Deep learning model for estimating the mechanical properties of concrete containing silica fume exposed to high temperatures

Tanyıldızı, Harun; Şengür, Abdulkadir; Akbulut, Yaman; Şahin, Murat

doi:10.1007/s11709-020-0646-z

Cited by 31 publications

(9 citation statements)

References 41 publications

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“…erefore, data processing technology plays an important role in resource recommendation and resource display algorithms. Datasets usually need to be preprocessed [19]. Especially for the dataset in the field of enterprise spatial structure, preprocessing the dataset can filter out the impurity information of the evolution of the silent structure of the enterprise to a certain extent.…”

Section: Coupling and Coordination Relationship Of The Evolution Of E...mentioning

confidence: 99%

Analysis of the Coupling and Coordination Relationship between the Evolution of Enterprise Spatial Structure and Economic Development Based on the Deep Learning Model

Wang

Yao

2022

Scientific Programming

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This study is based on the unsupervised learning-based enterprise spatial structure evolution and economic coupling coordination relationship situation assessment method. Pattern recognition has high-precision characteristics, but it is necessary to train the evaluation model for the enterprise spatial structure evolution in advance and then carry out economic coupling coordination based on the trained model. The conclusions are as follows: (1) through the RC1, RC2, RC3, RC4, RC5, and RC6 evaluation indicators to evaluate the situation evaluation method based on the unsupervised learning of the evolution of the enterprise spatial structure and the economic coupling and coordination relationship, it is found that the main component characteristics as a whole meet the standard. The optimal RC is RC6: profit = −0.0885, highest = −0.0809, lowest = −0.0932, WR.WR2 = 0.0038, MA.MA3 = −0.0782, MTM.MTM = −0.0427, OSC.OSC = −0.0355, ROC.MAROC = 0.0105, SKDJ.D = −0.0268, BIAS-QL.BIAS = −0.01, WIDTH.WIDTH = 0.2408, CYD.CYDN = −0.0961, FSL.SWL = −0.0868ADTM.ADTM = −0.0379, ATR.ATR = −0.0278, DMA.DIFMA = −0.0358, DMI.ADX = 0.8516, DMI.ADXR = 0.854, EMV.EMV = −0.0942, VMACD.DIF = 0.3312, and UOS.MAUOS = −0.0846.2. Based on the deep learning model of the coupling and coordination relationship between the evolution of the spatial structure of the enterprise, the time-dependent matrix comparison experiment is divided into directed + self, directed, undirected + self, and undirected time for comparison. The experimental results on directed + self are the best; with various indicators, the upward improvement is above 10%: CP = 0.8611, CR = 0.9353, C–F1 = 0.8967, EP = 0.8865, ER = 0.857, E–F1 = 0.917, OP = 0.856, OR = 0.9845, and O–F1 = 0.99.3. The time cost, profit, and transaction volume data of the company are collected for a certain period of time, and simulation experiments are conducted to get a small difference between the predicted result and the actual data. The January data are closest to the true value: cost = 30.78, profit = 30.11, highest = 30.1, lowest = 29.7, WR.WR1 = 81.21, WR.WR2 = 45.62, AMV.AMV2 = 32.67, AMV.AMV3 = 34.95, and MCST. MCST = 36.08.4. In the model score, the best performance of LSTM data is CP = 0.3829, CR = 0.3664, C–F1 = 0.3744, EP = 0.3726, ER = 0.3004, E–F1 = 0.3326, OP = 0.9155, OR = 0.9316, and O–F1 = 0.9234, which is better than the BiLSTM model with CP = 0.3648, CR = 0.3319, C–F1 = 0.3392, EP = 0.4402, ER = 0.391, E–F1 = 0.4145, OP = 0.9215, OR = 0.9318, and O–F1 = 0.9266.

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Section: Coupling and Coordination Relationship Of The Evolution Of E...mentioning

confidence: 99%

Analysis of the Coupling and Coordination Relationship between the Evolution of Enterprise Spatial Structure and Economic Development Based on the Deep Learning Model

Wang

Yao

2022

Scientific Programming

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“…The accuracy of ANN model was 99.02% and 96.80% for the prediction of residual compressive and flexural strength values, respectively, while SVM model predicted these strength values with 97.01% and 91.60% accuracy, respectively. In the study of Tanyildizi et al, 32 the DL models were used to estimate the residual compressive strength and ultrasonic pulse velocity of concrete exposed to high temperature. The input variables were determined as binders, water–cement ratio, aggregates, superplasticizer, curing age, and temperature.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, many researchers [24][25][26][27][28][29][30][31][32] have focused on applying the machine learning (ML) model and deep learning (DL) model to estimate the mechanical properties of concrete. Al-Shamiri et al 24 devised ELM and ANN model to predict the compressive strength of high-strength concrete by using the input variables of cement, aggregates, water, and superplasticizer.…”

