scholarly journals Computation of Compressive Strength of GGBS Mixed Concrete using Machine Learning

Author(s):  
Swati ◽  
◽  
Jitendra Khatti ◽  
Kamaldeep Singh Grover ◽  
◽  
...  

Concrete is a composite material formed by cement, water, and aggregate. Concrete is an important material for any Civil Engineering project. Several concretes are produced as per the functional requirements using waste materials or by-products. Many researchers reported that these waste materials or by-products enhance the concrete properties, but the laboratory procedures for determining the concrete properties are time-consuming. Therefore, numerous researchers used statistical and artificial intelligence methods for predicting concrete properties. In the present research work, the compressive strength of GGBS mixed concrete is computed using AI technologies, namely Regression Analysis (RA), Support Vector Machine (SVM), Decision Tree (DT), and Artificial Neural Networks (ANNs). The cement content (CC), C/F ratio, w/c ratio, GGBS (in Kg & %), admixture, and age (days) are selected as input parameters to construct the RA, SVM, DT, ANNs models for computing the compressive strength of GGBS mixed concrete. The CS_MLR, Link_CS_SVM, 20LF_CS_DT, and GDM_CS_ANN models are identified as the best architectural AI models based on the performance of AI models. The performance of the best architectural AI models is compared to determine the optimum performance model. The correlation coefficient is computed for input and output variables. The compressive strength of GGBS mixed concrete is highly influenced by age (curing days). Comparing the performance of optimum performance AI models and models available in the literature study shows that the optimum performance AI model outperformed the published models.

2014 ◽  
Vol 970 ◽  
pp. 147-152 ◽  
Author(s):  
Willie Wei Shung Chai ◽  
Delsye Teo Ching Lee ◽  
Chee Khoon Ng

Recycling and reusing waste materials as aggregate replacement play an important role in solving issues associated with environmental problems and depletion of non-renewable resources. The use of these waste materials as aggregate is highly desirable as it can serve to sanitise the environment and create cheaper, renewable aggregates which will provide a double advantage as cost effective construction material and waste disposal at the same time. Hence, there is growing interest in this research area to promote safe and economical use of waste material as aggregate alternative in concrete. In Malaysia, where oil palm shell (OPS) is generated in abundance from the oil palm industry, reusing OPS as concrete aggregate replacement has been widely studied. Results from previous studies have shown that OPS concrete can be used in practical application as structural lightweight concrete. However, the properties of OPS can be further improved to achieve better performance of the resulting concrete. Polyvinyl alcohol (PVA) is a water-soluble synthetic polymer which is extensively used in all kinds of industries, such as papermaking, adhesive for plywood, printing and even in the construction industry as internal wall coating, plasterwork and joint sealing. It has been found that PVA has the potential to improve the quality of the OPS aggregates and hence enhance the resulting concrete properties. In this paper, an experimental program on concrete produced from PVA coated OPS aggregates is presented. The PVA treated OPS concrete was tested for slump, air-dry density, compressive strength, and water absorption. It was found that PVA treated OPS concrete had significant improvement in its compressive strength as compared to raw OPS concrete. It was determined that PVA treated OPS concrete can achieve 28-day compressive strength of up to 33.53 MPa. Moreover, it was also determined that there was a decrease of 0.67% in the water absorption of PVA treated OPS concrete as compared to the raw OPS concrete. In general, the investigation results showed that PVA can be used to improve the OPS concrete properties for the production of structural lightweight concrete.


2018 ◽  
Vol 7 (3.3) ◽  
pp. 14 ◽  
Author(s):  
Professor P.Venkatreddy ◽  
A Siva Krishna ◽  
G SwamyYadav

In this article, the effect of replacing cement with silica fume and fine aggregate with copper slag has been investigated. For this research work, concrete of M40 grade is prepared and evaluated for fresh and harden concrete properties such as compressive strength, tensile strength and flexural strength. Further, the cement is replaced with silica fume at 0, 2, 4, 6, 8 and 10 % and fine aggregate replaced with copper slag at 0, 10, 20, 30, 40 and 50 %. Compressive strength, strength and Flexure strength have been tested. It is observed from the results that the use of silica fume and copper slag as partial replacement material improves mechanical properties of the concrete. Concrete with 40 % copper slag and 8 % silica fume shows better performance among all the mixes.  


