scholarly journals Development of ANN-based models to predict the bond strength of GFRP bars and concrete beams

2020 ◽  
Vol 71 (7) ◽  
pp. 814-827
Author(s):  
Nguyen Thuy Anh ◽  
Ly Hai Bang
Keyword(s):  
Bond Strength ◽  
Concrete Structures ◽  
Diameter Ratio ◽  
Design Solution ◽  
Transverse Reinforcement ◽  
Critical Element ◽  
Ann Model ◽  
Gfrp Bars ◽  
Ann Models ◽  
Bar Diameter

The use of glass fiber-reinforced polymer (GFRP) has gained increasing attention over the past decades, aiming at replacing traditional steel rebar in concrete structures, especially in corrosion or magnetic conditions. Understanding the working mechanism between the reinforcements and concrete is crucial in many practical applications, in which the corresponding bond strength is considered as a critical element. In this study, a database including 159 experimental beam results gathered from the available literature was used for the development of an artificial neural network (ANN) model in an effort to predict the bond strength between GFRP bars and concrete. Two ANN models using BFGS quasi-Newton backpropagation and conjugate gradient backpropagation with Polak-Ribiére algorithms were constructed and evaluated in terms of bond strength prediction accuracy. The considered database consisted of five input parameters, including the bar diameter, concrete compressive strength, minimum cover to bar diameter ratio, bar development length to bar diameter ratio, the ratio of the area of transverse reinforcement to the product of transverse reinforcement spacing, the number of developed bars and bar diameter. The evaluation of the models was conducted and compared using well-known statistical measurements, namely the correlation coefficient (R), root mean square error (RMSE), and absolute mean error (MAE). The results demonstrated that both ANN models could accurately predict the bond strength between GFRP bars and concrete, paving the way for engineers to possess a useful alternative design solution for reinforced concrete structures

Development of ANN-based models to predict the bond strength of GFRP bars and concrete beams

2020 ◽  
Vol 71 (7) ◽  
pp. 814-827
Author(s):  
Nguyen Thuy-Anh ◽  
Ly Hai-Bang
Keyword(s):  
Bond Strength ◽  
Concrete Structures ◽  
Diameter Ratio ◽  
Design Solution ◽  
Transverse Reinforcement ◽  
Critical Element ◽  
Ann Model ◽  
Gfrp Bars ◽  
Ann Models ◽  
Bar Diameter

The use of glass fiber-reinforced polymer (GFRP) has gained increasing attention over the past decades, aiming at replacing traditional steel rebar in concrete structures, especially in corrosion or magnetic conditions. Understanding the working mechanism between the reinforcements and concrete is crucial in many practical applications, in which the corresponding bond strength is considered as a critical element. In this study, a database including 159 experimental beam results gathered from the available literature was used for the development of an artificial neural network (ANN) model in an effort to predict the bond strength between GFRP bars and concrete. Two ANN models using BFGS quasi-Newton backpropagation and conjugate gradient backpropagation with Polak-Ribiére algorithms were constructed and evaluated in terms of bond strength prediction accuracy. The considered database consisted of five input parameters, including the bar diameter, concrete compressive strength, minimum cover to bar diameter ratio, bar development length to bar diameter ratio, the ratio of the area of transverse reinforcement to the product of transverse reinforcement spacing, the number of developed bars and bar diameter. The evaluation of the models was conducted and compared using well-known statistical measurements, namely the correlation coefficient (R), root mean square error (RMSE), and absolute mean error (MAE). The results demonstrated that both ANN models could accurately predict the bond strength between GFRP bars and concrete, paving the way for engineers to possess a useful alternative design solution for reinforced concrete structures

scholarly journals Artificial neural network for predicting the flexural bond strength of FRP bars in concrete

2019 ◽  
Vol 26 (1) ◽  
pp. 12-29 ◽  
Author(s):  
Mehmet Alpaslan Köroğlu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bond Strength ◽  
Diameter Ratio ◽  
Transverse Reinforcement ◽  
Ann Model ◽  
Type Specimens ◽  
Artificial Neural ◽  
Bar Diameter ◽  
Frp Bars

AbstractThe bond strength between fibre-reinforced polymer (FRP) rebars and concrete is one of the most significant aspects of composite behaviour for rebars and concrete. In this study, a database of 408 beam type specimens consisting of beam end specimens, beam anchorage specimens and splice beam specimens was compiled from the current literature and used to develop a simple prediction using the artificial neural network (ANN). The data used for modelling were organised in a format of eight input parameters that include FRP type, cover bar surface, confinement, bar diameter (db), concrete compressive strength $(\sqrt {{f_c}} )$, minimum cover-to-bar-diameter ratio (c/db), bar-development-length-to-bar-diameter ratio (l/db), and the ratio of the area of transverse reinforcement to the product of transverse reinforcement spacing, the number of developed bars and bar diameters (Atr/sndb). Additionally, a simple prediction formula by regression analysis was developed. The root mean square error and R2 values of the testing data were found in order to compare the results of both ANN and the proposed model with existing regulations. The new ANN model predicts the bond strength of FRP bars in reinforced concrete with 0.8989 R2, thus yielding better results when compared with existing regulations.

