Using ANN and ANFIS to predict the mechanical and chloride permeability properties of concrete containing GGBFS and CNI

Boğa, Ahmet Raif; Öztürk, Murat; Topçu, İ̇lker Bekir

doi:10.1016/j.compositesb.2012.05.054

Cited by 99 publications

(46 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Even if employing intelligent data analytics is already becoming a common practice in different disciplines, since recent times, its use in the field of concrete durability is at its infancy. For example to detect rebar corrosion [79,81,113], to characterize carbonation resistance [114][115][116][117][118][119][120], Cl − permeability [121][122][123][124][125][126], hygrothermal performance in concrete [127,128], Cl − diffusion coefficient [129][130][131][132], chloride penetration and carbonation predictors [116,133], and. Indeed, these works largely employ short-term data generated either from accelerated laboratory tests or field tests, but not real-time data from embedded sensors.…”

Section: Evolution Of Data-driven Corrosion Assessmentmentioning

confidence: 99%

Autonomous Corrosion Assessment of Reinforced Concrete Structures: Feasibility Study

Taffese

Nigussie

2020

Sensors

View full text Add to dashboard Cite

In this work, technological feasibility of autonomous corrosion assessment of reinforced concrete structures is studied. Corrosion of reinforcement bars (rebar), induced by carbonation or chloride penetration, is one of the leading causes for deterioration of concrete structures throughout the globe. Continuous nondestructive in-service monitoring of carbonation through pH and chloride ion (Cl−) concentration in concrete is indispensable for early detection of corrosion and making appropriate decisions, which ultimately make the lifecycle management of RC structures optimal from resources and safety perspectives. Critical state-of-the-art review of pH and Cl− sensors revealed that the majority of the sensors have high sensitivity, reliability, and stability in concrete environment, though the experiments were carried out for relatively short periods. Among the reviewed works, only three attempted to monitor Cl− wirelessly, albeit over a very short range. As part of the feasibility study, this work recommends the use of internet of things (IoT) and machine learning for autonomous corrosion condition assessment of RC structures.

show abstract

Section: Evolution Of Data-driven Corrosion Assessmentmentioning

confidence: 99%

Autonomous Corrosion Assessment of Reinforced Concrete Structures: Feasibility Study

Taffese

Nigussie

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…On the contrary, the hybrid tool has been employed in other engineering applications. For instance, a handful of exploration of ANN and ANFIS in the distributed system (Okwu and Adetunji, 2018), mechanical properties of concrete (Boga et al, 2013), wind speed sensor (Rahman and Rahim, 2016), grade estimation (Tahmasebi and Hezarkhani, 2012;Amirkhani et al, 2015), and under convective hot air dryer (Kaveh et al, 2018), sheet hydroforming process (Yaghoobi et al, 2016), etc., have appeared in the literature. Also, the adoption of ANN and ANFIS models are relevant since it strengthens the performance of the model and enables robust modeling for effective productivity and sustainability.…”

Section: Introductionmentioning

confidence: 99%

Modelling of Nicotiana Tabacum L. Oil Biodiesel Production: Comparison of ANN and ANFIS

et al. 2021

View full text Add to dashboard Cite

Among the modern computational techniques, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are preferred because of their ability to deal with non-linear modelling and complex stochastic dataset. Nondeterministic models involve some computational complexities while solving real-life problems but would always produce better outcomes. For the first time, this study utilized the ANN and ANFIS models for modelling tobacco seed oil methyl ester (TSOME) production from underutilized tobacco seeds in the tropics. The dataset for the models was obtained from an earlier study which focused on design of the experiment on TSOME production. This study is an an exposition of the influence of transesterification parameters such as reaction duration (T), methanol/oil molar ratio (M:O), and catalyst dosage on the TSOME/biodiesel yield. A multi-layer ANN model with ten hidden layers was trained to simulate the methanolysis process. The ANFIS approach was further implemented to model TSOME production. A comparison of the formulated models was completed by statistical criteria such as coefficient of determination (R2), mean average error (MAE), and average absolute deviation (AAD). The R2 of 0.8979, MAE of 4.34468, and AAD of 6.0529 for the ANN model compared to those of the R2 of 0.9786, MAE of 1.5311, and AAD of 1.9124 for the ANFIS model. The ANFIS model appears to be more reliable than the ANN model in predicting TSOME production in the tropics.

show abstract

“…ANNs can process information in a parallel distributed method, train the complicated cause and effect relation between input and output data, deal with nonlinear problems and generalize known examples or tasks to unknown tasks. ANNs are good methods for cases including a set of incomplete data and offering incomplete fuzzy information and for problems with high complexities where people usually make decisions based on intuition (Boga et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Mechanical and Flame Retardant Properties of Medium Density Fiberboard Using Artificial Neural Network

Nazerian

Shirzaii

Mohebbigargari

et al. 2020

CERNE

View full text Add to dashboard Cite

show abstract

Using ANN and ANFIS to predict the mechanical and chloride permeability properties of concrete containing GGBFS and CNI

Cited by 99 publications

References 30 publications

Autonomous Corrosion Assessment of Reinforced Concrete Structures: Feasibility Study

Autonomous Corrosion Assessment of Reinforced Concrete Structures: Feasibility Study

Modelling of Nicotiana Tabacum L. Oil Biodiesel Production: Comparison of ANN and ANFIS

Evaluation of Mechanical and Flame Retardant Properties of Medium Density Fiberboard Using Artificial Neural Network

Contact Info

Product

Resources

About