2013 ◽  
Vol 661 ◽  
pp. 108-111 ◽  
Author(s):  
Xiao Zhao ◽  
Qing Yuan Wang ◽  
Yong Jie Liu

A huge number of buildings collapsed during the earthquake in Wenchuan. Recently, solid waste management of these post-earthquake wastes has becomes a major management concerns in Sichuan. As yet, no specific plan has been formulated for managing those components of the disaster. In addition to this, immediate efforts are needed to rebuild the collapsed houses within the city. Therefore, this paper aims to overview of some of the research published regarding the use of recycled waste or by-products in concrete blocks or bricks production. The mechanical properties such as compressive strength, tensile strength, water absorption and shrinkage are presented. Studies show that bricks and blocks can be made with recycled waste or by-products.


2017 ◽  
Vol 25 (3) ◽  
pp. 161-170
Author(s):  
Henny Lydiasari ◽  
Ari Yusman Manalu ◽  
Rahmi Karolina

The potency of oil palm empty fruit bunches (OPEFB) fibers as one of the by-products of processing oil palm is increasing significantly so that proper management is needed in reducing environmental impact. One of the utilization of OPEFB fibers is as a substitution material in construction which usually the material is derived from non-renewable mining materials so that the number is increasingly limited. Therefore, it is necessary to study to know the performance of OPEFB fiber in making construction products especially concrete. In this case, the experiment was conducted using experimental method with variation of fiber addition by 0%, 10%, 15%, 20%, 25%, and 30%. Each specimen was tested by weight, slump value, compressive strength, tensile strength, elasticity and crack length. As the results, the variation of fibers addition by 10%, decrease of slump value is 7%, concrete weight is 3% and crack length is 8% while increase of the compressive strength is 2.7% and the modulus of elasticity is 33.3% but its tensile strength decreased insignificantly by 0.05% . Furthermore, the addition of fibers above 10% to 30% decreased compressive strength is still below 10% and tensile strength below 2% while the weight of concrete, slump value and crack length decreased. Therefore, the addition of 10% can replace the performance of concrete without fiber but the addition of above 10% can still be used on non-structural concrete.


2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Parvathaneni Rajendra Kumar ◽  
Suban Ravichandran ◽  
Satyala Narayana

AbstractObjectivesThis research work exclusively aims to develop a novel heart disease prediction framework including three major phases, namely proposed feature extraction, dimensionality reduction, and proposed ensemble-based classification.MethodsAs the novelty, the training of NN is carried out by a new enhanced optimization algorithm referred to as Sea Lion with Canberra Distance (S-CDF) via tuning the optimal weights. The improved S-CDF algorithm is the extended version of the existing “Sea Lion Optimization (SLnO)”. Initially, the statistical and higher-order statistical features are extracted including central tendency, degree of dispersion, and qualitative variation, respectively. However, in this scenario, the “curse of dimensionality” seems to be the greatest issue, such that there is a necessity of dimensionality reduction in the extracted features. Hence, the principal component analysis (PCA)-based feature reduction approach is deployed here. Finally, the dimensional concentrated features are fed as the input to the proposed ensemble technique with “Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN)” with optimized Neural Network (NN) as the final classifier.ResultsAn elaborative analyses as well as discussion have been provided by concerning the parameters, like evaluation metrics, year of publication, accuracy, implementation tool, and utilized datasets obtained by various techniques.ConclusionsFrom the experiment outcomes, it is proved that the accuracy of the proposed work with the proposed feature set is 5, 42.85, and 10% superior to the performance with other feature sets like central tendency + dispersion feature, central tendency qualitative variation, and dispersion qualitative variation, respectively.ResultsFinally, the comparative evaluation shows that the presented work is appropriate for heart disease prediction as it has high accuracy than the traditional works.


