scholarly journals Shear Strength Determination in RC Beams Using ANN Trained with Tabu Search Training Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Alireza Shahbazian ◽  
Hamidreza Rabiefar ◽  
Babak Aminnejad
Keyword(s):  
Shear Strength ◽  
Tabu Search ◽  
Shear Failure ◽  
Nonlinear Behavior ◽  
Critical Issue ◽  
Multiple Regression Model ◽  
Rc Beams ◽  
Ann Model ◽  
Factors Affecting ◽  
Search Training

The shear failure of reinforced concrete (RC) beams is a critical issue and has attracted the attention of researchers. The specific challenges of shear failure are the numerous factors affecting shear strength, the nonlinear behavior, and the nonlinear relationship between affecting parameters and the concrete properties. This study tackles this challenge by employing Artificial Neural Network (ANN) models. Since, according to No Free Lunch theorem, the performance of optimization algorithms is problem-dependent, this paper aims to assess the feasibility of modeling the shear strength of RC beams using ANNs trained with the Tabu Search Training (TST) algorithm. To this end, 248 experimental results were collected from the literature, and a feed-forward ANN model was employed to predict the shear strength. To assess its feasibility, the ANNs were also modeled using the Particle Swarm Optimization, and Imperialist Competitive Algorithms. As a traditional technique, the multiple regression model was also employed. The shear design equations of ACI-318-2019 were also investigated and compared with Tabu Search Trained ANN model. The analysis of results suggests the superiority of Tabu Search Trained ANNs in comparison to other suggested models in literature and the ACI-318-2019 design code.

scholarly journals CONTRIBUTION OF CONCRETE TO SHEAR STRENGTH OF RC BEAMS FAILING IN SHEAR

2013 ◽  
Vol 19 (3) ◽  
pp. 400-408 ◽  
Author(s):  
Guray Arslan ◽  
Zekeriya Polat
Keyword(s):  
Shear Strength ◽  
Shear Failure ◽  
Plastic Hinge ◽  
Transverse Reinforcement ◽  
Rc Beams ◽  
Earthquake Resistant ◽  
Earthquake Resistant Structures ◽  
Shear Failures ◽  
Beam Plastic Hinge ◽  
Shear Demand

Reinforced concrete (RC) beams with light transverse reinforcement are vulnerable to shear failure during seismic response. In order to prevent brittle shear failures at beam plastic hinge regions of earthquake-resistant structures, the Turkish Earthquake Code and ACI318 require the use of sufficient transverse reinforcement to resist the total expected shear demand. These codes tend to be excessively conservative and, in some cases, the contribution of the concrete to the shear strength is neglected. The aim of this study is to investigate the contribution of concrete to shear strength of RC beams failing in shear experimentally. The beams were tested under monotonically increasing reversed cyclic loading to determine the concrete contribution to shear strength. It is observed that the concrete contribution to the shear strength at ultimate state ranges from 18% to 69% of the ultimate strength.

Implication of Unbondedness in Reinforced Concrete Beams

2012 ◽  
Vol 587 ◽  
pp. 36-41 ◽  
Author(s):  
S.F.A. Rafeeqi ◽  
S.U. Khan ◽  
N.S. Zafar ◽  
T. Ayub
Keyword(s):  
Shear Strength ◽  
Reinforced Concrete ◽  
Failure Mode ◽  
Shear Failure ◽  
Concrete Beams ◽  
Experimental Results ◽  
Reinforced Concrete Beams ◽  
Rc Beams ◽  
Flexural Capacity ◽  
Flexural Reinforcement

In this paper, behaviour of nine (09) RC beams (including two control beams) after unbonding and exposing flexural reinforcement has been studied which were intentionally designed and detailed to observe flexural and shear failure. Beams have been divided into three groups based on failure mode and unbounded and exposed reinforcement. Beams have been tested under two-point loading up to failure. Experimental results are compared in terms of beam behaviour with respect to flexural capacity and failure mode which revealed that the exposed reinforcement does not altered flexural capacity significantly and unbondedness positively influences shear strength; however, serviceability performance of beams with unbonded and exposed reinforcement is less.

Shear strength predicting of FRP-strengthened RC beams by using artificial neural networks

2014 ◽  
Vol 21 (2) ◽  
pp. 239-255 ◽  
Author(s):  
Gunnur Yavuz ◽  
Musa Hakan Arslan ◽  
Omer Kaan Baykan
Keyword(s):  
Neural Networks ◽  
Shear Strength ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Fiber Reinforced Polymers ◽  
Rc Beams ◽  
Ann Model ◽  
Ultimate Shear Strength ◽  
Existing Building ◽  
Artificial Neural

AbstractIn this study, the efficiency of artificial neural networks (ANN) in predicting the shear strength of reinforced concrete (RC) beams, strengthened by means of externally bonded fiber-reinforced polymers (FRP), is explored. Experimental data of 96 rectangular RC beams from an existing database in the literature were used to develop the ANN model. Eight different input parameters affecting the shear strength were selected for creating the ANN structure. Each parameter was arranged in an input vector and a corresponding output vector that includes the shear strength of the RC beam. For all outputs, the ANN model was trained and tested using a three-layered back-propagation method. The initial performance of back-propagation was evaluated and discussed. In addition, the accuracy of well-known building codes in predicting the shear strength of FRP-strengthened RC beams was also examined, in a comparable way, using same test data. The study shows that the ANN model gives reasonable predictions of the ultimate shear strength of RC beams (R2≈0.93). Moreover, the study concludes that the ANN model predicts the shear strength of FRP-strengthened RC beams better than existing building code approaches.

scholarly journals From Exploratory Tunnel to Base Tunnel: Hard Rock TBM Performance Prediction by Means of a Stochastic Approach

