Modeling properties of self-compacting concrete: support vector machines approach

Siddique, Rafat; Aggarwal, Paratibha; Aggarwal, Yogesh; Gupta, Sakshi

doi:10.12989/cac.2008.5.5.461

Cited by 17 publications

(5 citation statements)

References 14 publications

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“…Following studies performs the estimation of SCC material properties. Siddique (2008) investigated the potential of SVM approach in predicting the compressive strength and slump flow of self-compacting concrete. The results are compared with those of back propagation neural network model and it is observed that the SVM has a greater performance both for compressive strength and slump flow prediction.…”

Section: Applications In Modeling Self-compacting Concrete (Scc) Propmentioning

confidence: 99%

Support Vector Machines in Structural Engineering: A Review

Çevik

Kurtoğlu

Bilgehan

et al. 2015

Journal of Civil Engineering and Management

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Recent development in data processing systems had directed study and research of engineering towards the creation of intelligent systems to evolve models for a wide range of engineering problems. In this respect, several modeling techniques have been created to simulate various civil engineering systems. This study aims to review the studies on support vector machines (SVM) in structural engineering and investigate the usability of this machine learning based approach by providing three case studies focusing on structural engineering problems. Firstly, the concept of SVM is explained and then, the recent studies on the application of SVM in structural engineering are summarized and discussed. Next, we performed three case studies using the experimental studies provided. Applicability of SVM in structural engineering is confirmed by these case studies. The results showed that SVM is superior to various other learning techniques considering the generalization capability of produced model.

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Section: Applications In Modeling Self-compacting Concrete (Scc) Propmentioning

confidence: 99%

Support Vector Machines in Structural Engineering: A Review

Çevik

Kurtoğlu

Bilgehan

et al. 2015

Journal of Civil Engineering and Management

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“…Plastic fibers (Al-Hadithi and Hilal, 2016;Mohammad-hosseini and Yatim, 2017) and steel fibers (Banthia and Onuaguluchi, 2021;Grünewald and Walraven, 2001) have also been used in SCC production. It was observed that there is a non-linear relationship between mixing constituents and compressive strength but there is no theoretical or mathematical relationship between mixture ratio and SCC strength (American Concrete Institute, 2019; Siddique et al, 2008). This means it is necessary to use appropriate methods to predict SCC strength based on the mixing ingredients during the design phase.…”

Section: Introductionmentioning

confidence: 99%

Compressive Strength Prediction for Industrial Waste-Based SCC Using Artificial Neural Network

Hossain

Islam

Mallick

2022

jcef

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Concrete is the most used construction material in the world. Sustainable construction practice demands durable material. A particular type of concrete that flows and consolidates under its weight is proposed to reduce labor dependency during construction, called self-compacting concrete. It is installed without vibration due to its excellent deformability and flowability while remaining cohesive enough to be treated without difficulty. Evaluating its compressive strength is essential as it is used in important construction projects. An artificial neural network (ANN) is a predicting tool that can predict output in various sectors. This study evaluated the compressive strength of industrial waste such as fly ash and silica fume incorporated in self-compacting concrete at various ages. A non-linear relationship was used to develop the model relating mix composition and SCC compressive strength using an Artificial Neural Network (ANN). The experimental and expected outcomes were compared with the model prediction to evaluate the predictive capacity, generalize the generated model, and observe suitable matches. The developed ANN network can predict the desired output, i.e., compressive strength incorporating industrial waste. Furthermore, the influence of individual parameters viz. cement, silica fume, and fly ash, w/b were also evaluated using parametric analysis, which shows the sensitivity of various materials on the compressive strength of Self-compacting concrete. As a result, a higher correlation coefficient of 0.9835 with a smaller value of MAPE (0.0347) and RMSE (2.4503) is obtained. Finally, a process of creating tools for practical engineers and field users is proposed, which would be very handy and fast for predicting the strength of SCC.

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“…Conventional modeling techniques are based on empirical relationships developed from the experiment al data. Within last few year, researchers have explored the capabilities of artificial intelligence techniques such as ANN [20][21][22][23][24][25], Support Vector Machines (SVM), Fuzzy Logic, M5 model tree, GRNN, ANFIS [25][26][27][28][29][30][31][32][33] for various problems in the field of civil engineering. A need to derive a simpler method to compete with the simplicit y of empirical formula can be use of data mining techniques.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Punching Shear Capacity of Concrete Slabs Using Data Mining Techniques

Aggarwal

Sihag

et al. 2019

IJE

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A B S T R A C TPunching shear capacity is a key factor for governing the collapsed form of slabs. This fragile failure that occurs at the slab-column connection is called punching shear failure and has been of concern for the engineers. The most common practice in evaluating the punching strength of the concrete slabs is to use the empirical expressions available in different building design codes. T he estimation of punching loads involves experimental setup which is time-consuming, uneconomical and also, more manpower and materials are required. T he present study demonstrates the use of data mining techniques as a substitute of former to predict the punching loads on the variation of various parameters. In this study, various type of data mining techniques including Adaptive Neuro-fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Generalized Neural Network (GRNN) were applied to model and estimate the punching load of reinforced concrete slab-column connections. For the study, a data set consisting of 89 observations from available literature was analysed and randomly selected 62 observations were used for model development whereas the rest 27 were used to test the developed models. While the outcomes of ANN and GRNN model provides suitable estimation performance, the Gaussian membership based ANFIS model performed best in the determination of coefficient of correlation (Cc). Sensitivity study indicates that the parameter effective depth of slab (d) is the most influencing one for the estimation of punching load of reinforced concrete slab-column connections for this data set.

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Modeling properties of self-compacting concrete: support vector machines approach

Cited by 17 publications

References 14 publications

Support Vector Machines in Structural Engineering: A Review

Support Vector Machines in Structural Engineering: A Review

Compressive Strength Prediction for Industrial Waste-Based SCC Using Artificial Neural Network

Estimation of Punching Shear Capacity of Concrete Slabs Using Data Mining Techniques

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