calculating the four commonly used error metrices: mean absolute error (MAE), root mean square error (RMSE), coefficient of correlation (R) and performance index (ƿ) for both datasets. The statistical evaluation revealed that the errors are within the ranges specified in the literature. The accuracy of the algorithm is also verified by plotting scatter and series plots of training and validation datasets. Thus, the developed equation by GEP algorithm can be effectively used to forecast the 28-day compressive strength of SCC having fly ash and silica fume as mineral admixtures.

Effects of waste magnesia powder as partial cement replacement on self-compacting concrete

This experimental study investigated the performance of self-compacting concrete (SCC) mixes with magnesia (MgO) waste. In the series produced with a water/binder ratio of 0.40, cement was replaced by magnesia waste at 2%, 4% and 10% by weight in the SCC. Le Chatelier test, slump flow, compressive strength, flexural strength, depth of penetration of water under pressure, ultrasonic pulse velocity and water absorption by capillary testing was conducted to assess sample performance. X-ray diffraction, thermogravimetric analysis, mercury intrusion porosimetry, differential thermal analysis and scanning electron microscopy were used for the microstructural analysis and quantification of phases within each sample. The results indicated that concrete with magnesia waste contains magnesium silicate hydrate (M–S–H) and brucite ((Mg(OH)2) products. Brucite causes strength loss in concrete. Up to 90 days, specimens with magnesia showed increasing compressive and flexural strength. As the amount of magnesia waste increased, the porosity, depth of water penetration under pressure and water absorption by capillary increased. Incorporating more than 10% of magnesia waste in the SCC mixtures resulted in declining strength. The addition of magnesia waste enhanced the expansion of SCC. An optimum dosage (2%) of magnesia waste was the most advantageous to the strength of SCC.

Experimental Study on Direct Shear Strength of Fiber Reinforced Self Compacting Concrete under Acid and Sulfate Attack

This study primary is to investigate the shear strength of self-compacting concrete (SCC) reinforced by steel-fiber (SF) and polypropylene-fiber (PPF) in different environmental conditions: the air, sulfate (MgSO4 with a concentration of 5%) and acid (H2SO4 with a concentration of 5%). The study also examines the effect of fiber volume fraction on the workability, shear strength, compressive strength, and splitting tensile strength of fiber reinforced SCC. The article aims to determine the durability effects of both fibers and their resistance to aggressive environmental conditions. The contribution of this article is an experimental investigation on the shear strength of SCC reinforced by SF as well as PPF in 3 different environmental conditions after 30 days of exposure. The study also investigated the fresh and mechanical properties of 5 different mixtures of SCC with/out 0.1% and 0.2% fibers. The study also concluded that PPF decreased the workability of SCC badly, and special care must be taken when selecting its volume fraction. Also, it was found that generally shear strength of SCC mixes enhanced with increasing SF and PPF volume fraction. Moreover, it was found that both fibers have good durability effects, and resist aggressive environmental conditions, with the best results obtained from samples containing 0.2% SF. In the air condition, while the compressive strength, shear strength and tensile strength results were 52.6MPa, 6.43MPa and 3.91MPa, in the sulfate condition those were 46.37MPa, 6.55MPa and 3.59MPa, and in the acid condition those were 34.4MPa, 5.5MPa and 3.46MPa, respectively.

Comparative analysis of various machine learning algorithms to predict 28-day compressive strength of Self-compacting concrete

Construction industry is indirectly the largest source of CO2 emissions in the atmosphere, due to the use of cement in concrete. These emissions can be reduced by using industrial waste materials in place of cement. Self-Compacting Concrete (SCC) is a promising material to enhance the use of industrial wastes in concrete. However, there are very few methods available for accurate prediction of its strength, therefore, reliable models for estimating 28-day Compressive Strength (C–S) of SCC are developed in current study by using three Machine Learning (ML) algorithms including Multi Expression Programming (MEP), Extreme Gradient Boosting (XGB), and Random Forest (RF). The ML models were meticulously developed using a dataset of 231 points collected from internationally published literature considering seven most influential parameters including cement content, quantities of fly ash and silica fume, water content, coarse aggregate, fine aggregate, and superplasticizer dosage to predict C–S. The developed models were evaluated using different statistical errors including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R2) etc. The results showed that the XGB model outperformed the MEP and RF model in terms of accuracy with a correlation R2 = 0.998 compared to 0.923 for MEP and 0.986 for RF. Similar trend was observed for other error metrices. Thus, XGB is the most accurate model for estimating C–S of SCC. However, it is pertinent to mention here that it does not give its output in the form of an empirical equation like MEP model. The construction of these empirical models will help to efficiently estimate C–S of SCC for practical purposes.

