Prof. Dr. Ahad Amini Pishro

Civil Engineering, Sichuan University of Science and Engineering, Zigong, Sichuan, China

Prof. Dr. Ahad Amini Pishro

Civil Engineering, Sichuan University of Science and Engineering, Zigong, Sichuan, China

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Academic Career Length

9Years (2018-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 November 2020 in Construction and Building Materials

Journal Article

Comprehensive equation of local bond stress between UHPC and reinforcing steel bars

Ahad Amini Pishro

Xiong Feng

Yang Ping

Huang Dengshi

Reza S. Shirazinejad

27 September 2022 in Scientific Reports

Journal Article

Node, place, ridership, and time model for rail-transit stations: a case study

Ahad Amini Pishro

Qihong Yang

Shiquan Zhang

Mojdeh Amini Pishro

Zhengrui Zhang

WeiYu Li

Nowadays, Transit-Oriented Development (TOD) plays a vital role for public transport planners in developing potential city facilities. Knowing the necessity of this concept indicates that TOD effective parameters such as network accessibility (node value) and station-area land use (place value) should be considered in city development projects. To manage the coordination between these two factors, we need to consider ridership and peak and off-peak hours as essential enablers in our investigations. To aim this, we conducted our research on Chengdu rail-transit stations as a case study to propose our Node-Place-Ridership-Time (NPRT) model. We applied the Multiple Linear Regression (MLR) to examine the impacts of node value and place value on ridership. Finally, K-Means and Cube Methods were used to classify the stations based on the NPRT model results. This research indicates that our NPRT model could provide accurate results compared with the previous models to evaluate rail-transit stations.

23 July 2021 in Scientific Reports

Journal Article

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

Ahad Amini Pishro

Shiquan Zhang

Dengshi Huang

Feng Xiong

WeiYu Li

Qihong Yang

We 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.

12 July 2022 in Materials

Journal Article

Structural Performance of EB-FRP-Strengthened RC T-Beams Subjected to Combined Torsion and Shear Using ANN

This research study applied Artificial Neural Networks (ANNs) to predict and evaluate the structural responses of externally bonded FRP (EB-FRP)-strengthened RC T-beams under combined torsion and shear. Previous studies proved that, compared to reinforced concrete (RC) rectangular beams, RC T-beams performance in shear is significantly higher in structural analysis and design. The structural response of RC beams experiences a critical change while torsion moments are applied in load conditions. Fiber Reinforced Polymer (FRP) is used to retrofit the structural elements due to changing structural design codes and loadings, especially in earthquake-prone countries. We applied Finite Element Method (FEM) software, ABAQUS, to provide a precise numerical database of a set of experimentally tested FRP-retrofitted RC T-beams in previous research works. ANN predicted structural analysis results and Mean Square Error (MSE) and Multiple Determination Coefficients

(R^{2})

proved the accuracy of this study. The MSE values that were less than 0.0009 and

R^{2}

values greater than 0.9960 showed that the ANN precisely fits the data. The consistency between analyzed experimental and numerical results demonstrated the accurate implication of ANN, MSE, and

R^{2}

in predicting the structural responses of EB-FRP- strengthened RC T-beams.

29 October 2022 in Structures

Journal Article

UHPC-PINN-parallel micro element system for the local bond stress–slip model subjected to monotonic loading

Ahad Amini Pishro

Zhengrui Zhang

Mojdeh Amini Pishro

Feng Xiong

Lili Zhang

Siti Jahara Matlan

29 April 2022 in Materials

Journal Article

Structural Behavior of FRP-Retrofitted RC Beams under Combined Torsion and Bending

Fiber-reinforced polymers (FRPs) retrofit reinforced concrete (RC) structures. ABAQUS finite element software was used to perform numerical parametric analysis on a group of RC beams in this research. All specimens were retrofitted by FRP strips as an external retrofitting and experimentally tested up to previous researchers’ failure points. The range of subjects examined in these RC beams included cracking torque, ultimate torque, angle of twist, and the effect of using FRP on these subjects. We applied artificial neural networks (ANNs) to predict the structural behavior of RC beams under combined torsion and bending to develop the research accuracy. After testing, the ANN results were compared with the ABAQUS results. Consequently, a reasonable examination of the determined mathematical and trial results confirmed this study’s logical accuracy in predicting retrofitted RC beams’ structural behavior under combined loading.

About Me

Ahad Amini Pishro is an accomplished Associate Professor at Sichuan University of Science and Engineering, specializing in Structural Engineering. With a Doctor of Engineering degree from Sichuan University, he has held various academic and research positions throughout his career, including a Postdoctoral Research Fellowship at Southwest Jiaotong University. Dr. Pishro's current position as an Associate Professor allows him to actively pursue his research interests, which encompass Numerical Analysis, Analytical Modeling of Reinforced UHPC Structures, and the application of Artificial Neural Networks (ANNs) in solving structural engineering problems and Partial Differential Equations (PDE). His expertise also extends to Structural Dynamics, Seismic Analysis, and Transit-Oriented Development (TOD). Dr. Pishro holds verified international academic qualifications from World Education Services (WES) and serves as a reviewer for esteemed journals such as Springer Nature's International Journal of Concrete Structures and Materials, as well as several MDPI journals. He is a distinguished member of professional organizations of Canada including the CSCE, OIQ, and OSPE, and holds the EIT designation from Professional Engineers Ontario (PEO). Dr. Pishro's exceptional contributions have been acknowledged through awards, including Level A of Foreign Experts Affairs, Sichuan High-Level Foreign Talent (A3), and national and provincial scientific projects in China.

Prof. Dr. Ahad Amini Pishro

Prof. Dr. Ahad Amini Pishro

Research Focus & Metrics

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Representative Publications

About Me