Axial monotonic and cyclic compression behaviour of hollow-bar micropiles

2015 ◽  
Vol 52 (4) ◽  
pp. 426-441 ◽  
Author(s):  
Osama F. El Hadi Drbe ◽  
M. Hesham El Naggar
Keyword(s):  
Bond Strength ◽  
Ground Improvement ◽  
Design Guidelines ◽  
Diameter Ratio ◽  
Load Test ◽  
Drill Bit ◽  
Type B ◽  
Load Tests ◽  
Drilling Bit ◽  
Bar Diameter

Micropiles are used in various applications, including low-capacity micropile networks, underpinning, and seismic retrofitting of existing foundations and high-capacity foundations for new structures. Hollow-bar micropiles have an added advantage, as they provide fast installation with a high degree of ground improvement. The current Federal Highway Administration (FHWA) design guidelines designate hollow-bar micropiles as type B, even though the FHWA construction technique is different than the technique used for typical type B, which results in an overly conservative design. In addition, the current practice for construction of hollow-bar micropiles is limited to a drilling bit / hollow-bar diameter ratio of 2.5 or less. In this paper, full-scale load tests were conducted to evaluate the suitability of FHWA design guidelines to hollow-bar micropiles installed in cohesive soil and to evaluate the performance of hollow-bar micropiles constructed with a drilling bit / hollow-bar diameter ratio of 3. Eight micropiles were constructed using 76 mm (3 in.) hollow bars (76 mm outside diameter and 48 mm inside diameter) with the air–water flushing technique and advanced to a depth of 5.75 m: six micropiles were installed using a 228 mm (9 in.) drill bit and two micropiles were installed using a 178 mm (7 in.) drill bit. All micropiles were instrumented with vibrating wire strain gauges to measure the axial strain at three stations along the micropile shaft. The load tests included four axial monotonic and four cyclic axial loading tests. The results are presented and discussed in terms of load–displacement curves and load transfer mechanism. The load test results showed that the grout–ground bond strength values proposed by the FHWA (in 2005) for type B micropiles grossly underestimate the bond strength for calculating the ultimate capacity. In addition, the toe resistance can be significant for micropiles resting on sand due to the increased toe diameter. No tangent stiffness degradation was observed in the micropile capacity after applying 15 load cycles.

Using genetic programming to model the bond strength of GFRP bars in concrete under the effects of design guidelines

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ying-Ji Chuang ◽  
Hsing-Chih Tsai
Keyword(s):  
Compressive Strength ◽  
Genetic Programming ◽  
Bond Strength ◽  
Design Guidelines ◽  
Concrete Compressive Strength ◽  
Design Guideline ◽  
Content Type ◽  
Gfrp Bars ◽  
Influence Parameter ◽  
Bar Diameter

Purpose This paper aims to use a derivative of genetic programming to predict the bond strength of glass fiber-reinforced polymer (GFRP) bars in concrete under the effects of design guidelines. In developing bond strength prediction models, this paper prioritized simplicity and meaningfulness over extreme accuracy. Design/methodology/approach Assessing the bond strength of GFRP bars in concrete is a critical issue in designing and building reinforced concrete structures. Findings Ultimately, the equation of a linear form of a particular design guideline was suggested as the optimal prediction model. Improvements to the current design guidelines suggested by this model include setting a 1.31 magnification and considering the effects of the three significant parameters of bar diameter (db), minimum cover-to-bar diameter (C/db) and development length to bar diameter (l/db) under an acceptable root mean square error accuracy of around 2 MPa. Furthermore, the model suggests that the original influence parameter of concrete compressive strength (fc) may be removed from bond strength calculations. Originality/value The model suggests that the original influence parameter of concrete compressive strength (fc) may be removed from bond strength calculations.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

Physical, mechanical, and durability characteristics of newly developed thermoplastic GFRP bars for reinforcing concrete structures

