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Technical Note

An artificial neural network for the prediction of the strength of supplementary cementitious concrete

Lomesh Mahajan, Sariputt Bhagat

Department of Civil Engineering, Dr. Babasaheb Ambedkar Technological University, 402103, India



Supplementary Material; 




ANN Model; 

Prediction Techniques

Supplementary materials (SM) for cement replacement became more feasible in the previous decade due to their pozzolanic strength and durability properties. The strength variation according to the age of the binding material is a critical subject for SM concrete. The time of water curing is critical in order to maintain the pozzolanic reaction in SM concrete, which assists in the development of strength in cementitious properties.In this study, the laboratory results of concrete specimens were assessed for various mix designs, and the obtained corresponding strengths were also predicted with ANN techniques. The difference between experimental and ANN predicted values was marginally low. Thus, the ANN model applicability emphasised the productive use in predicting the strength of supplementary materials like fly ash or any similar pozzalanic mineral admixture. This research proposes an ANN-based technique for predicting the strength of fly ash added SM concrete. Typical experimental data is used to build, train, and test the artificial neural network (ANN) model. With ANN and input parameters, a total of 324 distinct data points for SM concrete were utilised to estimate SM concrete strength. Various combinations of layers, number of neurons, and learning rate were examined during the training phase. When the root mean square error (RMSE) reached or remained below 0.001, the training was stopped, and the findings were verified using a test data set. With respect to the relative error provided for trained model data, the results achieved were typically below 10% for compressive strength and below 5% for split tensile strength. The ANN models predict concrete strength with excellent accuracy, and the findings show that utilising ANNs to predict concrete strength is both practicable and advantageous.

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8/12/2023 Special Issue: Embark on a journey of innovation with the journal of Research on Engineering Structures and Materials as we unveil a compelling opportunity for contributors in our upcoming special issue, "Design, Analysis, and Manufacturing of Composite Vehicle Structures." Led by distinguished Guest Editors Liubov Gavva and Oleg Mitrofanov from Moscow Aviation Institute. For more info see the link.

21/10/2023 Journal Submission System Upgrade Completed: We're delighted to announce that our Journal Submission and Tracking System has undergone a significant upgrade, aimed at enhancing your experience. We apologize for the delay, and any inconvenience it may have caused. Here are the key enhancements from a user perspective:

Improvements are designed to make your interaction with our journal smoother and more efficient. Please take the upgraded system for a spin and share your thoughts with us. If you encounter any issues or have questions, please don't hesitate to reach out to our support team.

27/12/2022 Reviewer AwardsThe winners of 2022 reviewer awards of Research on Engineering Structures and Materials (RESM) are announced. More information can be found at Reviewer Awards section. 

(More details of the news may be given in the News section)

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2022 Reviewer Awards:

Please, visit Reviewer Awards section for the winners of the 2022 RESM reviewer awards.

2022 Best Paper Award:

The paper authored by Nitin Kumar, Michele Barbato, Erika L. Rengifo-López and Fabio Matta entitled as “Capabilities and limitations of existing finite element simplified micro-modeling techniques for unreinforced masonry” is awarded the 

2022 Most Cited Paper Award:

The paper authored by Aykut Elmas, Güliz Akyüz, Ayhan Bergal, Müberra Andaç and Ömer Andaç entitled as “Mathematical modelling of drug release" is awarded the


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