Exploring the major factors affecting fly-ash concrete carbonation using artificial neural network

Kellouche, Yasmina; Boukhatem, Bakhta; Ghrici, Mohamed; Tagnit-Hamou, Arezki

doi:10.1007/s00521-017-3052-2

Cited by 56 publications

(32 citation statements)

References 39 publications

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“…In this figure, the effect of fly ash addition becomes more evident primarily at later ages such as 545 and 730 days. In general, an increase in the fly ash addition is observed to yield an increased carbonation depth as suggested in the previous literature (40). This general trend has been steadily observed except for the specific value of 50% fly ash replacement yielding a relatively lower value of carbonation depth in this study.…”

Section: Parametric Analysis Results Studying the Progress Of Carbonasupporting

confidence: 89%

“…Hence, the ingress of CO 2 into concrete to initiate carbonation becomes less in concrete mixes with higher densities (1). These findings are also in accordance with the previous literature (40). Kazts et al (43) also has discussed the effect of increasing cement content on the progress of carbonation, in terms of carbonation "coefficient" rather than the determined "depth"; hence the reported results could not be directly compared with the values obtained in this study.…”

Section: Parametric Analysis Results Studying the Progress Of Carbonasupporting

confidence: 87%

“…Both Silva et al (2014) (36) and Elmoaty et al (2018) (42) emphasize the importance of cement types in the determination of final carbonation depth. Some of the studies in the literature, have considered only the cement content (kg/m 3 ), practically ignoring the type of cement to be added (11,40). Some other studies presented concrete carbonation results varying based on only certain cement types that were specified in those particular studies (42) in a categorical manner, and their models cannot be employed directly for other cases where another cement could be used.…”

Section: Parametric Analysis Results Studying the Progress Of Carbonamentioning

confidence: 99%

“…A partially comparable study was previously presented by Kellouche et al (2017) (40); in which only six input parameters were used in a three-layer neural net having 8 neurons in the hidden layer. Furthermore, previous works done on the concrete carbonation by using ANN approach for prediction (9-11) are observed to have limited number of experimental data, which is known to negatively affect the reliability of the predictions' accuracy.…”

Section: Prediction Efficiencies Of the Proposed Models Based On The mentioning

confidence: 85%

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Investigation of the parameters influencing progress of concrete carbonation depth by using artificial neural networks

Akpınar

Uwanuakwa

2020

Mater. construcc.

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Carbonation is a deleterious concrete durability problem which may alter concrete microstructure and yield initiation of corrosion in reinforcing steel bars. Previous studies focused on the use of Artificial Neural Networks (ANN) for the prediction of concrete carbonation depth and to minimize the need for destructive and elaborated civil engineering laboratory tests. This study aims to provide improved accuracy of simulation and prediction of carbonation with an ANN architecture including eighteen input parameters employing alternative Scaled Conjugate Gradient (SCG) function. After ensuring a promising value of the coefficient of correlation as high as 0.98, the influence of proposed input parameters on the progress of carbonation depth was studied. The results of this parametric analysis were observed to successfully comply with the conventional civil engineering experience. Hence, the employed ANN model can be used as an efficient tool to study in detail and to provide insights into the carbonation problem in concrete.

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Section: Parametric Analysis Results Studying the Progress Of Carbonasupporting

confidence: 89%

Section: Parametric Analysis Results Studying the Progress Of Carbonasupporting

confidence: 87%

Section: Parametric Analysis Results Studying the Progress Of Carbonamentioning

confidence: 99%

Section: Prediction Efficiencies Of the Proposed Models Based On The mentioning

confidence: 85%

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Investigation of the parameters influencing progress of concrete carbonation depth by using artificial neural networks

Akpınar

Uwanuakwa

2020

Mater. construcc.

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“…e integration of computer vision methods using deep convolutional neural networks (CNNs) shows exceptional promise for use in crack detection applications, but require many images for the training process [10,27,33]. Although…”

Section: Introductionmentioning

confidence: 99%

Real‐Time Road Crack Mapping Using an Optimized Convolutional Neural Network

et al. 2019

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Pavement surveying and distress mapping is completed by roadway authorities to quantify the topical and structural damage levels for strategic preventative or rehabilitative action. The failure to time the preventative or rehabilitative action and control distress propagation can lead to severe structural and financial loss of the asset requiring complete reconstruction. Continuous and computer-aided surveying measures not only can eliminate human error when analyzing, identifying, defining, and mapping pavement surface distresses, but also can provide a database of road damage patterns and their locations. The database can be used for timely road repairs to gain the maximum durability of the asphalt and the minimum cost of maintenance. This paper introduces an autonomous surveying scheme to collect, analyze, and map the image-based distress data in real time. A descriptive approach is considered for identifying cracks from collected images using a convolutional neural network (CNN) that classifies several types of cracks. Typically, CNN-based schemes require a relatively large processing power to detect desired objects in images in real time. However, the portability objective of this work requires to utilize low-weight processing units. To that end, the CNN training was optimized by the Bayesian optimization algorithm (BOA) to achieve the maximum accuracy and minimum processing time with minimum neural network layers. First, a database consisting of a diverse population of crack distress types such as longitudinal, transverse, and alligator cracks, photographed at multiple angles, was prepared. Then, the database was used to train a CNN whose hyperparameters were optimized using BOA. Finally, a heuristic algorithm is introduced to process the CNN’s output and produce the crack map. The performance of the classifier and mapping algorithm is examined against still images and videos captured by a drone from cracked pavement. In both instances, the proposed CNN was able to classify the cracks with 97% accuracy. The mapping algorithm is able to map a diverse population of surface cracks patterns in real time at the speed of 11.1 km per hour.

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Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend

Kazemi

2023

Engineering Reports

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Advanced concrete technology is the science of efficient, cost‐effective, and safe design in civil engineering projects. Engineers and concrete designers are generally faced with the slightest change in the conditions or objectives of the project, which makes it challenging to choose the optimal design among several ones. Besides, the experimental examination of all of them requires time and high costs. Hence, an efficient approach is to utilize artificial intelligence (AI) techniques to predict and optimize real‐world problems in concrete technology. Despite the large body of publications in this field, there are few comprehensive surveys that conduct scientometric analysis. This paper provides a state‐of‐the‐art review that lists, summarizes, and categorizes the most widely used machine learning methods, meta‐heuristic algorithms, and hybrid approaches to concrete issues. To this end, 457 publications are considered during the recent decade with a scientometric approach to highlight the annual trend/active journals/top researchers/co‐occurrence of key title words/countries' participation/research hotspots. In addition, AI techniques are classified into distinct clusters using VOSviewer clustering visualization to identify the application scope and their relationship through the link strength. The findings can be a beacon to help researchers utilize AI techniques in future research on advanced concrete technology.

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Exploring the major factors affecting fly-ash concrete carbonation using artificial neural network

Cited by 56 publications

References 39 publications

Investigation of the parameters influencing progress of concrete carbonation depth by using artificial neural networks

Investigation of the parameters influencing progress of concrete carbonation depth by using artificial neural networks

Real‐Time Road Crack Mapping Using an Optimized Convolutional Neural Network

Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend

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