NDT prediction of self-compacting concrete strength based on maturity method

NDT prediction of self-compacting concrete strength based on maturity method

Machine learning approach for investigating compressive strength of self-compacting concrete containing supplementary cementitious materials and recycled aggregate

Supplementary cementitious materials (SCMs) and recycled coarse aggregate (RCA) have the potential for sustainable development and resource utilization and have been widely applied in self-compacting concrete (SCC). However, traditional experiment approaches for predicting its compressive strength suffer from inefficiency, time-consuming, and high cost. To address these issues, machine learning (ML) models are employed, evaluated, and compared in this paper. First, a database of 337 samples with 10 features is collected and established. Then, the 12 ML models, including SVM, KNN, DT, RF, ANN, GBoost, XGBoost, AdaBoost, CatBoost, LightGBM, HistGBM, and GEP are applied for modeling to predict the compressive strength. Finally, both feature importance, individual conditional expectation, and partial dependence plot are utilized to assess the importance and the effect of each and coupled parameters. The results show that the CatBoost and LightGBM models generally possess superior performance in various aspects, including higher R2 values, lower RMSE, MSE, and MAE values, as well as smaller errors. Among the Boosting models, except for the AdaBoost model, satisfactory predictive ability is observed. The ANN and RF models also demonstrate relatively good performance, and the SVM, DT, KNN, and GEP models present relatively poorer performance. Curing age and W/CMs exhibit the most pivotal features on model prediction. Furthermore, RCA replacement ratio plays a more prominent role than CA, and SCMs contribute somewhat to the compressive strength of concrete, but not as much compared to cement. Also, it is important to acknowledge the significant contribution of SP. This study offers a systematic evaluation of the prediction of compressive strength in SCC containing SCMs and RCA, making a notable contribution to both the existing literature and practical applications.

Mechanical and shrinkage performance of steel fiber reinforced high strength self-compacting lightweight concrete

This study investigated the effects of mixture proportion on the mechanical and shrinkage behaviors of steel fiber reinforced high strength self-compacting lightweight concrete (SHSLC) mixtures incorporating bottom ash aggregates for internal curing. In this study, different types of fine aggregates, fine aggregate ratios, supplementary materials such as silica fume (SF) and colloidal nano-silica (CNS), and four types of steel fibers were considered to develop SHSLC. The self-compacting characteristics of concrete were evaluated by means of slump flow, J-ring flow, and V-funnel tests. The test results indicate that incorporation of SF and CNS had a positive effect on the mechanical properties, but 4% incorporation of CNS decreased workability and strength due to the agglomeration of mixture. The addition of steel fibers improved compressive strength, and flexural toughness of SHSLC, but reduced the flowability and passing ability with increased fiber contents. Due to the internal curing from coarse bottom ash, significant reduction of autogenous shrinkage up to 33% was observed, whereas 7% for drying shrinkage. The incorporation of fibers reduced the autogenous and drying shrinkage up to 31% and 22% compared with non-fiber reinforced concrete, respectively. Finally, the autogenous shrinkage behavior was simulated with several models in the literature, and a model for high strength self-compacting lightweight concrete with and without fibers considering equivalent age was proposed.

Machine Learning Models for the Prediction of the Compressive Strength of Self-Compacting Concrete Incorporating Incinerated Bio-Medical Waste Ash

Self-compacting concrete (SCC) is a special form of high-performance concrete that is highly efficient in its filling, flowing, and passing abilities. In this study, an attempt has been made to model the compressive strength (CS) of SCC mixes using machine-learning approaches. The SCC mixes were designed considering lightweight expandable clay aggregate (LECA) as a partial replacement for coarse aggregate; ground granulated blast-furnace slag (GGBS) as a partial replacement for binding material (cement); and incinerated bio-medical waste ash (IBMWA) as a partial replacement for fine aggregate. LECA, GGBS, and IBMWA were replaced with coarse aggregate, cement, and fine aggregate, respectively at different substitution levels of 10%, 20%, and 30%. M30-grade SCC mixes were designed for two different water/binder ratios—0.40 and 0.45—and the CS of the SCC mixes was experimentally determined along with the fresh state properties assessed by slump-flow, L-box, J-ring, and V-funnel tests. The CS of the SCC mixes obtained from the experimental analysis was considered for machine learning (ML)-based modeling using paradigms such as artificial neural networks (ANN), gradient tree boosting (GTB), and CatBoost Regressor (CBR). The ML models were developed considering the compressive strength of SCC as the target parameter. The quantities of materials (in terms of %), water-to-binder ratio, and density of the SCC specimens were used as input variables to simulate the ML models. The results from the experimental analysis show that the optimum replacement percentages for cement, coarse, and fine aggregates were 30%, 10%, and 20%, respectively. The ML models were successful in modeling the compressive strength of SCC mixes with higher accuracy and the least errors. The CBR model performed relatively better than the other two ML models, with relatively higher efficiency (KGE = 0.9671) and the least error (mean absolute error = 0.52 MPa) during the testing phase.

