Description
This special publication draws inspiration from the Technical Session entitled “The Concrete Industry in the Era of Artificial Intelligence,” held during the ACI Virtual Concrete Convention in spring 2020. To parallel the Technical Session, this special publication is also tailored to showcase the unprecedented potential of leveraging artificial intelligence (AI) methods—including its derivatives of machine learning (ML) and deep learning (DL)—in the concrete industry as a whole.
The idea behind this effort started as a thought during an ACI Committee 216 meeting. From there, both ACI Committees 444 (Chair: Thomas Schumacher) and 554 (Chair: Liberato Ferrara) displayed interest in co-sponsoring this special publication. This special publication comprises fifteen papers (five from our panelists and ten received from authors representing academia and the concrete industry). This collection of papers covers the use of various AI techniques at the material level (i.e., concrete performance and mass-scale testing, property predictions, and optimization, etc.), elemental level (e.g., behavioral and capacity prediction of slabs, walls, beams, and anchorages, etc.), as
well as system level (viz. damage and crack detection of concrete bridges and concrete composite structures).
We are very thankful to ACI, the ACI Technical Activities Committee, as well as all three technical committees. Your kind support and commitment have not only allowed us to explore a new realm of possibilities but have also enabled us to set the stage towards a new and modern future to our industry. Special thanks go to our panelists and contributors who were very kind to share their most recent research and unique ideas pertaining to infusing AI solutions to various problems within our domain. In addition, we send our warm regards to our reviewers, ACI staff, and Ms. Barbara A. Coleman for her help in setting up and editing this effort.
Table of Contents
SP-350-1:
Damage Detection in Concrete Bridge T girders using 3D Finite Element Simulations Trained by Artificial Neural Network 1-15
Authors: AlaaEldin Abouelleil, Hayder A. Rasheed, and Eric Fletcheri
SP-350-2:
Machine Learning Driven Drift Capacity Model for Reinforced Concrete Walls 16-26
Authors: Muneera Aladsani, Henry Burton, Saman Abdullah, and John Wallace
SP-350-3:
State of the Art on Self-Healing Capacity of Cementitious Materials Based on Data Mining Strategies 27-44
Authors: Shashank Gupta, Salam Al-Obaidi, and Liberato Ferraral
SP-350-4:
Development of Fault-Detection ANNs for Structural Damage Prediction 45-53
Authors: Mohammad H. AlHamaydeh, Ahmed F. Mohamed, and Mahmoud I. Awad
SP-350-5:
Review of Artificial Neural Networks and A New Feed-Forward Network for Anchorage Analysis in Cracked Concrete 54-68
Authors: Salvio A. Almeida Jr. and Serhan Guner
SP-350-6:
Deep Neural Network to Predict Fire Resistance of FRP-Strengthened Beams 69-80
Authors: Bhatt, P.P. and Sharma, N.
SP-350-7:
Artificial Neural Network to Predict Bond Strength of Deformed Bars in Concrete 81-89
Authors: Vitaliy V. Degtyarev
SP-350-8:
Predicting the Compressive Strength Based in NDT Using Deep Learning 90-102
Authors: José A. Guzmán-Torres, Francisco J. Domínguez-Mota, Gerardo Tinoco-Guerrero, Elia M. Alonso-Guzmán, and Wilfrido Martínez-Molina
SP-350-9:
An Intelligently Designed AI for Structural Health Monitoring of a Reinforced Concrete Bridge 103-112
Authors: William R. Locke, Stefani C. Mokalled, Omar R. Abuodeh, Laura M. Redmond, and Christopher S. McMahan
SP-350-10:
Response Prediction of Ultra-High-Performance Concrete Beams using Machine Learning 113-122
Authors: Roya Solhmirzaei, Hadi Salehi, and Venkatesh Kodur
SP-350-11:
Regression-Based Surrogate Models for the Probabilistic Study of Fire Exposed Composite Structures Considering Tensile Membrane Action 123-131
Authors: Ranjit Kumar Chaudhary, Ruben Van Coile, and Thomas Gernay
SP-350-12:
Prediction of Shear Strength of One-Way Slabs Voided by Circular Paper Tubes using Artificial Intelligence 132-141
Author: Iman Mansouri, Chang-Hwan Lee, and Paul O. Awoyera
SP-350-13:
The Use of Machine Learning Algorithms and IoT Sensor Data for Concrete Performance Testing and Analysis 142-152
Authors: Andrew Fahim, Tahmid Mehdi, Ali Taheri, Pouria Ghods, Aali Alizadeh, and Sarah De Carufel
SP-350-14:
Artificial Intelligence for Real-Time Crack Detection of Ultra-High-Performance Concrete 153-166
Authors: Jung Wang, Chao Liu, and Yail J. Kim
SP-350-15:
Artificial Neural Network Utilization for Nondestructive Testing and Evaluation of Concrete Structures 167-177
Authors: Wael A. Zatar, M. Ammar Alzarrad, Tu T. Nguyen, and Hai D. Nguyen