List of Articles  

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  1 - Evaluation and Prediction of W/C Ratio vs. Compressive Concrete Strength Using A.I and M.L Based on Random Forest Algorithm Approach
  R. Jamalpour
  Concrete, an artificial stone composed of cement, aggregate, water, and additives, is extensively utilized in contemporary civil projects. A pivotal characteristic of concrete is its capacity to efficiently serve various purposes and structural requirements. Cement, wat More

  Concrete, an artificial stone composed of cement, aggregate, water, and additives, is extensively utilized in contemporary civil projects. A pivotal characteristic of concrete is its capacity to efficiently serve various purposes and structural requirements. Cement, water, aggregate, and additives are pivotal parameters wherein even minor alterations can significantly impact concrete strength. Among these parameters, the Water/Cement (W/C) ratio holds particular significance due to its inverse correlation with strength. Traditionally, predicting concrete strength solely based on the water-to-cement ratio has been challenging. However, with advancements in AI and machine learning techniques coupled with ample data availability, accurate strength prediction is achievable. This paper presents an analysis of a diverse dataset comprising various concrete tests utilizing machine learning methodologies, followed by a comparative examination of the outcomes. Furthermore, this study scrutinizes a renowned dataset encompassing 1030 experiments, featuring diverse combinations of cement, water, aggregate, etc., employing artificial intelligence and machine learning techniques. Model accuracy and result fidelity are evaluated through rigorous sampling methodologies. Initially, the dataset is subjected to analysis utilizing the linear regression algorithm, followed by validation employing the random forest algorithm. The random forest algorithm is employed to predict the water-to-cement ratio and corresponding compressive strength for concrete with a density of 300 kg/m3. Notably, the obtained results exhibit a high level of concordance with experimental and laboratory findings from prior studies. Hence, the efficacy of the random forest algorithm in concrete strength prediction is established, offering promising prospects for future applications in this domain. Manuscript profile
• Open Access Article
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  2 - Shape Memory Alloy (SMA) as Smart Materials Application in Structural Engineering in the Last Decade
  R. Jamalpour
  The shape memory alloys (SMAs) is a specific property some materials have to restore their original shape. This strange behavior has caused these materials to be classified as smart materials. Due to the capabilities of this material, it has been used in all industries, More

  The shape memory alloys (SMAs) is a specific property some materials have to restore their original shape. This strange behavior has caused these materials to be classified as smart materials. Due to the capabilities of this material, it has been used in all industries, and in the last decade, its use has developed tremendously. In this article, while dealing with their general properties and production method, their applications especially in structural and earthquake engineering have been reviewed and investigated. For this reason, some of the works and studies done by structural engineering researchers in the recent period (from 2014 to 2024) in the field of structural engineering and with the approach of evaluating connections equipped by shape memory alloys have been mentioned. Finally, while examining the details of a Practical study in the field of steel column connection to the foundation, which was done with and without using of shape memory alloys, the advantages of using shape memory alloys in connections are summarized in the results. Manuscript profile