The Protective Structures

The purpose of the research is to predict the compressive and flexural strengths of the concrete mix by using waste marble powder as a partial replacement of cement and sand, based on the experimental data that was acquired from the laboratory tests. In order to accomplish the goal, the models of Support vector machines, Support vector machines with bagging and Stochastic, Linear regression, and Gaussian processes were applied to the experimental data for predicting the compressive and flexural strength of concrete. The effectiveness of models was also evaluated by using statistical criteria. Therefore, it can be inferred that the gaussian process and support vector machine methods can be used to predict the respective outputs, i.e., flexural and compressive strength. The Gaussian process and Support vector machines Stochastic predicts better outcomes for flexural and compressive strength because it has a higher coefficient of correlation (0.8235 and 0.9462), lower mean absolute and root mean squared error values as (2.2808 and 1.8104) and (2.8527 and 2.3430), respectively. Results suggest that all applied techniques are reliable for predicting the compressive and flexural strength of concrete and are able to reduce the experimental work time. In comparison to input factors for this data set, the number of curing days followed by the CA, C, FA, w, and MP is essential in predicting the flexural and compressive strength of a concrete mix for this data set.

https://www.mdpi.com/1996-1944/15/17/5811/pdf?version=1661421721

Machine Learning Techniques for Evaluating Concrete Strength with Waste Marble Powder

Concrete is the most widely used man-made material in the construction of structures, pavements, bridges, dams, and infrastructures. Depending on the type of components and mixture proportions, different behavior can be expected from different types of concretes, which necessitates the study of concrete behavior in designing procedures. The properties of the concrete mixtures and elements can be estimated through expensive and time-taking laboratory-based experiments. Alternatively, these properties can be estimated through predictive models developed using statistical or artificial intelligence (AI) techniques. AI techniques, because of their capabilities in knowledge processing and pattern recognition, are among the leading methods to find solutions for engineering problems. In this paper, the available studies on the applications of AI techniques to model the behavior of concrete elements and estimate the properties of concrete mixtures are reviewed. In addition, the capabilities of various AI techniques in handling different types of data are discussed. This paper also provides recommendations on the selection of the appropriate input variables in developing the predictive models. It is hoped that this paper will provide the interested practicing engineers with the information needed to fully exploit the resources available on the use of AI techniques in the concrete industry. Moreover, this paper will be helpful to the researchers to explore future avenues of research on the applications of AI techniques in the field of concrete mixtures and elements.

Artificial Intelligence to Model the Performance of Concrete Mixtures and Elements: A Review

Abstract To mitigate the environmental issues related to the utilisation of ordinary portland cement (OPC) in concrete mixtures, attempts have been carried out to find alternative binders such as rice husk ash (RHA) as replacements for OPC. This study contributes to moving from the traditional laboratory-based methods for the determination of compressive strength (CS) towards machine learning-based approaches by developing three accurate models (i.e. artificial neural network (ANN), multivariate adaptive regression spline (MARS) and M5P model tree) for the estimation of the CS of concretes containing RHA. For this purpose, the models were developed employing 909 data records collected through technical literature. The results indicate that all three techniques provide reliable estimations of the CS for both training and testing datasets. However, the ANN-based model outperforms the other two models, while the MP5-based model is associated with the least accuracy and the maximum error values among all three techniques. The parametric study revealed that by increasing the contents of cement, coarse aggregate and age, and decreasing the contents of water, fine aggregate, RHA and superplasticizer, the CS of concrete increases while the sensitivity analysis demonstrated that the coarse aggregate content was the most influential parameter affecting the values of CS.

Estimation of the compressive strength of green concretes containing rice husk ash: a comparison of different machine learning approaches

The experimental investigations were carried out on recycled aggregate concrete to study the influence of basal fiber through mechanical and durability tests. The mechanical tests like compressive strength and split tensile strength were conducted on recycled concrete aggregate along with basalt fiber of various proportions. The durability tests like carbonation properties were conducted on recycled concrete aggregate along with basalt fiber. The percentage of recycled aggregate replacement was varied as 0, 50, and 100%, whereas the percentage of basalt fiber was varied as 0, 2, and 4 kg/m3. In hardened concrete, compressive strength, split tensile strength test, and carbonation tests were conducted against 7, 28, and 90 days after casting and their behavior was compared. It is observed that the basalt fiber does not contribute much in case of compressive strength of the concrete. It is observed that the split tensile strength has found to be increased considerably with increasing basalt fiber. The split tensile strength of R50B0 has been found to be increased 14% as compared to conventional SCC, R0B0 concrete, however, the strength of R50B0 concrete was found to be decreased by 22% as compared to R100B0 mix. It is observed that the carbonation depth of concrete was found to be increased with increase of recycled concrete aggregate as well as basalt fiber.

