scholarly journals Bond Strength Assessment of Concrete-Corroded Rebar Interface Using Artificial Neutral Network

2020 ◽  
Vol 10 (14) ◽  
pp. 4724 ◽  
Author(s):  
Yi Wang ◽  
Zong Woo Geem ◽  
Kohei Nagai
Keyword(s):  
Bond Strength ◽  
Concrete Strength ◽  
Neutral Network ◽  
Machine Learning Techniques ◽  
Structural Safety ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Strength Assessment ◽  
Artificial Neutral Network ◽  
Spacing Ratio

Bond strength assessment is important for reinforced concrete structures with rebar corrosion since the bond degradation can threaten the structural safety. In this study, to assess the bond strength in concrete-corroded rebar interface, one of the machine learning techniques, artificial neutral network (ANN), was utilized for the application. From existing literature, data related to the bond strength of concrete and corroded rebar were collected. The ANN model was applied to understand the factors on bond property degradation. For the input in the ANN model, the following factors were considered the relative bond strength: (1) corrosion level; (2) crack width; (3) cover-to-diameter ratio; and (4) concrete strength. For the cases with confinement (stirrups), (5) the diameter/stirrups spacing ratio was also considered. The assessment was conducted from input with single parameter to multiple parameters. The scaled feed-forward multi-layer perception ANN model with the error back-propagation algorithm of gradient descent and momentum was found to match the experimental and computed results. The correlation of each parameter to the bond strength degradation was clarified. In cases without confinement, the relative importance was (1) > (2) > (4) > (3), while it was (2) > (1) > (3) > (5) > (4) for the cases with confinement.

Lightweight Concrete Strength Prediction by BP-ANN

2015 ◽  
Vol 1090 ◽  
pp. 101-106
Author(s):  
Kai Huang ◽  
Cheng Wei Zhong
Keyword(s):  
Lightweight Concrete ◽  
Concrete Strength ◽  
Back Propagation ◽  
Strength Prediction ◽  
Test Results ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Technical Literature ◽  
Model Based

The back propagation artificial neural networks (BP-ANN) use a resilient back-propagation algorithm and early stopping technique. By inputing the properties of geometries and material, NNs can predict the strength of lightweight concrete. An BP-ANN model based on feed-forward neural network is built, trained and tested using the available test data of 148 mix records collected from the technical literature. And the test results are compared and analyzed with experimental data . It shows that the strength of lightweight concrete obtained by the simplified model based on NNs are in good agreement with test results, and they are close to the experimental values. The NNs model can be used in the shear strength prediction and design for the strength of lightweight concrete.

PENGKAJIAN KEKUATAN BETON STRUKTUR JEMBATAN PASCA KEBAKARAN

2013 ◽  
Vol 12 (3) ◽  
Author(s):  
Sudarmadi Sudarmadi
Keyword(s):  
Compressive Strength ◽  
Concrete Strength ◽  
Ultrasonic Pulse Velocity ◽  
Ultrasonic Pulse ◽  
Pulse Velocity ◽  
Test Results ◽  
Concrete Compressive Strength ◽  
Strength Assessment ◽  
Concrete Testing

In this paper a case study about concrete strength assessment of bridge structure experiencing fire is discussed. Assessment methods include activities of visual inspection, concrete testing by Hammer Test, Ultrasonic Pulse Velocity Test, and Core Test. Then, test results are compared with the requirement of RSNI T-12-2004. Test results show that surface concrete at the location of fire deteriorates so that its quality is decreased into the category of Very Poor with ultrasonic pulse velocity ranges between 1,14 – 1,74 km/s. From test results also it can be known that concrete compressive strength of inner part of bridge pier ranges about 267 – 274 kg/cm2 and concrete compressive strength of beam and plate experiencing fire directly is about 173 kg/cm2 and 159 kg/cm2. It can be concluded that surface concrete strength at the location of fire does not meet the requirement of RSNI T-12-2004. So, repair on surface concrete of pier, beam, and plate at the location of fire is required.

scholarly journals Research on bond–slip performance between pultruded glass fiber-reinforced polymer tube and nano-CaCO3 concrete

