What is it about?

Concrete is one of the most widely used construction materials, and in recent days, thrust is being given to produce concrete in a sustainable manner. Concrete strength is one of the important parameters that decides the durability of structures, and it is being evaluated mostly by destructive testing in the laboratory which is not only costly but also time consuming. Numerous methods were developed by researchers across the globe to predict concrete strength using limited data; in this work, we had made an attempt to predict sustainable concrete strength using autoregressive integrated moving average (ARIMA) model. The ARIMA model (with expert modeler) coefficients are described as ARIMA (0,1,14). The study uses ARIMA model with expert modeler to choose the most significant variables that Box and Jenkins used to build a prediction model in SPSS that can estimate CS, mass, NDT of concrete results. The results reveal that the best ARIMA model can be formulized as ARIMA (0,1,14). This kind of analysis and implications of ARIMA models and fitting procedures are useful in forecasting results of compressive strength, mass, and NDT results.

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Why is it important?

Concrete strength is one of the important parameters that decides the durability of structures, and it is being evaluated mostly by destructive testing in the laboratory which is not only costly but also time consuming. Numerous methods were developed by researchers across the globe to predict concrete strength using limited data; in this work, we had made an attempt to predict sustainable concrete strength using autoregressive integrated moving average (ARIMA) model.

Perspectives

Writing this article was a great pleasure as it has co-authors with whom I have had long standing collaborations. This article also lead to rare disease groups contacting me and ultimately to a greater involvement in rare disease research.

Prof Gobinath R
S R Engineering College

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This page is a summary of: Sustainable Concrete Strength Prediction Using ARIMA Model, January 2022, Springer Science + Business Media,
DOI: 10.1007/978-981-19-2572-6_1.
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