Mechanical and Durability Properties of Recycled Aggregate Self-compacting Concrete Along with Basalt Fibers

The objective of the present work is to evaluate the influence of two different methods of improving the ductility of Reinforced Concrete Frames and their influence on the full range behavior of the frames with M40 grade of concrete. For this purpose one fourth scale reinforced concrete square frames are experimentally tested subjected to static cyclic loading for three cases and monotonic loading for one case. The parameters are varied as method introducing ductility to the frame viz. (i) by using conventional concrete (ii) adding 1% of steel fibres by volume of concrete at hinging zones (iii) using self-compacting concrete with fibres at hinging zones. The energy absorption by ductile and non-ductile frames has been compared. The behavior of frames tested under cyclic loading have revealed that there is a positive trend in improvement of ductility of frames when fibrous concrete is used along with self-compacting concrete.

Energy absorption of fibrous self compacting reinforcedconcrete system

The present study focuses on the application of artificial neural network (ANN) and Multiple linear Regression (MLR) analysis for developing a model to predict the unconfined compressive strength (UCS) and split tensile strength (STS) of the fiber reinforced clay stabilized with grass ash, fly ash and lime. Unconfined compressive strength and Split tensile strength are the nonlinear functions and becomes difficult for developing a predicting model. Artificial neural networks are the efficient tools for predicting models possessing non linearity and are used in the present study along with regression analysis for predicting both UCS and STS. The data required for the model was obtained by systematic experiments performed on only Kaolin clay, clay mixed with varying percentages of fly ash, grass ash, polypropylene fibers and lime as between 10-20%, 1-4%, 0-1.5% and 0-8% respectively. Further, the optimum values of the various stabilizing materials were determined from the experiments. The effect of stabilization is observed by performing compaction tests, split tensile tests and unconfined compression tests. ANN models are trained using the inputs and targets obtained from the experiments. Performance of ANN and Regression analysis is checked with statistical error of correlation coefficient (R) and both the methods predict the UCS and STS values quite well; but it is observed that ANN can predict both the values of UCS as well as STS simultaneously whereas MLR predicts the values separately. It is also observed that only STS values can be predicted efficiently by MLR.

Prediction of UCS and STS of Kaolin clay stabilized with supplementary cementitious material using ANN and MLR

The numerical investigations has been carried out on reinforced concrete structures against blast loading to demonstrate the accuracy and effectiveness of the finite element based numerical models. The size of building was considered 3 ×3 × 3 m and whereas the size of beam and column was 0.3 m, arbitrary. The thickness of roof slab was 120 mm, whereas the reinforced concrete wall on all four sides was 0.2 m. The simulations were carried out through finite element code ABAQUS/CAE. The inelastic behavior of concrete has been incorporated through concrete damage plasticity model and the model includes compressive and tensile behavior. The elastic and plastic behavior of steel reinforcement bar has been incorporated using Johnson-cook model includes the effect of state of stress, temperature and strain rate. The simulations were carried out against varying standoff distance, mass of TNT, locations of blast origin and thickness of roof slab to examine the resistance of building. The response of structural elements were studied in light of deflection, impulsive velocity, von-Mises stresses, compression and tension damage of concrete. The results indicate that the standoff distance has great influence on the survivability of reinforced concrete slab and wall.

The Performance of Monolithic Reinforced Concrete Structure Includes Slab, Beam and Column against Blast Load

Experiments on ductile and non-ductile reinforced concrete frames under static and cyclic loading

An explosion on the elevated structures caused by terrorist activities or manmade events can induce significant deformations in the Civil Engineering structures. Therefore, it is necessary to review the response of the structural behavior such as reinforced concrete slab, reinforced concrete beams, and columns. On the basis of this objective, a detailed literature review is conducted to understand the scope for protecting such structures and the structural behavior under blast loading. Based on the detailed literature survey, the investigations about the behavior of conventional reinforced concrete columns and slab initiated in 2005 however, the behavior of reinforced concrete beam was focused since the year 2010. Also, the literature reveals that the investigations on structural elements using analytical techniques are limited in comparison to experiments and simulations. In addition to that, the response of the structural elements was predicted and the trend was calibrated and fitted logarithmically with the experimental results. The predicted spall diameter in the reinforced concrete slab is 0.95 m corresponding charge weight of 100 kg however the influence of spalling was found to be negligible after the 100 kg of charge weight. The predicted spall length in the reinforced concrete beam is 1.6 m corresponding charge weight of 100 kg and the effect may be negligible after 100 kg of charge weight. The predicted deflection in the reinforced concrete columns is 30 mm corresponding to a peak reflected impulse of 1000 MPa-ms, whereas the deflection was found to be negligible after the 1000 MPa-ms of peak reflected impulse.

/pdf/a-review-on-the-performance-of-reinforced-concrete-nsjvaavrdj.pdf

S. Rupali

Papers

Energy absorption of fibrous self compacting reinforcedconcrete system

Prediction of UCS and STS of Kaolin clay stabilized with supplementary cementitious material using ANN and MLR

The Performance of Monolithic Reinforced Concrete Structure Includes Slab, Beam and Column against Blast Load

Experiments on ductile and non-ductile reinforced concrete frames under static and cyclic loading

A review on the performance of reinforced concrete structures under blast loading