2020 ◽  
Vol 53 (12) ◽  
pp. 5473-5487 ◽  
Author(s):  
Andrea Rispoli ◽  
Anna Maria Ferrero ◽  
Marilena Cardu
Keyword(s):  
Performance Prediction ◽  
Early Stage ◽  
Tunnel Boring Machine ◽  
Stochastic Approach ◽  
Critical Issue ◽  
Utilization Factor ◽  
Tbm Performance ◽  
Factors Affecting ◽  
Tunnel Section ◽  
Input Variables

AbstractTunnel boring machine (TBM) performance prediction is often a critical issue in the early stage of a tunnelling project, mainly due to the unpredictable nature of some important factors affecting the machine performance. In this regard, deterministic approaches are normally employed, providing results in terms of average values expected for the TBM performance. Stochastic approaches would offer improvement over deterministic methods, taking into account the parameter variability; however, their use is limited, since the level of information required is often not available. In this study, the data provided by the excavation of the Maddalena exploratory tunnel were used to predict the net and overall TBM performance for a 2.96 km section of the Mont Cenis base tunnel by using a stochastic approach. The preliminary design of the TBM cutterhead was carried out. A prediction model based on field penetration index, machine operating level and utilization factor was adopted. The variability of the parameters involved was analysed. A procedure to take into account the correlation between the input variables was described. The probability of occurrence of the outcomes was evaluated, and the total excavation time expected for the tunnel section analysed was calculated.

scholarly journals Application of Artificial Neural Networks for Predicting the Bearing Capacity of Shallow Foundations on Rock Masses

2021 ◽  
Author(s):  
M. A. Millán ◽  
R. Galindo ◽  
A. Alencar
Keyword(s):  
Bearing Capacity ◽  
Analytical Solutions ◽  
Shear Failure ◽  
Numerical Models ◽  
Computational Effort ◽  
Shallow Foundations ◽  
Geological Strength Index ◽  
Rock Masses ◽  
Ann Model ◽  
Artificial Neural

AbstractCalculation of the bearing capacity of shallow foundations on rock masses is usually addressed either using empirical equations, analytical solutions, or numerical models. While the empirical laws are limited to the particular conditions and local geology of the data and the application of analytical solutions is complex and limited by its simplified assumptions, numerical models offer a reliable solution for the task but require more computational effort. This research presents an artificial neural network (ANN) solution to predict the bearing capacity due to general shear failure more simply and straightforwardly, obtained from FLAC numerical calculations based on the Hoek and Brown criterion, reproducing more realistic configurations than those offered by empirical or analytical solutions. The inputs included in the proposed ANN are rock type, uniaxial compressive strength, geological strength index, foundation width, dilatancy, bidimensional or axisymmetric problem, the roughness of the foundation-rock contact, and consideration or not of the self-weight of the rock mass. The predictions from the ANN model are in very good agreement with the numerical results, proving that it can be successfully employed to provide a very accurate assessment of the bearing capacity in a simpler and more accessible way than the existing methods.

Machine learning-based failure mode identification of RCSPSW

2021 ◽  
Author(s):  
Dongqi Jiang ◽  
Shanquan Liu ◽  
Tao Chen ◽  
Gang Bi
Keyword(s):  
Machine Learning ◽  
Failure Mode ◽  
Failure Modes ◽  
Shear Failure ◽  
Tall Buildings ◽  
Shear Walls ◽  
Superior Performance ◽  
Ann Model ◽  
Mode Recognition ◽  
Wide Range

<p>Reinforced concrete – steel plate composite shear walls (RCSPSW) have attracted great interests in the construction of tall buildings. From the perspective of life-cycle maintenance, the failure mode recognition is critical in determining the post-earthquake recovery strategies. This paper presents a comprehensive study on a wide range of existing experimental tests and develops a unique library of 17 parameters that affects RCSPSW’s failure modes. A total of 127 specimens are compiled and three types of failure modes are considered: flexure, shear and flexure-shear failure modes. Various machine learning (ML) techniques such as decision trees, random forests (RF), <i>K</i>-nearest neighbours and artificial neural network (ANN) are adopted to identify the failure mode of RCSPSW. RF and ANN algorithm show superior performance as compared to other ML approaches. In Particular, ANN model with one hidden layer and 10 neurons is sufficient for failure mode recognition of RCSPSW.</p>

Factors Affecting NPAs in Indian Banking Sector

Paradigm ◽  
2021 ◽  
Vol 25 (2) ◽  
pp. 181-193
Author(s):  
Nitya Garg
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Banking Sector ◽  
Relative Weight ◽  
Multiple Regression Model ◽  
Factors Affecting ◽  
Weight Analysis ◽  
Specific Factors ◽  
Relative Weight Analysis ◽  
Bank Specific Factors

Banking sector is the backbone of any economy, so it is necessary to focus on its performance which is largely affected by its non-performing assets (NPAs). In the year 2018–2019, NPA of scheduled banks was Rs 355,076 Crore which is 3.7% of net advances. The purpose of this study is to identify the determinants based on analysis from previous literatures, and majorly macroeconomic and bank specific factors which are affecting NPAs using the relative weight analysis and to frame a model to predict future NPAs using multiple regression model using SPSS. The study also attempts to focus on actions and remedies that banks should make to control future NPAs. Findings of the study will act as a scaffolding for financial analysts and policymakers to prevent the conversion of its performing assets into NPAs and also help in proper management of banks and also in the recovery of economy.

Shear strength of RC beams subjected to axial load

2017 ◽  
Vol 15 (1) ◽  
pp. 32-48 ◽  
Author(s):  
T. Zhang ◽  
P. Visintin ◽  
D. J. Oehlers
Keyword(s):  
Shear Strength ◽  
Axial Load ◽  
Rc Beams
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