2021 ◽  
Vol 276 ◽  
pp. 122200
Author(s):  
Brahim Benmokrane ◽  
Salaheldin Mousa ◽  
Khaled Mohamed ◽  
Mahmoud Sayed-Ahmed
Keyword(s):  
Concrete Structures ◽  
Gfrp Bars

scholarly journals Using Multivariate Regression and ANN Models to Predict Properties of Concrete Cured under Hot Weather: A Case of Rawalpindi Pakistan

2021 ◽  
Vol 13 (18) ◽  
pp. 10164
Author(s):  
Ahsen Maqsoom ◽  
Bilal Aslam ◽  
Muhammad Ehtisham Gul ◽  
Fahim Ullah ◽  
Abbas Z. Kouzani ◽  
...  
Keyword(s):  
Construction Material ◽  
Pulse Velocity ◽  
Ann Model ◽  
Quadratic Regression ◽  
Split Tensile Strength ◽  
Curing Conditions ◽  
Curing Methods ◽  
Ann Models ◽  
Hot Weather ◽  
The Individual

Concrete is an important construction material. Its characteristics depend on the environmental conditions, construction methods, and mix factors. Working with concrete is particularly tricky in a hot climate. This study predicts the properties of concrete in hot conditions using the case study of Rawalpindi, Pakistan. In this research, variable casting temperatures, design factors, and curing conditions are investigated for their effects on concrete characteristics. For this purpose, water–cement ratio (w/c), in-situ concrete temperature (T), and curing methods of the concrete are varied, and their effects on pulse velocity (PV), compressive strength (fc), depth of water penetration (WP), and split tensile strength (ft) were studied for up to 180 days. Quadratic regression and artificial neural network (ANN) models have been formulated to forecast the properties of concrete in the current study. The results show that T, curing period, and moist curing strongly influence fc, ft, and PV, while WP is adversely affected by T and moist curing. The ANN model shows better results compared to the quadratic regression model. Furthermore, a combined ANN model of fc, ft, and PV was also developed that displayed higher accuracy than the individual ANN models. These models can help construction site engineers select the appropriate concrete parameters when concreting under hot climates to produce durable and long-lasting concrete.

Application of artificial neural network to control the coagulant dosing in water treatment plant

2000 ◽  
Vol 42 (3-4) ◽  
pp. 403-408 ◽  
Author(s):  
R.-F. Yu ◽  
S.-F. Kang ◽  
S.-L. Liaw ◽  
M.-c. Chen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Water Treatment ◽  
Regression Model ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Ann Model ◽  
Coagulant Dosage ◽  
Ann Models

Coagulant dosing is one of the major operation costs in water treatment plant, and conventional control of this process for most plants is generally determined by the jar test. However, this method can only provide periodic information and is difficult to apply to automatic control. This paper presents the feasibility of applying artificial neural network (ANN) to automatically control the coagulant dosing in water treatment plant. Five on-line monitoring variables including turbidity (NTUin), pH (pHin) and conductivity (Conin) in raw water, effluent turbidity (NTUout) of settling tank, and alum dosage (Dos) were used to build the coagulant dosing prediction model. Three methods including regression model, time series model and ANN models were used to predict alum dosage. According to the result of this study, the regression model performed a poor prediction on coagulant dosage. Both time-series and ANN models performed precise prediction results of dosage. The ANN model with ahead coagulant dosage performed the best prediction of alum dosage with a R2 of 0.97 (RMS=0.016), very low average predicted error of 0.75 mg/L of alum were also found in the ANN model. Consequently, the application of ANN model to control the coagulant dosing is feasible in water treatment.

Optimal Model Parameters of Inverse Kinematics Solution of a 3R Robotic Manipulator Using ANN Models

2020 ◽  
pp. 814-832
Author(s):  
Nikolaos E. Karkalos ◽  
Angelos P. Markopoulos ◽  
Michael F. Dossis
Keyword(s):  
Network Architecture ◽  
Inverse Kinematics ◽  
Input Data ◽  
Robotic Manipulator ◽  
Industrial Applications ◽  
Model Parameters ◽  
Ann Model ◽  
Inverse Kinematics Problem ◽  
Ann Models ◽  
And Training

Solution of inverse kinematics equations of robotic manipulators constitutes usually a demanding problem, which is also required to be resolved in a time-efficient way to be appropriate for actual industrial applications. During the last few decades, soft computing models such as Artificial Neural Networks (ANN) models were employed for the inverse kinematics problem and are considered nowadays as a viable alternative method to other analytical and numerical methods. In the current paper, the solution of inverse kinematics equations of a planar 3R robotic manipulator using ANN models is presented, an investigation concerning optimum values of ANN model parameters, namely input data sample size, network architecture and training algorithm is conducted and conclusions concerning models performance in these cases are drawn.

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