2021 ◽  
Vol 11 (11) ◽  
pp. 4754
Author(s):  
Assia Aboubakar Mahamat ◽  
Moussa Mahamat Boukar ◽  
Nurudeen Mahmud Ibrahim ◽  
Tido Tiwa Stanislas ◽  
Numfor Linda Bih ◽  
...  

Earth-based materials have shown promise in the development of ecofriendly and sustainable construction materials. However, their unconventional usage in the construction field makes the estimation of their properties difficult and inaccurate. Often, the determination of their properties is conducted based on a conventional materials procedure. Hence, there is inaccuracy in understanding the properties of the unconventional materials. To obtain more accurate properties, a support vector machine (SVM), artificial neural network (ANN) and linear regression (LR) were used to predict the compressive strength of the alkali-activated termite soil. In this study, factors such as activator concentration, Si/Al, initial curing temperature, water absorption, weight and curing regime were used as input parameters due to their significant effect in the compressive strength. The experimental results depict that SVM outperforms ANN and LR in terms of R2 score and root mean square error (RMSE).


2020 ◽  
pp. 002029402096482
Author(s):  
Sulaiman Khan ◽  
Abdul Hafeez ◽  
Hazrat Ali ◽  
Shah Nazir ◽  
Anwar Hussain

This paper presents an efficient OCR system for the recognition of offline Pashto isolated characters. The lack of an appropriate dataset makes it challenging to match against a reference and perform recognition. This research work addresses this problem by developing a medium-size database that comprises 4488 samples of handwritten Pashto character; that can be further used for experimental purposes. In the proposed OCR system the recognition task is performed using convolution neural network. The performance analysis of the proposed OCR system is validated by comparing its results with artificial neural network and support vector machine based on zoning feature extraction technique. The results of the proposed experiments shows an accuracy of 56% for the support vector machine, 78% for artificial neural network, and 80.7% for the proposed OCR system. The high recognition rate shows that the OCR system based on convolution neural network performs best among the used techniques.


Algorithms ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 37 ◽  
Author(s):  
Zhigang Hu ◽  
Hui Kang ◽  
Meiguang Zheng

A distributed data stream processing system handles real-time, changeable and sudden streaming data load. Its elastic resource allocation has become a fundamental and challenging problem with a fixed strategy that will result in waste of resources or a reduction in QoS (quality of service). Spark Streaming as an emerging system has been developed to process real time stream data analytics by using micro-batch approach. In this paper, first, we propose an improved SVR (support vector regression) based stream data load prediction scheme. Then, we design a spark-based maximum sustainable throughput of time window (MSTW) performance model to find the optimized number of virtual machines. Finally, we present a resource scaling algorithm TWRES (time window resource elasticity scaling algorithm) with MSTW constraint and streaming data load prediction. The evaluation results show that TWRES could improve resource utilization and mitigate SLA (service level agreement) violation.


Materials ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 953
Author(s):  
Michał Łach ◽  
Reda A. Gado ◽  
Joanna Marczyk ◽  
Celina Ziejewska ◽  
Neslihan Doğan-Sağlamtimur ◽  
...  

Alkali activated cement (AAC) can be manufactured from industrial by-products to achieve goals of “zero-waste” production. We discuss in detail the AAC production process from (waste) post-production clay, which serves as the calcium-rich material. The effect of different parameters on the changes in properties of the final product, including morphology, phase formation, compressive strength, resistance to the high temperature, and long-term curing is presented. The drying and grinding of clay are required, even if both processes are energy-intensive; the reduction of particle size and the increase of specific surface area is crucial. Furthermore, calcination at 750 °C ensure approximately 20% higher compressive strength of final AAC in comparison to calcination performed at 700 °C. It resulted from the different ratio of phases: Calcite, mullite, quartz, gehlenite, and wollastonite in the final AAC. The type of activators (NaOH, NaOH:KOH mixtures, KOH) affected AAC mechanical properties, significantly. Sodium activators enabled obtaining higher values of strength. However, if KOH is required, the supplementation of initial materials with fly ash or metakaolin could improve the mechanical properties and durability of AAC, even c.a. 28%. The presented results confirm the possibility of recycling post-production clay from the Raciszyn II Jurassic limestone deposit.


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