Developing Soft-Computing Models for Simulating the Maximum Moment of Circular Reinforced Concrete Columns

There has been a significant rise in research using soft-computing techniques to predict critical structural engineering parameters. A variety of models have been designed and implemented to predict crucial elements such as the load-bearing capacity and the mode of failure in reinforced concrete columns. These advancements have made significant contributions to the field of structural engineering, aiding in more accurate and reliable design processes. Despite this progress, a noticeable gap remains in literature. There's a notable lack of comprehensive studies that evaluate and compare the capabilities of various machine learning models in predicting the maximum moment capacity of circular reinforced concrete columns. The present study addresses a gap in the literature by examining and comparing the capabilities of various machine learning models in predicting the ultimate moment capacity of spiral reinforced concrete columns. The main models explored include AdaBoost, Gradient Boosting, and Extreme Gradient Boosting. The R2 value for Histogram-Based Gradient Boosting, Random Forest, and Extremely Randomized Trees models demonstrated high accuracy for testing data at 0.95, 0.96, and 0.95, respectively, indicating their robust performance. Furthermore, the Mean Absolute Error of Gradient Boosting and Extremely Randomized Trees on testing data was the lowest at 36.81 and 35.88 respectively, indicating their precision. This comparative analysis presents a benchmark for understanding the strengths and limitations of each method. These machine learning models have shown the potential to significantly outperform empirical formulations currently used in practice, offering a pathway to more reliable predictions of the ultimate moment capacity of spiral RC columns

Applications of Nearest Neighbor Search Algorithm Toward Efficient Rubber-Based Solid Waste Management in Concrete

Indeed, natural processes of discarding rubber waste have many disadvantages for the environment. As a result, multiple researchers suggested addressing this problem by recycling rubber as an aggregate in concrete mixtures. Previously, numerous studies have been undertaken experimentally to investigate the properties of rubberized concrete. Furthermore, investigations were carried out to develop estimating techniques to precisely specify the generated concrete's characteristics, making its use in real-life applications easier. However, there is still a gap in the conducted studies on the performance of the k-nearest neighbor algorithm. Hence, this research explores the accuracy of using the k-nearest neighbor's algorithm in predicting the compressive and tensile strength and the modulus of elasticity of rubberized concrete. It will be done by developing an optimized machine learning model using the aforementioned method and then benchmarking its results to the outcomes of multiple linear regression and artificial neural networks. The study's findings have shown that the k-nearest neighbor's algorithm provides significantly higher accuracy than other methods. This kind of study needs to be discussed in the literature so that people can better deal with rubber waste in concrete. Doi: 10.28991/CEJ-2022-08-04-06 Full Text: PD

Parametric Assessment of Concrete Constituent Materials Using Machine Learning Techniques

Nowadays, technology has advanced, particularly in machine learning which is vital for minimizing the amount of human work required. Using machine learning approaches to estimate concrete properties has unquestionably triggered the interest of many researchers across the globe. Currently, an assessment method is widely adopted to calculate the impact of each input parameter on the output of a machine learning model. This paper evaluates the capability of various machine learning methodologies in conducting parametric assessments to understand the influence of each concrete constituent material on its compressive strength. It is accomplished by conducting a partial dependence analysis to quantify the effect of input features on the prediction results. As a part of the study, the effects of machine learning method selection for such analysis are also investigated by employing a concrete compressive strength algorithm developed using a decision tree, random forest, adaptive boosting, stochastic gradient boosting, and extreme gradient boosting. Additionally, the significance of the input features to the accuracy of the constructed estimation models is ranked through drop-out loss and MSE reduction. This investigation shows that the machine learning techniques could accurately predict the concrete's compressive strength with very high performance. Further, most analyzed algorithms yielded similar estimations regarding the strength of concrete constituent materials. In general, the study's results have shown that the drop-out loss and MSE reduction outputs were misleading, whereas the partial dependence plots provide a clear idea about the influence of the value of each feature on the prediction outcomes

