Drying shrinkage and crack width prediction using machine learning in mortars containing different types of industrial by-product fine aggregates

Aug 5, 2026

Drying shrinkage and cracking of concrete mortar are driven by water loss during hardening and by the aggregate type used in the mixture. This study estimates the evolution of drying shrinkage and crack width over time after substituting fine aggregates with industrial by-product or waste-derived aggregates at different replacement ratios. Experimental results for substituted fine aggregate concrete mortars were transformed into a dataset, and supervised machine learning models were trained using 60-day drying shrinkage and crack width measurements for mortars with bottom ash, granulated blast furnace slag, fly ash, and crushed tiles. Hyperparameters were optimized to improve prediction capability, achieving accuracy above 99.6%.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages24

Reading Time1 h 0 min

AccessFree to download

Introduction

Concrete durability and role of aggregates

Prior experimental approaches and AI-based alternatives

Journal of Building Engineering 97 (2024) 110737 Contents lists available at ScienceDirect Journal of Building Engineering journal [homepage:](homepage: www.elsevier.com/locate/jobe)[ www.elsevier.com/locate/jobe](homepage: www.elsevier.com/locate/jobe) | Drying shrinkage and crack width prediction using machine learning in mortars containing different types of industrial by-product fine aggregates | | | | | --- | --- | --- | --- | | Ayla Ocak a , Gebrail Bekda a, * , Ümit Iıkda˘g b, Sinan Melih Nigdeli a , Turhan Bilir a
a Department of Civil Engineering, Istanbul University-Cerrahpaa, Istanbul, 34320, Turkey b Department of Architecture, Mimar Sinan Fine Arts University, Istanbul, 34427, Turkey | | | | | A R T I C L E I N F O | | A B S T R A C T | | | Keywords:
Drying shrinkage
Crack
Machine learning Hyperparameter optimization | | Concrete is a material that loses water and changes shape while hardening due to its structure. Over time, this water loss results in some shrinkage of the hardened concrete, referred to as drying shrinkage. In addition, water loss of concrete also causes the formation of various cracks. The aggregate used in concrete plays an important role in the shrinkage and cracking of concrete. The focus of this study is to accurately estimate the amount of crack width and drying shrinkage over time after the substitution of fine aggregates with other types of aggregates (consisting of various industrial by-products or wastes at different percentages) in the concrete mortar. For this purpose, various experimental results of the ‘substituted fine aggregate concrete mortars’ were converted into a data set. Following this a model was developed to predict the drying shrinkage and crack width of concrete mortars. The machine learning model was trained with the measurement results of 60-day drying shrinkage and crack widths of concrete mortars with different proportions of bottom ash (BA), granulated blast furnace slag (GBFS), fly ash (FA), and crushed tiles (CT). To enhance the detection/prediction capability of the model, the model hyperparameters were optimized. It is observed that the developed model was able to detect the drying shrinkage and crack width with an accuracy exceeding 99.6 %. In addition, the physical properties such as grain shape (angular or round) of components like fine aggregates may be effective for improved performance of the machine learning models in predictions of the drying shrinkage values or drying shrinkage cracking widths. | | 1. Introduction In reinforced concrete structures, the durability of concrete has major impacts on strength and stability of the structure, in protection from corrosion for steel reinforcements, and in service life and overall performance of the structure. One of the key components that has a big impact on the durability of the concrete is the type of aggregate used in the concrete mortar. Along with the developments in the production industries, waste production has also increased to elevated levels, and researches have focused on consumption of the industrial wastes through recycling them into useful raw materials and products for other industries. In this context, in the construction industry, the use of industrial wastes as aggregates in concrete mortars has increased significantly. Drying * Corresponding author. E-mail addresses: [aylaocak@outlook.com](aylaocak@outlook.com) (A. Ocak), [bekdas@iuc.edu.tr](bekdas@iuc.edu.tr) (G. Bekda), umit.isikdag@msgsu.edu.tr (Ü. Iıkda˘g), melihnig@iuc.edu.tr (S.M. Nigdeli), [tbilir@iuc.edu.tr](tbilir@iuc.edu.tr) (T. Bilir). [https://doi.org/10.1016/j.jobe.2024.110737](https://doi.org/10.1016/j.jobe.2024.110737) Received 13 May 2024; Received in revised form 26 August 2024; Accepted 11 September 2024 Available online 13 September 2024 2352-7102/© 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. shrink

FAQs

What does drying shrinkage and crack formation in concrete mortar depend on?

Drying shrinkage results from water loss during hardening, and crack formation is also linked to water loss. The aggregate type and its contents strongly influence both phenomena.

How were the prediction models developed in this study?

Experimental results from substituted fine aggregate concrete mortars were converted into a dataset. Supervised machine learning models were trained using 60-day drying shrinkage and crack width measurements for mortars with several by-product-derived fine aggregates.

Which fine aggregates and replacement ratios were considered?

The dataset covers mortars where fine aggregates were replaced using bottom ash (BA), granulated blast furnace slag (GBFS), fly ash (FA), and crushed tiles (CT) at different replacement ratios.

Aug 5, 2026

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