Prediction of Compressive Strength of Concrete by Data-Driven Models

2015 ◽  
Vol 5 (2) ◽  
pp. 16-23 ◽  
Author(s):  
Faezehossadat Khademi ◽  
◽  
Mahmoud Akbari ◽  
Sayed Mohammadmehdi Jamal ◽  
◽  
...  
Author(s):  
Funso Falade ◽  
Taim Iqbal

Compressive strength of concrete, renowned as one of the most substantial mechanical properties of concrete and key factors for the quality assurance of concrete. In the present study, two different data-driven models, i.e., Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR) were used to predict the 28 days compressive strength of recycled aggregate concrete (RAC). 16 different input parameters, including both dimensional and non-dimensional parameters, were used for predicting the 28 days compressive strength of concrete. The present study established that estimation of 28 days compressive strength of recycled aggregate concrete was performed better by ANFIS in comparison to MLR. Besides, the performance of data-driven models with and without the non-dimensional parameters is explored. It was observed that the data-driven models show better accuracy when the non-dimensional parameters were used as additional input parameters. Furthermore, the effect of each non-dimensional parameter on the performance of each data-driven model is investigated and 28 days compressive strength of concrete is examined.


2018 ◽  
Vol 9 (2) ◽  
pp. 67-73
Author(s):  
M Zainul Arifin

This research was conducted to determine the value of the highest compressive strength from the ratio of normal concrete to normal concrete plus additive types of Sika Cim with a composition variation of 0.25%, 0.50%, 0.75%, 1.00%, 1.25%, 1 , 50% and 1.75% of the weight of cement besides that in this study also aims to find the highest tensile strength from the ratio of normal concrete to normal concrete in the mixture of sika cim composition at the highest compressive strength above and after that added fiber wire with a size diameter of 1 mm in length 100 mm with a ratio of 1% of material weight. The concrete mix plan was calculated using the ASTM method, the matrial composition of the normal concrete mixture as follows, 314 kg / m3 cement, 789 kg / m3 sand, 1125 kg / m3 gravel and 189 liters / m3 of water at 10 cm slump, then normal concrete added variations of the composition of sika cim 0.25%, 0.50%, 0.75%, 1.00%, 1.25%, 1.5%, 1.75% by weight of cement and fiber, the tests carried out were compressive strength of concrete and tensile strength of concrete, normal maintenance is soaked in fresh water for 28 days at 30oC. From the test results it was found that the normal concrete compressive strength at the age of 28 days was fc1 30 Mpa, the variation in the addition of the sika cim additive type mineral was achieved in composition 0.75% of the cement weight of fc1 40.2 Mpa 30C. Besides that the tensile strength test results were 28 days old with the addition of 1% fiber wire mineral to the weight of the material at a curing temperature of 30oC of 7.5%.


Author(s):  
Oldřich Sucharda ◽  
David Mikolášek ◽  
Jiří Brožovský

Abstract This paper deals with the determination of compressive strength of concrete. Cubes, cylinders and re-used test beams were tested. The concrete beams were first subjected to three-point or fourpoint bending tests and then used for determination of the compressive strength of concrete. Some concrete beams were reinforced, while others had no reinforcement. Accuracy of the experiments and calculations was verified in a non-linear analysis.


2021 ◽  
Vol 13 (4) ◽  
pp. 2073 ◽  
Author(s):  
Hossein Mohammadhosseini ◽  
Rayed Alyousef ◽  
Mahmood Md. Tahir

Recycling of waste plastics is an essential phase towards cleaner production and circular economy. Plastics in different forms, which are non-biodegradable polymers, have become an indispensable ingredient of human life. The rapid growth of the world population has led to increased demand for commodity plastics such as food packaging. Therefore, to avert environment pollution with plastic wastes, sufficient management to recycle this waste is vital. In this study, experimental investigations and statistical analysis were conducted to assess the feasibility of polypropylene type of waste plastic food tray (WPFT) as fibrous materials on the mechanical and impact resistance of concrete composites. The WPFT fibres with a length of 20 mm were used at dosages of 0–1% in two groups of concrete with 100% ordinary Portland cement (OPC) and 30% palm oil fuel ash (POFA) as partial cement replacement. The results revealed that WPFT fibres had an adverse effect on the workability and compressive strength of concrete mixes. Despite a slight reduction in compressive strength of concrete mixtures, tensile and flexural strengths significantly enhanced up to 25% with the addition of WPFT fibres. The impact resistance and energy absorption values of concrete specimens reinforced with 1% WPFT fibres were found to be about 7.5 times higher than those of plain concrete mix. The utilisation of waste plastic food trays in the production of concrete makes it low-cost and aids in decreasing waste discarding harms. The development of new construction materials using WPFT is significant to the environment and construction industry.


2021 ◽  
Vol 1107 (1) ◽  
pp. 012171
Author(s):  
A. C. Ekeleme ◽  
E. I. Ugwu ◽  
C.E. Njoku ◽  
E.C. Amanamba ◽  
E. E. Arinze ◽  
...  

2021 ◽  
Vol 1098 (6) ◽  
pp. 062013
Author(s):  
V L Roding ◽  
A I Candra ◽  
D A Karisma ◽  
T A Laksono ◽  
A Ansori ◽  
...  

2021 ◽  
Vol 11 (2) ◽  
pp. 485
Author(s):  
Amirreza Kandiri ◽  
Farid Sartipi ◽  
Mahdi Kioumarsi

Using recycled aggregate in concrete is one of the best ways to reduce construction pollution and prevent the exploitation of natural resources to provide the needed aggregate. However, recycled aggregates affect the mechanical properties of concrete, but the existing information on the subject is less than what the industry needs. Compressive strength, on the other hand, is the most important mechanical property of concrete. Therefore, having predictive models to provide the required information can be helpful to convince the industry to increase the use of recycled aggregate in concrete. In this research, three different optimization algorithms including genetic algorithm (GA), salp swarm algorithm (SSA), and grasshopper optimization algorithm (GOA) are employed to be hybridized with artificial neural network (ANN) separately to predict the compressive strength of concrete containing recycled aggregate, and a M5P tree model is used to test the efficiency of the ANNs. The results of this study show the superior efficiency of the modified ANN with SSA when compared to other models. However, the statistical indicators of the hybrid ANNs with SSA, GA, and GOA are so close to each other.


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