A Review of MRI Acute Ischemic Stroke Lesion Segmentation

Immediate treatment of a stroke can minimize long-term effects and even help reduce death risk. In the ischemic stroke cases, there are two zones of injury which are ischemic core and ischemic penumbra zone. The ischemic penumbra indicates the part that is located around the infarct core that is at risk of developing a brain infarction. Recently, various segmentation methods of infarct lesion from the MRI input images were developed and these methods gave a high accuracy in the extraction and detection of the infarct core. However, only some limited works have been reported to isolate the penumbra tissues and infarct core separately. The challenges exist in ischemic core identification are traditional approach prone to error, time-consuming and tedious for medical expert which could delay the treatment. In this paper, we study and analyse the segmentation algorithms for brain MRI ischemic of different categories. The focus of the review is mainly on the segmentation algorithms of infarct core with penumbra and infarct core only. We highlight the advantages and limitations alongside the discussion of the capabilities of these segmentation algorithms and its key challenges. The paper also devised a generic structure for automated stroke lesion segmentation. The performance of these algorithms was investigated by comparing different parameters of the surveyed algorithms. In addition, a new structure of the segmentation process for segmentation of penumbra is proposed by considering the challenges remains. The best accuracy for segmentation of infarct core and penumbra tissues is 82.1% whereas 99.1% for segmentation infarct core only. Meanwhile, the shortest average computational time recorded was 3.42 seconds for segmenting 10 slices of MR images. This paper presents an inclusive analysis of the discussed papers based on different categories of the segmentation algorithm. The proposed structure is important to enable a more robust and accurate assessment in clinical practice. This could be an opportunity for the medical and engineering sector to work together in designing a complete end-to-end automatic framework in detecting stroke lesion and penumbra

Performance of CFRP retrofitted two way RC slabs with modified epoxy

&nbsp;The research is focused in improving the performance of two-way reinforced concrete slabs retrofitted with CFRP using modified epoxy. The work concluded the advantages of the new epoxy over the conventional epoxy, the structural behaviour of two way reinforced concrete slabs strengthened with CFRP was improved.<br /

A Review on Strength and Durability Properties of Wooden Ash Based Concrete

The partial replacement of cement in concrete with other building materials has come to light because of research on industrial waste and sustainable building practices. Concrete is made more affordable by using such components, and it also helps to ease disposal worries. Ash made by burning wood and other wood products is one example of such a substance. Many researchers focused on the utilization of wooden ash (WA) as a construction material. However, information is scattered, and no one can easily judge the impact of WA on concrete properties which restrict its use. Therefore, a details review is required which collect the past and current progress on WA as a construction material. relevant information. This review aims to collect all the relevant information including the general back of WA, physical and chemical aspects of WA, the impact of WA on concrete fresh properties, strength properties, and durability aspects in addition to microstructure analysis. The results indicate the WA decreased the slump and increased the setting time. Strength and durability properties improved with the substitution of WA due to pozzolanic reaction and micro-filling effects. However, the optimum dose is important. Different research recommends different optimum doses depending on source mix design etc. However, the majority of researcher suggests a 10% optimum substitution of WA. The review also concludes that, although WA has the potential to be used as a concrete ingredient but less researchers focused on WA as compared to other waste materials such as fly ash and silica fume etc

Basalt Fibers Reinforced Concrete: Strength and Failure Modes

The low tensile capacity of concrete often results in brittle failure without any warning. One way to cope with this issue is to add fibers and essentially improve the tensile strength (TS) behavior of concrete and offset its undesirable brittle failure. In recent investigations, basalt fibers (BFs), as compared to a variety of other kinds of fiber, have attracted the attention of researchers. In that respect, BFs exhibit several benefits, such as excellent elastic properties, great strength, high elastic modulus, higher thermal stability, and decent chemical stability. Although many researchers have reported that BFs can be embedded in concrete to improve the tensile capacity, a more profound understanding of its contribution is still needed. However, the information is scattered and it is difficult for the reader to identify the benefits of BFs. Therefore, a detailed assessment is essential to summarize all relevant information and provide an easy path for the reader. This review (part Ⅰ) summarizes all the relevant information, including flow properties, strength properties, and failure modes. Results reveal that BFs can greatly enhance the strength properties and change the brittle nature of concrete to one of ductility. However, it unfavorably impacts the flowability of concrete. Furthermore, the optimal proportion is shown to be important as a higher dose can adversely affect the strength of concrete, due to a deficiency of flowability. The typical range of the ideal incorporation of BFs varies from 0.5 to 1.5%. Finally, the review also indicates the research gap for future research studies that must be cautiously explored before being used in the real world