2020 ◽  
Vol 9 (1) ◽  
pp. 637-649 ◽  
Author(s):  
Zhan Guo ◽  
Qingxia Zhu ◽  
Wenda Wu ◽  
Yu Chen
Keyword(s):  
Bond Strength ◽  
Concrete Strength ◽  
Fiber Reinforced Polymer ◽  
Glass Fiber Reinforced Polymer ◽  
Bond Slip ◽  
Ordinary Concrete ◽  
Bond Performance ◽  
Reinforced Polymer ◽  
Glass Fiber Reinforced ◽  
Push Out

AbstractThe article describes an experimental study on the bond–slip performance between the pultruded glass fiber-reinforced polymer (GFRP) tube and the nano-CaCO3 concrete. Taking the nano-CaCO3 concrete strength and GFRP tube thickness as primary parameters, nine specimens were designed and tested to study the influence of these parameters on the bond strength of the specimens. Besides, three specimens filled with the ordinary concrete were also tested by using the push-out tests to make comparisons with the bond performance of the specimens filled with nano-CaCO3 concrete. A total of four push-out tests were conducted on each specimen. The experimental results indicate that there are two types of axial load–slip curves for each specimen in four push-out tests. Moreover, comparison of the results of the push-out tests in the same direction shows that the bond failure load of the specimen decreases with the increase in the number of push-out tests. Based on the analysis of the test results, it is shown that the bond performance between the GFRP tube and the nano-CaCO3 concrete is better than that between the GFRP tube and the ordinary concrete. Furthermore, as the nano-CaCO3 concrete strength increases, the bond strength of the specimens decreases, indicating that the concrete strength has a negative effect on the bond strength. When the nano-CaCO3 concrete strength is relatively smaller (C20), the bond strength of the specimens decreases with the increase in the thickness of the GFRP tube. However, when the nano-CaCO3 concrete strength is relatively larger (C30 and C40), the bond strength of the specimens increases as the thickness of the GFRP tube increases.

scholarly journals Workflow to build a continuous static elastic moduli profile from the drilling data using artificial intelligence techniques

2021 ◽  
Author(s):  
Osama Siddig ◽  
Salaheldin Elkatatny
Keyword(s):  
Young’S Modulus ◽  
Elastic Moduli ◽  
Young's Modulus ◽  
Correlation Coefficients ◽  
Wellbore Stability ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Ann Model ◽  
Weight On Bit ◽  
Data Points

AbstractRock mechanical properties play a crucial role in fracturing design, wellbore stability and in situ stresses estimation. Conventionally, there are two ways to estimate Young’s modulus, either by conducting compressional tests on core plug samples or by calculating it from well log parameters. The first method is costly, time-consuming and does not provide a continuous profile. In contrast, the second method provides a continuous profile, however, it requires the availability of acoustic velocities and usually gives estimations that differ from the experimental ones. In this paper, a different approach is proposed based on the drilling operational data such as weight on bit and penetration rate. To investigate this approach, two machine learning techniques were used, artificial neural network (ANN) and support vector machine (SVM). A total of 2288 data points were employed to develop the model, while another 1667 hidden data points were used later to validate the built models. These data cover different types of formations carbonate, sandstone and shale. The two methods used yielded a good match between the measured and predicted Young’s modulus with correlation coefficients above 0.90, and average absolute percentage errors were less than 15%. For instance, the correlation coefficients for ANN ranged between 0.92 and 0.97 for the training and testing data, respectively. A new empirical correlation was developed based on the optimized ANN model that can be used with different datasets. According to these results, the estimation of elastic moduli from drilling parameters is promising and this approach could be investigated for other rock mechanical parameters.

scholarly journals Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 626
Author(s):  
Svajone Bekesiene ◽  
Rasa Smaliukiene ◽  
Ramute Vaicaitiene
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Back Propagation ◽  
Armed Forces ◽  
Activation Function ◽  
Optimal Number ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Classification Rate ◽  
Artificial Neural

The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training and testing of the ANN models. Using nonlinearity in stress research, numerous ANN structures were constructed and verified to limit the optimal number of neurons, hidden layers, and transfer functions. The highest accuracy was obtained by the multilayer perceptron neural network (MLPNN) with a 6-2-2 partition. A standardized rescaling method was used for covariates. For the activation function, the hyperbolic tangent was used with 20 units in one hidden layer as well as the back-propagation algorithm. The best ANN model was determined as the model that showed the smallest cross-entropy error, the correct classification rate, and the area under the ROC curve. These findings show, with high precision, that cohesion in a team and adaptation to military routines are two critical elements that have the greatest impact on the stress level of conscripts.