Axial compressive behaviour and physical characteristics of high strength self-compacting geopolymer concrete (HSGC) columns exposed to elevated temperature

The growing demand in the utilization of the cement has increased carbon emissions, necessitating the adoption of new technologies that are eco-friendly. Geopolymer concrete (GC) is a viable alternative to traditional cement-based concrete, with superior mechanical properties and sustainable performance. However, research on the elevated temperature exposure of geopolymer paste, mortar, and concrete is limited, particularly with respect to high-strength and self-compacting geopolymer concrete. This study aims to investigate the performance of high-strength self-compacting geopolymer concrete (HSGC) at room temperature and elevated temperatures up to 1029 ℃. The investigation is carried out consisting of two strength grades: normal strength GC and HSGC. Tests are conducted on both HSGC cube and column specimens to examine the residual compressive strength (CS) and axial compression performance of the concrete before and after exposure to elevated temperature. The fresh physical characteristics of HSGC are examined following EFNARC guidelines. Post-fire performance is analysed through physical observation, surface modification, crack pattern, weight loss (WL), and residual CS of HSGC samples. Load-Moment (P-M) interaction curves are developed to predict the ultimate load and ultimate moment capacity of the HSGC columns before and after exposure to elevated temperature. The ductility of heated and unheated HSGC columns is found to be almost same. Microstructural analysis, scanning electron microscopy (SEM), and energy dispersive X-ray spectroscopy (EDS) are performed to examine the internal morphology of the HSGC samples before and after exposure to elevated temperature. Furthermore, a life-cycle assessment (LCA) is conducted to compare the cost of HSGC production with conventional cement-based concrete. The analysis indicates that HSGC emits 6.15% less CO2-e than its counterpart cement-based concrete.

Effects of chloride salt erosion and freeze–thaw cycle on interface shear behavior between ordinary concrete and self-compacting concrete

Self-compacting concrete (SCC) has been widely used for concrete jacketing to strengthen RC structures due to its high workability, fluidity, and segregation resistance. In this study, a test was carried out to study the effects of substrate curing including chloride salt erosion and the freeze–thaw cycle on the interface shear strength between ordinary concrete and the SCC. Eighty-four Z-type specimens were designed and tested through direct shear experiments, and the influence of interface treatment (implanting steel bar and cutting groove), concrete type, and substrate curing on the interface shear strength were analyzed. Four calculation models of interface shear strength based on different codes are compared, the applicability of these models is also analyzed. The results show that: For close the substrate concrete and the SCC strength, the effect of chloride salt erosion and freeze–thaw cycle will cause a slight decrease in the interface shear strength compared with the specimen that substrate is not cured, the influence of chloride ion erosion and freeze–thaw cycle on the degeneration of interface shear strength for specimen with implanting steel bar is not more obviously than specimen with cutting groove. Any single code model can not accurately predict the interface shear strength of all specimens with different interface treatments and substrate curing. The fib model can accurately predict interface shear strength of specimens which the average compressive strength of old and new concrete is less than 40 MPa and the substrate is not cured. Whether the substrate is cured or not, EC2 and CSA A23.3–14 models can quite accurately predict the interface shear strength of specimens with an implanting steel bar, but for specimens with cutting groove, the modified AASHTO model can accurately predict the interface shear strength. Except modified AASHTO model, the factor considering the effect of interface treatment should be taken into account when using fib, EC2 and CSA A23.3–14 models to calculate the interface shear strength.

Deep learning based concrete compressive strength prediction model with hybrid meta-heuristic approach

In concrete design, the compressive strength of the concrete is the critical parameter that defines the quality of concrete. Determination of the compressive strength of concrete by laboratory testing is the conventional way, however, with the help of advancements in technology, the development of an exact predicting technique for the compressive strength of concrete will reduce time as well as cost to a greater extent. There are various types of concrete, as concrete is the most used material and has wide applications in the construction sector and hence huge article is being done to increase the performance of concrete. In this paper, the compressive strength of High-Performance Concrete and Fiber Reinforced High Strength Self-Compacting Concrete is predicted by next three main stages, namely (a) pre-processing, (b) feature extraction, and (c) prediction of concrete compressive strength. Initially, in the data pre-processing phase, duplicate data samples are removed. Subsequently, various features like statistical features, symmetrical uncertainty-based features, higher order statistical features, ReliefF feature, and are derived from the pre-processed data. In the prediction phase of concrete compressive strength, a hybrid algorithm is designed by mingling the concepts of Deep Belief Network and Long Short-Term Memory. Moreover, The Rock Hyraxes Updated Sparrow Algorithm is used to fine-tune the weights of DBN to improve the prediction accuracy of the suggested work. The suggested hybrid optimization method is the hybridization of the standard Sparrow Search Algorithm and Rock Hyraxes Swarm Optimization, respectively. Eventually, the output from the optimized hybrid method will reveal the compression energy of concrete. Ultimately, the efficiency of the suggested method is verified by the comparative estimation. The MAPE of the suggested method is 235.32, which is 30.6%, 21.64%, 20.4%, and 11.4% superior than the CLPSO, ABC, SSA + Hybrid Classifier and RHSO + Hybrid Classifier, respectively.