scholarly journals Application of artificial neural networks and multiple linear regression on local bond stress equation of UHPC and reinforcing steel bars

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ahad Amini Pishro ◽  
Shiquan Zhang ◽  
Dengshi Huang ◽  
Feng Xiong ◽  
WeiYu Li ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
High Performance ◽  
High Performance Concrete ◽  
Bond Stress ◽  
Ultra High Performance Concrete ◽  
Steel Bar ◽  
Steel Bars ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

AbstractWe investigated the use of an Artificial Neural Network (ANN) to predict the Local Bond Stress (LBS) between Ultra-High-Performance Concrete (UHPC) and steel bars, in order to evaluate the accuracy of our LBS equation, proposed by Multiple Linear Regression (MLR). The experimental and numerical LBS results of specimens, based on RILEM standards and using pullout tests, were assessed by the ANN algorithm using the TensorFlow platform. For each specimen, steel bar diameters ($$d_{b} )$$ d b ) of 12, 14, 16, 18, and 20, concrete compressive strength ($$f_{c}^{\prime }$$ f c ′ ), bond lengths ($$L$$ L ), and concrete covers ($$C$$ C ) of $$d_{b}$$ d b , $$2d_{b}$$ 2 d b , $$3d_{b}$$ 3 d b and $$4d_{b}$$ 4 d b were used as input parameters for our ANN. To obtain an accurate LBS equation, we first modified the existing formula, then used MLR to establish a new LBS equation. Finally, we applied ANN to verify our new proposed equation. The numerical pullout test values from ABAQUS and experimental results from our laboratory were compared with the proposed LBS equation and ANN algorithm results. The results confirmed that our LBS equation is logically accurate and that there is a strong agreement between the experimental, numerical, theoretical, and the predicted LBS values. Moreover, the ANN algorithm proved the precision of our proposed LBS equation.

Study on Restrained Expansive Deformation of Steel Fiber Reinforced Self-Stressing Concrete

2008 ◽  
Vol 385-387 ◽  
pp. 305-308
Author(s):  
Huan An He ◽  
Cheng Kui Huang
Keyword(s):  
Prestressed Concrete ◽  
High Performance ◽  
Steel Fiber ◽  
High Performance Concrete ◽  
Steel Fibers ◽  
Fiber Reinforced ◽  
The Troubles ◽  
Steel Bar ◽  
Steel Bars ◽  
Steel Fiber Concrete

A new sort of high performance concrete is introduced which combines most advantages of prestressed concrete and steel fiber concrete, named steel fiber reinforced self-stressing concrete(SFFRSSC for short). Self-stressing concrete is actually a kind of expansive concrete which self-stresses, namely pre-compressive stresses, are induced by dint of some restrictions generally provided by steel bars to concrete expansion after hydration of expansive cement. As a result of chemical reaction, concrete archived prestresses by itself different from mechanical prestressed concrete, so called self-stressing concrete. By distributing short-cut steel fibers into self-stressing concrete at random, prestresses( self-stress) are created in concrete under combined restriction of steel bars and steel fibers. Thank to the pre-stresses tensile strength of concrete are significantly increased as well as cracking strength. In addition, expansive deformation of SFFRSSC can compensate the shrinkage of concrete to decrease shrinkage crack, and the steel fibers play an important role in post-crack behavior. On the other hand, self-stressing concrete can avoid the troubles of construction compared with conventional mechanical prestressed concrete. For purpose of understanding the properties of SFFRSSC, in this paper some researches were carried out to investigate the special expansive behaviors of restrained expansive deformation with restriction of steel bar as well as steel fiber. The test results indicated that steel bar and steel fiber both provide effective restrict to self-stressing concrete as result of forming prestresses in concrete.

Development on the Corrosion of Steel Fiber and Prevention in the Ultra-High Performance Concrete (UHPC)

2021 ◽  
Vol 1036 ◽  
pp. 358-370
Author(s):  
Zhen Wen Guo ◽  
Xin Zhi Duan ◽  
Qiang Wang ◽  
Si Jia Wang ◽  
Xiao Lu Guo
Keyword(s):  
High Performance ◽  
Steel Fiber ◽  
High Performance Concrete ◽  
Chloride Ions ◽  
Control Methods ◽  
Ultra High Performance Concrete ◽  
Hybrid Fibers ◽  
Steel Bar ◽  
Corrosion Of Steel

Chloride ions, water, and oxygen could cause the corrosion of steel fiber in the aggressive environment. The corrosion of steel fiber in UHPC is a long-term process and the rate is very slow. As one of the important components of ultra-high performance concrete (UHPC), the corrosion of steel fiber is the result of multiple factors. The characteristics of steel fiber corrosion in UHPC, the factors influencing the corrosion of steel fiber in UHPC (including nanomaterials, curing condition and crack width), and effects of steel fiber corrosion on the UHPC performance (including mechanical properties, matrix rehydration and corrosion of steel bar), are emphatically elaborated. And the control methods of steel fiber corrosion in UHPC are briefly introduced, i.e. hybrid fibers and stainless steel fibers.

scholarly journals Application of Multiple Linear Regression Models and Artificial Neural Networks on the Surface Ozone Forecast in the Greater Athens Area, Greece

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
K. P. Moustris ◽  
P. T. Nastos ◽  
I. K. Larissi ◽  
A. G. Paliatsos
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Surface Ozone ◽  
Daily Maximum ◽  
Linear Regression Models ◽  
Ann Model ◽  
Ozone Forecast ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Athens Area

An attempt is made to forecast the daily maximum surface ozone concentration for the next 24 hours, within the greater Athens area (GAA). For this purpose, we applied Multiple Linear Regression (MLR) models against a forecasting model based on Artificial Neural Network (ANN) approach. The availability of basic meteorological parameters is of great importance in order to forecast the ozone’s concentration levels. Modelling was based on recorded meteorological and air pollution data from thirteen monitoring sites within the GAA (network of the Hellenic Ministry of the Environment, Energy and Climate Change) over five years from 2001 to 2005. The evaluation of the performance of the constructed models, using appropriate statistical indices, shows clearly that in every aspect, the prognostic model by far is the ANN model. This suggests that the ANN model can be used to issue warnings for the general population and mainly sensitive groups.

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