Structural Safety Assessment of a 1100 TEU Container Ship, Based on a Enhanced Long Term Fatigue Analysis

2014 ◽  
Vol 1036 ◽  
pp. 935-940
Author(s):  
Leonard Domnisoru ◽  
Ionica Rubanenco ◽  
Mihaela Amoraritei
Keyword(s):  
Safety Assessment ◽  
Safety Evaluation ◽  
Irregular Waves ◽  
Structural Safety ◽  
Random Wave ◽  
Wave Loads ◽  
Strength Assessment ◽  
Integrated Method ◽  
Hydroelastic Response

This paper is focused on an enhanced integrated method for structural safety assessment of maritime ships under extreme random wave loads. In this study is considered an 1100 TEU container test ship, with speed range 0 to 18 knots. The most comprehensive criteria for ships structural safety evaluation over the whole exploitation life is based on the long term ship structures analysis, that includes: stress hot-spots evaluation by 3D/1D-FEM hull models, computation of short term ship dynamic response induced by irregular waves, long term fatigue structure assessment. The analysis is enhanced by taking into account the ships speed influence on hydroelastic response. The study includes a comparative analysis on two scenarios for the correlation between the ships speed and waves intensity. The standard constant ship speed scenario and CENTEC scenario, with total speed loss at extreme waves condition, are considered. Instead of 20 years ship exploitation life estimated by classification societies rules from the long term structural safety criteria, the enhanced method has predicted more restrictive values of 14.4-15.7 years. The numerical analyses are based on own software and user subroutines. The study made possible to have a more realistic approach of ships structural strength assessment, for elastic and faster ships as container carriers, in compare to the standard one based only on naval rules, delivering a method with higher confidence in the designed structural safety.

AN ARTIFICIAL INTELLIGENT APPROACH TO TRAFFIC ACCIDENT ESTIMATION: MODEL DEVELOPMENT AND APPLICATION

Transport ◽  
2009 ◽  
Vol 24 (2) ◽  
pp. 135-142 ◽  
Author(s):  
Ali Payıdar Akgüngör ◽  
Erdem Doğan
Keyword(s):  
Genetic Algorithm ◽  
Model Development ◽  
Back Propagation ◽  
Linear Functions ◽  
Model Parameters ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Estimation Model ◽  
Average Growth ◽  
Feed Forward Back Propagation

This study proposes an Artificial Neural Network (ANN) model and a Genetic Algorithm (GA) model to estimate the number of accidents (A), fatalities (F) and injuries (I) in Ankara, Turkey, utilizing the data obtained between 1986 and 2005. For model development, the number of vehicles (N), fatalities, injuries, accidents and population (P) were selected as model parameters. In the ANN model, the sigmoid and linear functions were used as activation functions with the feed forward‐back propagation algorithm. In the GA approach, two forms of genetic algorithm models including a linear and an exponential form of mathematical expressions were developed. The results of the GA model showed that the exponential model form was suitable to estimate the number of accidents and fatalities while the linear form was the most appropriate for predicting the number of injuries. The best fit model with the lowest mean absolute errors (MAE) between the observed and estimated values is selected for future estimations. The comparison of the model results indicated that the performance of the ANN model was better than that of the GA model. To investigate the performance of the ANN model for future estimations, a fifteen year period from 2006 to 2020 with two possible scenarios was employed. In the first scenario, the annual average growth rates of population and the number of vehicles are assumed to be 2.0 % and 7.5%, respectively. In the second scenario, the average number of vehicles per capita is assumed to reach 0.60, which represents approximately two and a half‐fold increase in fifteen years. The results obtained from both scenarios reveal the suitability of the current methods for road safety applications.