Feasibility Study on Concrete Made with Substitution of Quarry Dust: A Review

Concrete mechanical properties could be improved through adding different materials at the mixing stage. Quarry dust (QD) is the waste produced by manufactured sand machines and comprise approximately 30&ndash;40% of the total quantity of QD generated. When it dries, it transforms into a fine dust that poses a tremendous hazard to the environment by contaminating the soil and water and seriously endangering human health. QD utilization in concrete is one of the best options. Though a lot of scholars focus on imitation of QD in concrete, knowledge is scattered, and a detailed review is required. This review collects the information regarding QD-based concrete, including fresh properties, strength, durability, and microstructure analysis. The results indicate that QD is suitable for concrete to a certain extent, but higher percentages adversely affect properties of concrete due to absence of fluidity. The review also indicates that up to 40&ndash;50% substitution of QD as a fine aggregate can be utilized in concrete with no harmful effects on strength and durability. Furthermore, although QD possesses cementitious properties and can be used as cement substitute to some extent, less research has explored this area

Feasibility Study on Concrete Made with Substitution of Quarry Dust: A Review

Concrete mechanical properties could be improved through adding different materials at the mixing stage. Quarry dust (QD) is the waste produced by manufactured sand machines and comprise approximately 30–40% of the total quantity of QD generated. When it dries, it transforms into a fine dust that poses a tremendous hazard to the environment by contaminating the soil and water and seriously endangering human health. QD utilization in concrete is one of the best options. Though a lot of scholars focus on imitation of QD in concrete, knowledge is scattered, and a detailed review is required. This review collects the information regarding QD-based concrete, including fresh properties, strength, durability, and microstructure analysis. The results indicate that QD is suitable for concrete to a certain extent, but higher percentages adversely affect properties of concrete due to absence of fluidity. The review also indicates that up to 40–50% substitution of QD as a fine aggregate can be utilized in concrete with no harmful effects on strength and durability. Furthermore, although QD possesses cementitious properties and can be used as cement substitute to some extent, less research has explored this area

A Review of MRI Acute Ischemic Stroke Lesion Segmentation

Immediate treatment of a stroke can minimize long-term effects and even help reduce death risk. In the ischemic stroke cases, there are two zones of injury which are ischemic core and ischemic penumbra zone. The ischemic penumbra indicates the part that is located around the infarct core that is at risk of developing a brain infarction. Recently, various segmentation methods of infarct lesion from the MRI input images were developed and these methods gave a high accuracy in the extraction and detection of the infarct core. However, only some limited works have been reported to isolate the penumbra tissues and infarct core separately. The challenges exist in ischemic core identification are traditional approach prone to error, time-consuming and tedious for medical expert which could delay the treatment. In this paper, we study and analyse the segmentation algorithms for brain MRI ischemic of different categories. The focus of the review is mainly on the segmentation algorithms of infarct core with penumbra and infarct core only. We highlight the advantages and limitations alongside the discussion of the capabilities of these segmentation algorithms and its key challenges. The paper also devised a generic structure for automated stroke lesion segmentation. The performance of these algorithms was investigated by comparing different parameters of the surveyed algorithms. In addition, a new structure of the segmentation process for segmentation of penumbra is proposed by considering the challenges remains. The best accuracy for segmentation of infarct core and penumbra tissues is 82.1% whereas 99.1% for segmentation infarct core only. Meanwhile, the shortest average computational time recorded was 3.42 seconds for segmenting 10 slices of MR images. This paper presents an inclusive analysis of the discussed papers based on different categories of the segmentation algorithm. The proposed structure is important to enable a more robust and accurate assessment in clinical practice. This could be an opportunity for the medical and engineering sector to work together in designing a complete end-to-end automatic framework in detecting stroke lesion and penumbra