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Concrete is a fundamental building material, and understanding its strengths is crucial for construction projects. The relationship between its tensile and compressive strengths is intricate, showing that while these strengths are related, they do not increase at the same rate. Tensile strength's growth is slower and is affected by various factors such as the methods used for testing, the size and shape of the specimen, the texture of the aggregate used, and the moisture content of the...
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Considering the tensile strength of concrete involves recognizing that the theoretical strength of cement paste can be up to a thousand times higher than what is observed in practical applications. This significant discrepancy is largely attributed to the presence of microscopic cracks within the concrete. These cracks tend to amplify stress at their tips when a load is applied, a phenomenon explained by Griffith's theory of brittle fracture.
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Abrasion resistance is an essential characteristic of concrete that determines its durability and longevity under various wear conditions. Concrete surfaces are vulnerable to different types of abrasion. For instance, surfaces may wear down due to the constant movement of vehicles or be eroded by solids carried in water, as seen in concrete canal linings. Specific tests are conducted to measure the abrasion resistance of concrete.
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Fatigue, in the context of materials science and engineering, refers to the weakening or failure of a material caused by repeatedly applied loads, even if these loads are below the strength limit of the material. Fatigue strength in concrete is a critical property that influences its durability and longevity. Concrete can fail in two ways due to fatigue. Static fatigue or creep rupture occurs under a constant load or one that increases slowly. The other failure mode is due to cyclical or...
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Compressive strength of nano concrete materials under elevated temperatures using machine learning.

Abdullah M Zeyad1, Alaa A Mahmoud2, Alaa A El-Sayed2

  • 1Civil and Architectural Engineering Department, College of Engineering and Computer Sciences, Jazan University, Jazan 45142, Saudi Arabia., Jazan University, Jazan, Kingdom of Saudi Arabia. azmohsen@jazanu.edu.sa.

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Summary

This study developed Artificial Intelligence (AI) models to predict concrete strength after heat exposure. The Water Cycle Algorithm (WCA) provided the most accurate predictions and derived practical equations for residual compressive strength (RCS).

Keywords:
Artificial neural networksElevated temperatureFuzzy logic modelsGenetic algorithmsMachine learningNano additivesWater Cycle Algorithm

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Area of Science:

  • Materials Science
  • Civil Engineering
  • Artificial Intelligence

Background:

  • Assessing the residual compressive strength (RCS) of concrete after high-temperature exposure is crucial for structural integrity.
  • Nano additives like Nanocarbon tubes (NCTs) and Nano alumina (NAl) can influence concrete's thermal performance.
  • Developing accurate predictive models is essential for understanding material behavior under extreme conditions.

Purpose of the Study:

  • To develop and compare Artificial Intelligence (AI) based machine learning models for estimating the Residual Compressive Strength (RCS) of concrete.
  • To investigate the efficacy of meta-heuristic algorithms (Water Cycle Algorithm - WCA, Genetic Algorithm - GA) and classical AI models (Artificial Neural Networks - ANNs, Fuzzy Logic Models - FLM) in predicting RCS.
  • To derive accurate predictive equations for RCS using AI models and analyze the influence of input parameters.

Main Methods:

  • Developed four AI-based machine learning models: WCA, GA, ANNs, and FLM, alongside Multiple Linear Regression (MLR).
  • Utilized 156 experimental post-heating data points with inputs including temperature, heat exposure duration, nanomaterial type, and replacement proportion.
  • Performed sensitivity analysis using neural network weights and SHAP to determine input variable impact.

Main Results:

  • ANN and FLM showed potential for RCS prediction, but lacked practical equations.
  • The WCA model demonstrated superior accuracy across all performance indicators compared to other models.
  • WCA and GA yielded highly accurate RCS prediction equations with low Mean Absolute Errors (MAEs) for training, validation, and testing datasets.
  • Sensitivity analysis indicated temperature and exposure time significantly impact RCS, followed by NAl and NCTs.

Conclusions:

  • Meta-heuristic AI models, particularly WCA, are highly effective for predicting the residual compressive strength of nano-modified concrete after heat exposure.
  • The developed WCA and GA models provide accurate and practical equations for RCS estimation.
  • Temperature and exposure duration are the most critical factors influencing concrete's residual strength, with nanomaterials playing a secondary role.