Utilizing alkali-activated materials as ordinary Portland cement replacement to study the bond performance of fiber-reinforced polymer bars in seawater sea-sand concrete

2022 ◽  
pp. 136943322110651
Author(s):  
Ruiming Cao ◽  
Bai Zhang ◽  
Luming Wang ◽  
Jianming Ding ◽  
Xianhua Chen
Keyword(s):  
Tensile Strength ◽  
Bond Strength ◽  
Ordinary Portland Cement ◽  
Concrete Strength ◽  
Fiber Reinforced Polymer ◽  
Bond Performance ◽  
Reinforced Polymer ◽  
Sea Sand ◽  
Frp Bars ◽  
Alkali Activated

Alkali-activated materials (AAMs) are considered an eco-friendly alternative to ordinary Portland cement (OPC) for mitigating greenhouse-gas emissions and enabling efficient waste recycling. In this paper, an innovative seawater sea-sand concrete (SWSSC), that is, seawater sea-sand alkali-activated concrete (SWSSAAC), was developed using AAMs instead of OPC to explore the application of marine resources and to improve the durability of conventional SWSSC structures. Then, three types of fiber-reinforced polymer (FRP) bars, that is, basalt-FRP, glass-FRP, and carbon-FRP bars, were selected to investigate their bond behavior with SWSSAAC at different alkaline dosages (3%, 4%, and 6% Na2O contents). The experimental results manifested that the utilization of the alkali-activated binders can increase the splitting tensile strength ( ft) of the concrete due to the denser microstructures of AAMs than OPC pastes. This improved characteristic was helpful in enhancing the bond performance of FRP bars, especially the slope of bond-slip curves in the ascending section (i.e., bond stiffness). Approximately three times enhancement in terms of the initial bond rigidity was achieved with SWSSAAC compared to SWSSC at the same concrete strength. Furthermore, compared with the BFRP and GFRP bars, the specimens reinforced with the CFRP bars experienced higher bond strength and bond rigidity due to their relatively high tensile strength and elastic modulus. Additionally, significant improvements in initial bond stiffness and bond strength were also observed as the alkaline contents (i.e., concrete strength) of the SWSSAAC were aggrandized, demonstrating the integration of the FRP bars and SWSSAAC is achievable, which contributes to an innovative channel for the development of SWSSC pavements or structures.

Bond behavior of high strength concrete under reversed pull-out cyclic loading

2002 ◽  
Vol 29 (2) ◽  
pp. 191-200 ◽  
Author(s):  
M Alavi-Fard ◽  
H Marzouk
Keyword(s):  
Cyclic Loading ◽  
Bond Strength ◽  
High Strength Concrete ◽  
Concrete Strength ◽  
High Strength ◽  
Displacement Curve ◽  
Bond Slip ◽  
Strength Concrete ◽  
Pull Out ◽  
Normal Strength

Structures located in seismic zones require significant ductility. It is necessary to examine the bond slip characteristics of high strength concrete under cyclic loading. The cyclic bond of high strength concrete is investigated under different parameters, including load history, confining reinforcement, bar diameter, concrete strength, and the rate of pull out. The bond strength, cracking, and deformation are highly dependent on the bond slip behavior between the rebar and the concrete under cyclic loading. The results of cyclic testing indicate that an increase in cyclic displacement will lead to more severe bond damage. The slope of the bond stress – displacement curve can describe the influence of the rate of loading on the bond strength in a cyclic test. Specimens with steel confinement sustained a greater number of cycles than the specimens without steel confinement. It has been found that the maximum bond strength increases with an increase in concrete strength. Cyclic loading does not affect the bond strength of high strength concrete as long as the cyclic slip is less than the maximum slip for monotonic loading. The behavior of high strength concrete under a cyclic load is slightly different from that of normal strength concrete.Key words: bond, high strength, cyclic loading, bar spacing, loading rate, failure mechanism.

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