Point-load test and UPV for compressive strength prediction of recycled coarse aggregate concrete via generalized GMDH-class neural network

Razzaghi, Hossein; Madandoust, Rahmat; Aghabarati, Hassan

doi:10.1016/j.conbuildmat.2020.122143

Cited by 15 publications

(3 citation statements)

References 33 publications

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“…This is attributed to the higher degree of hydration. This result was also reported by Bingöl and Tohumcu [ 82 ], Razzaghi et al [ 83 ], and Benli et al [ 84 ].…”

Section: Resultssupporting

confidence: 87%

Investigation of Impact Resistance of High-Strength Portland Cement Concrete Containing Steel Fibers

et al. 2022

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Impact resistance of Portland cement concrete (PCC) is an essential property in various applications of PCC, such as industrial floors, hydraulic structures, and explosion-proof structures. Steel-fiber-fortified high-strength concrete testing was completed using a drop-weight impact assessment for impact strength. One mix was used to manufacture 320 concrete disc specimens cured in both humid and dry conditions. In addition, 30 cubic and 30 cylindrical specimens were used to evaluate the compressive and indirect tensile strengths. Steel fibers with hooked ends of lengths of 20, 30, and 50 mm were used in the concrete mixtures. Data on material strength were collected from impact testing, including the number of post-first-crack blows (INPBs), first-crack strength, and failure strength. Findings from the results concluded that all the steel fibers improved the mechanical properties of concrete. However, hooked steel fibers were more effective than crimped steel fibers in increasing impact strength, even with a smaller length-to-diameter ratio. Concrete samples containing hybrid fibers (hooked + crimped) also had lower compressive strength than the other fibers. Comparisons and analogies drawn between the test results and the static analyses (Kolmogorov–Smirnov and Kruskal–Wallis) show that the p-value of the analyses indicates a more normal distribution for curing in a humid environment. A significant difference was also observed between the energy absorptions of the reinforced mixtures into steel fibers.

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“…This is attributed to the higher degree of hydration. This result was also reported by Bingöl and Tohumcu [ 82 ], Razzaghi et al [ 83 ], and Benli et al [ 84 ].…”

Section: Resultssupporting

confidence: 87%

Investigation of Impact Resistance of High-Strength Portland Cement Concrete Containing Steel Fibers

et al. 2022

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“…Genetic Algorithms (GAs) have recently attracted attention in feedforward self-organizing networks. In this study, neuron connections are controlled to adjacent layers [27]. The lack of effective training algorithms for training multi-layer perceptron is an important issue in GMDH networks.…”

Section: Related Workmentioning

confidence: 99%

Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network

Goudarzi¹,

Soleymani²,

Anisi³

et al. 2022

Computers, Materials &Amp; Continua

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The Wireless Sensor Network (WSN) is a promising technology that could be used to monitor rivers' water levels for early warning flood detection in the 5G context. However, during a flood, sensor nodes may be washed up or become faulty, which seriously affects network connectivity. To address this issue, Unmanned Aerial Vehicles (UAVs) could be integrated with WSN as routers or data mules to provide reliable data collection and flood prediction. In light of this, we propose a fault-tolerant multi-level framework comprised of a WSN and a UAV to monitor river levels. The framework is capable to provide seamless data collection by handling the disconnections caused by the failed nodes during a flood. Besides, an algorithm hybridized with Group Method Data Handling (GMDH) and Particle Swarm Optimization (PSO) is proposed to predict forthcoming floods in an intelligent collaborative environment. The proposed water-level prediction model is trained based on the real dataset obtained from the Selangor River in Malaysia. The performance of the work in comparison with other models has been also evaluated and numerical results based on different metrics such as coefficient of determination (R 2 ), correlation coefficient (R), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and BIAS are provided.

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“…The XGBoost model developed on the traditional tree gradient lifting method predicts the compressive strength of concrete laboratory data sets, and XGBoost shows the strong ability of structured data sets. The machine-learning method offers substantial advantages in the prediction of concrete strength [25][26][27][28][29][30][31]. SVM often needs to use a cross-validation method to determine the model complexity parameter C. For RVM, another advantage of introducing the Bayesian method is that it eliminates the step of model selection.…”

Section: Introductionmentioning

confidence: 99%

Numerical Test and Strength Prediction of Concrete Failure Process Based on RVM Algorithm

2022

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Recycled aggregate concrete (RAC) based on the machine learning (ML) method predicts the nonlinear uncertainty relationship between various mixing ratios and strength. Uniaxial compressive strength is one of the important indices to evaluate its performance. Machine learning is one of the essential methods for solving this nonlinear uncertainty relationship. To realize the selection of concrete raw materials and the learning and application of other influencing factors and provide guidance for engineering construction and application, this paper establishes a database of concrete uniaxial compressive strength based on Abaqus simulation software. The simulation results are highly consistent with the actual values. Based on the simulation database, with different water-cement ratios, different curing days, and recycled aggregate replacement rates as the input data set, the uniaxial compressive strength of concrete is the output data set. The data set is divided into a training set and a test set. A prediction model of the uniaxial compressive strength of concrete based on a relevance vector machine (RVM) algorithm is established. The results show that the maximum error between the simulated and experimental uniaxial compressive strength values is only 0.2 MPa. The correlation coefficient R between the predicted and simulated values of the concrete uniaxial compressive strength prediction model based on the RVM algorithm is 0.975. The model can effectively predict the compressive strength of RAC to meet the engineering requirements.

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Point-load test and UPV for compressive strength prediction of recycled coarse aggregate concrete via generalized GMDH-class neural network

Cited by 15 publications

References 33 publications

Investigation of Impact Resistance of High-Strength Portland Cement Concrete Containing Steel Fibers

Investigation of Impact Resistance of High-Strength Portland Cement Concrete Containing Steel Fibers

Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network

Numerical Test and Strength Prediction of Concrete Failure Process Based on RVM Algorithm

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