Implementation of Artificial Neural Networks for Prediction of Chloride Penetration in Concrete

Mohamed, Osama Ahmed; Ati, Modafar; Hawat, Waddah Al

doi:10.14419/ijet.v7i2.28.12880

Cited by 13 publications

(5 citation statements)

References 15 publications

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“…It is well known that there are a significant number of variables that influence the concrete compressive strength, such as the water-cement ratio, aggregate-cement ratio, age of testing, additions, curing time, cement types, etc. In order to associate this information, the use of artificial neural networks (ANN) is crucial, as an ANN can present a response that reflects the influence of the parameters in the obtained result [7,8]. In recent years, ANN modelling has become increasingly popular and has been commonly used in civil engineering tasks with some degree of success, where the modelling of material behavior and characteristics plays a significant role in these applications [9].…”

Section: Introductionmentioning

confidence: 99%

Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete

et al. 2021

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The work presents the results of an experimental campaign carried out on concrete elements in order to investigate the potential of using artificial neural networks (ANNs) to estimate the compressive strength based on relevant parameters, such as the water–cement ratio, aggregate–cement ratio, age of testing, and percentage cement/metakaolin ratios (5% and 10%). We prepared 162 cylindrical concrete specimens with dimensions of 10 cm in diameter and 20 cm in height and 27 prismatic specimens with cross sections measuring 25 and 50 cm in length, with 9 different concrete mixture proportions. A longitudinal transducer with a frequency of 54 kHz was used to measure the ultrasonic velocities. An ANN model was developed, different ANN configurations were tested and compared to identify the best ANN model. Using this model, it was possible to assess the contribution of each input variable to the compressive strength of the tested concretes. The results indicate an excellent performance of the ANN model developed to predict compressive strength from the input parameters studied, with an average error less than 5%. Together, the water–cement ratio and the percentage of metakaolin were shown to be the most influential factors for the compressive strength value predicted by the developed ANN model.

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Section: Introductionmentioning

confidence: 99%

Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete

et al. 2021

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“…The Dcl increased with a high w/b ratio [ 48 ]. However, few studies have investigated the influence of SCMs such as GGBFS and mix design concerning the chloride diffusion coefficient ML approach [ 35 , 36 , 49 ]. Therefore, this study could provide a comprehensive literature reference value for subsequent related requirements and has an excellent guiding significance for engineering practice, as the evaluation of durability-related properties requires extensive laboratory experiment, resources, and time consumption.…”

Section: Significance Of Researchmentioning

confidence: 99%

Prediction of Chloride Diffusion Coefficient in Concrete Modified with Supplementary Cementitious Materials Using Machine Learning Algorithms

Fuhaid

Alanazi

2023

Materials

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The chloride diffusion coefficient (Dcl) is one of the most important characteristics of concrete durability. This study aimed to develop a prediction model for the Dcl of concrete incorporating supplemental cementitious material. The datasets of concrete containing supplemental cementitious materials (SCMs) such as tricalcium aluminate (C3A), ground granulated blast furnace slag (GGBFS), and fly ash were used in developing the model. Five machine learning (ML) algorithms including adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), support vector machine (SVM), and extreme learning machine (ELM) were used in the model development. The performance of the developed models was tested using five evaluation metrics, namely, normalized reference index (RI), coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The SVM models demonstrated the highest prediction accuracy with R2 values of 0.955 and 0.951 at the training and testing stage, respectively. The prediction accuracy of the machine learning (ML) algorithm was checked using the Taylor diagram and Boxplot, which confirmed that SVM is the best ML algorithm for estimating Dcl, thus, helpful in establishing reliable tools in concrete durability design.

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“…Chandwani [74] developed a slump prediction model of ready-mix concrete using genetically evolved artificial neural networks. The chloride penetration in self-consolidating concrete (SCC) mixes containing various types of minerals was predicted by Mohamed et al [75] using the ANN technique in which training was conducted using published data and validation was carried out using chloride penetration experimental data. ANN-based prediction of the concrete's interfacial transition-zone fracture properties was performed by Xi et al [76] to understand the cracking behavior to develop quality concrete.…”

Section: Materials Testing and Controlmentioning

confidence: 99%

Artificial Neural Networks for Sustainable Development of the Construction Industry

et al. 2022

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Artificial Neural Networks (ANNs), the most popular and widely used Artificial Intelligence (AI) technology due to their proven accuracy and efficiency in control, estimation, optimization, decision making, forecasting, and many other applications, can be employed to achieve faster sustainable development of construction industry. The study presents state-of-the-art applications of ANNs to promote sustainability in the construction industry under three aspects of sustainable development, namely, environmental, economic, and social. The environmental aspect surveys ANNs’ applications in sustainable construction materials, energy management, material testing and control, infrastructure analysis and design, sustainable construction management, infrastructure functional performance, and sustainable maintenance management. The economic aspect covers financial management and construction productivity through ANN applications. The social aspect reviews society and human values and health and safety issues in the construction industry. The study demonstrates the wide range of interdisciplinary applications of ANN methods to support the sustainable development of the construction industry. It can be concluded that a holistic research approach with comprehensive input data from various phases of construction and segments of the construction industry is needed for the sustainable development of the construction industry. Further research is certainly needed to reduce the dependency of ANN applications on the input dataset. Research is also needed to apply ANNs in construction management, life cycle assessment of construction projects, and social aspects in relation to sustainability concerns of the construction industry.

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Implementation of Artificial Neural Networks for Prediction of Chloride Penetration in Concrete

Cited by 13 publications

References 15 publications

Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete

Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete

Prediction of Chloride Diffusion Coefficient in Concrete Modified with Supplementary Cementitious Materials Using Machine Learning Algorithms

Artificial Neural Networks for Sustainable Development of the Construction Industry

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