Implementation of hybrid neuro-fuzzy and self-turning predictive model for the prediction of concrete carbonation depth: A soft computing technique

Malami, Salim Idris; Anwar, Faiz Habib; Haruna, Sadi İbrahim; Ali‬, Shaban Ismael Albrka; Abba, S.I.

doi:10.1016/j.rineng.2021.100228

Cited by 61 publications

(15 citation statements)

References 37 publications

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“…A reação leva à alteração da microestrutura do concreto e à redução do pH nos poros do material, além de uma redução global da alcalinidade do sistema (CASCUDO et al, 2021;CASCUDO;CARASEK, 2022). À medida que o processo avança, desenvolve-se a corrosão do aço, que acarreta graves danos às estruturas de concreto (MALAMI et al, 2021).…”

Section: Introductionunclassified

Estimativa de frentes de carbonatação baseada em análises estatísticas: aplicando modelo europeu em território brasileiro

2023

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Resumo Este artigo tem como objetivo avaliar a aplicabilidade do modelo de predição de frentes de carbonatação preconizado pelo CEB Bulletin 34 (2006) em território brasileiro. Para isso, dados de concretos produzidos em laboratório e expostos à carbonatação natural por 10 anos foram utilizados como balizadores. Tempo de exposição, relação água/ligante, adição mineral e cura foram consideradas variáveis independentes, ao passo que a profundidade e o coeficiente de carbonatação como dependentes. Foram realizadas análises de variância e regressão linear entre os dados reais e os estimados, de maneira a estabelecer critériosque permitiram constatar a aplicabilidade do modelo. Consideraram-se condições ambientais e climáticas da cidade de Goiânia, GO, localizada na zona tropical Brasil Central, com clima quente semiúmido. Como resultado, com base nos coeficientes de correlação e determinação - iguais a 0,83 e 0,68 respectivamente - conclui-se que o modelo precisa ser revisto antes de ser aplicado no Brasil. Mais especificamente, o fator de resistência inversa efetiva à carbonatação natural, que associa a relaçãoágua/ligante e o tipo de adição, deve ser reajustado de maneira que represente os efeitos em ordem de grandeza semelhantes às reais.

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Section: Introductionunclassified

Estimativa de frentes de carbonatação baseada em análises estatísticas: aplicando modelo europeu em território brasileiro

2023

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“…Fang et al 22 used an image segmentation method to investigate the effect of pore structure on the split tensile strength of cellular concrete. Malami et al 23 used a neuro-fuzzy hybrid model composed of, an extreme learning machine (ELM), an adaptive neuro-fuzzy inference system (ANFIS), a multi-linear regression model (MLR), and SVR to study the impact of carbonization on reinforced concrete durability (R 0.96). Ashrafian et al 24 have developed an evolutionary-based ML model which give promising prediction of post-fire mechanical properties of green concrete.…”

Section: Introductionmentioning

confidence: 99%

Modeling strength characteristics of basalt fiber reinforced concrete using multiple explainable machine learning with a graphical user interface

Kulasooriya,

Ranasinghe,

Perera

et al. 2023

Sci Rep

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This study investigated the importance of applying explainable artificial intelligence (XAI) on different machine learning (ML) models developed to predict the strength characteristics of basalt-fiber reinforced concrete (BFRC). Even though ML is widely adopted in strength prediction in concrete, the black-box nature of predictions hinders the interpretation of results. Among several attempts to overcome this limitation by using explainable AI, researchers have employed only a single explanation method. In this study, we used three tree-based ML models (Decision tree, Gradient Boosting tree, and Light Gradient Boosting Machine) to predict the mechanical strength characteristics (compressive strength, flexural strength, and tensile strength) of basal fiber reinforced concrete (BFRC). For the first time, we employed two explanation methods (Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME)) to provide explanations for all models. These explainable methods reveal the underlying decision-making criteria of complex machine learning models, improving the end user's trust. The comparison highlights that tree-based models obtained good accuracy in predicting strength characteristics yet, their explanations were different either by the magnitude of feature importance or the order of importance. This disagreement pushes towards complicated decision-making based on ML predictions which further stresses (1) extending XAI-based research in concrete strength predictions, and (2) involving domain experts to evaluate XAI results. The study concludes with the development of a “user-friendly computer application” which enables quick strength prediction of basalt fiber reinforced concrete (BFRC).

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“…AI and other computational machine learning models have been recently developed and have been demonstrated to be effective in comparison to various classical, statistical physics-based, and mathematical models [17][18][19][20]. The promising applications of AI-based models are not limited to the understanding and removal of HMs but also extend to the system identification of science and engineering problems [21][22][23][24][25][26][27]. The superiority of data-driven models is attributed to certain factors, such as the building of models, type of learning, data type, and basin characteristics.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variability Assessment of Trace Metals Based on Subsurface Water Quality Impact Integrated with Artificial Intelligence-Based Modeling

et al. 2022

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Increasing anthropogenic emissions due to rapid industrialization have triggered environmental pollution and pose a threat to the well-being of the ecosystem. In this study, the first scenario involved the spatio-temporal assessment of topsoil contamination with trace metals in the Dammam region, and samples were taken from 2 zones: the industrial (ID), and the agricultural (AG) area. For this purpose, more than 130 spatially distributed samples of topsoil were collected from residential, industrial, and agricultural areas. Inductively coupled plasma—optical emission spectroscopy (ICP-OES)—was used to analyze the samples for various trace metals. The second scenario involved the creation of different artificial intelligence (AI) models, namely an artificial neural network (ANN) and a support vector regression (SVR), for the estimation of zinc (Zn), copper (Cu), chromium (Cr), and lead (Pb) using feature-based input selection. The experimental outcomes depicted that the average concentration levels of HMs were as follows: Chromium (Cr) (31.79 ± 37.9 mg/kg), Copper (Cu) (6.76 ± 12.54 mg/kg), Lead (Pb) (6.34 ± 14.55 mg/kg), and Zinc (Zn) (23.44 ± 84.43 mg/kg). The modelling accuracy, based on different evaluation criteria, showed that agricultural and industrial stations showed performance merit with goodness-of-fit ranges of 51–91% and 80–99%, respectively. This study concludes that AI models could be successfully applied for the rapid estimation of soil trace metals and related decision-making.

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Implementation of hybrid neuro-fuzzy and self-turning predictive model for the prediction of concrete carbonation depth: A soft computing technique

Cited by 61 publications

References 37 publications

Estimativa de frentes de carbonatação baseada em análises estatísticas: aplicando modelo europeu em território brasileiro

Estimativa de frentes de carbonatação baseada em análises estatísticas: aplicando modelo europeu em território brasileiro

Modeling strength characteristics of basalt fiber reinforced concrete using multiple explainable machine learning with a graphical user interface

Spatiotemporal Variability Assessment of Trace Metals Based on Subsurface Water Quality Impact Integrated with Artificial Intelligence-Based Modeling

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