Section: Introductionmentioning

confidence: 99%

“…There have been much research [24][25][26][27][28][29][30][31][32] related to the prediction of the mechanical properties of mortar, concrete, SCC. Besides, several studies [33][34][35][36][37] are present about the water absorption of SCC, but according to the literature survey, it is believed that ML and DL based prediction studies about the capillary water absorption of fiberreinforced SCC have been very limited, and there are no published studies yet.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning and machine learning‐based prediction of capillary water absorption of hybrid fiber reinforced self‐compacting concrete

Kına

Türk

Tanyıldızı

2022

Structural Concrete

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Deep auto‐encoders and long short‐term memory methodology (LSTM) based on deep learning as well as support vector regression (SVR) and k‐nearest neighbors (kNN) based on machine learning models for the capillary water absorption prediction of self‐compacting concrete (SCC) with single and binary, ternary, and quaternary fiber hybridization were developed. A macro and two types of micro steel fibers having different aspect ratios, and PVA fiber were used. One hundred and sixty‐eight specimens produced from 24 mixtures were used in the prediction models. The input was the content of cement, fly ash, silica fume, fine and coarse aggregate, water, superplasticizer (SP), macro and micro steel fibers, PVA, time that the specimen was immersed in water, and splitting tensile strength. Water absorption was used as output. As per the ANOVA analysis of the experiment results, the most effective parameters were macro steel fiber and time for tensile strength and water absorption, respectively. Finally, binary hybridization of 1% macro steel fiber and PVA improved the splitting tensile strength while the use of PVA as binary, ternary, and quaternary fiber hybridization increased the water absorption of SCC specimens. The auto‐encoder, LSTM, SVR, and kNN models predicted the water absorption of fiber reinforced SCC with 99.99%, 99.80%, 94.57%, and 95.50% accuracy, respectively. The performance of deep autoencoder in the estimation of water absorption of fiber‐reinforced SCC was superior to the other prediction models.

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“…Concerning the development of artificial intelligence (AI), today there are more and more scientists and organizations researching and developing AI methods to handle complex problems [10][11][12][13]. For example, an artificial neural networks (ANNs) model was applied to predict the shear strength of reinforced concrete beams [14] and to predict the strength of rectangular CFST beam-columns [15], to analyze probabilistic pushover of reinforced concrete frame structures [16].…”

Section: Introductionmentioning

confidence: 99%

Prediction of axial strength in circular steel tube confined concrete columns using artificial intelligence

Ngo

Pham

Hoang

et al. 2021

STCE

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In recent years, together with the boom of the industrial revolution 4.0, terms such as artificial intelligence (AI) are gradually gaining popularity engineering domain. This study proposed a number of AI models for predicting the axial strength in circular steel tube confined concrete (STCC) columns. Particularly, artificial neural networks (ANNs), support vector regression (SVR), linear regression (LR), and M5P were applied in this study. This study applied 136 samples of short and intermediate STCC columns infilled with normal strength concrete, high strength concrete, or ultimate high strength concrete to evaluate the AI models. Compressive strengths of concrete cylinders was ranged from 23.20 Mpa to 188.10 Mpa. The AI models were assessed by statistical indexes including MAPE, MAE, RMSE, and R. The analytical results revealed that the M5P the most effective AI model comparing to others. Comparing with the other models, predicted data obtained by the M5P model show the highest agreement with the actual data in predicting the axial strength of STCC columns. Particularly, the MAPE and R of M5P models were 10.62% and 0.977 respectively. Similarly, the RMSE by the M5P model was 330.38 kN which is the lowest value among 419.39 kN by the LR model, 337.84 kN by the ANNs model, and 857.11 kN by the SVR model. Therefore, the M5P model can be considered as a useful tool to accurately predict the compressive capacity of the STCC columns. Keywords: artificial intelligence; circular steel tube confined concrete columns; axial strength; support vector regression.

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Deep learning model for estimating the mechanical properties of concrete containing silica fume exposed to high temperatures

Cited by 31 publications

References 41 publications

Analysis of the Coupling and Coordination Relationship between the Evolution of Enterprise Spatial Structure and Economic Development Based on the Deep Learning Model

Analysis of the Coupling and Coordination Relationship between the Evolution of Enterprise Spatial Structure and Economic Development Based on the Deep Learning Model

Deep learning and machine learning‐based prediction of capillary water absorption of hybrid fiber reinforced self‐compacting concrete

Prediction of axial strength in circular steel tube confined concrete columns using artificial intelligence

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