Prediction of Shear Strength of One-Way Slabs Voided by Circular Paper Tubes using Artificial Intelligence

2021 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Shear Strength ◽  
Circular Paper

scholarly journals A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 463
Author(s):  
Sparsh Sharma ◽  
Suhaib Ahmed ◽  
Mohd Naseem ◽  
Waleed S. Alnumay ◽  
Saurabh Singh ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Shear Strength ◽  
Geotechnical Engineering ◽  
Construction Cost ◽  
Cost Overrun ◽  
Risk Sensitive ◽  
Soil Shear Strength ◽  
External Forces ◽  
The Cost ◽  
Future Work

Ensuring soil strength, as well as preliminary construction cost and duration prediction, is a very crucial and preliminary aspect of any construction project. Similarly, building strong structures is very important in geotechnical engineering to ensure the bearing capability of structures against external forces. Hence, in this first-of-its-kind state-of-the-art review, the capability of various artificial intelligence (AI)-based models toward accurate prediction and estimation of preliminary construction cost, duration, and shear strength is explored. Initially, background regarding the revolutionary AI technology along with its different models suited for geotechnical and construction engineering is presented. Various existing works in the literature on the usage of AI-based models for the abovementioned applications of construction and maintenance are presented along with their advantages, limitations, and future work. Through analysis, various crucial input parameters with great impact on the estimation of preliminary construction cost, duration, and soil shear strength are enumerated and presented. Lastly, various challenges in using AI-based models for accurate predictions in these applications, as well as factors contributing to the cost-overrun issues, are presented. This study can, thus, greatly assist civil engineers in efficiently using the capabilities of AI for solving complex and risk-sensitive tasks, and it can also be used in Internet of things (IoT) environments for automated applications such as smart structural health-monitoring systems.

Shear strength of SFRCB without stirrups simulation: implementation of hybrid artificial intelligence model

2018 ◽  
Vol 36 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Abeer A. Al-Musawi ◽  
Afrah A. H. Alwanas ◽  
Sinan Q. Salih ◽  
Zainab Hasan Ali ◽  
Minh Tung Tran ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Shear Strength ◽  
Hybrid Artificial Intelligence

scholarly journals Application of Artificial Intelligence Methods to Estimate Shear Strength of Reinforced Concrete Shear Wall

2020 ◽  
Author(s):  
Hosein Naderpour ◽  
Mohammadreza Sharei ◽  
Pouyan Fakharian
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Shear Strength ◽  
Reinforced Concrete ◽  
Shear Walls ◽  
Shear Wall ◽  
Shear Capacity ◽  
Reinforcement Ratio ◽  
Transverse Reinforcement ◽  
Vertical Reinforcement

Shear walls are the type of structural systems that provide the lateral resistance to a building or structure. Lateral loads are applied on one plate and along the vertical dimension of the wall. These type of loads are usually transmitted to the wall collectors. Concrete shear walls have a considerable resistance to lateral seismic loading. Model prediction is required for the shear capacity of these walls to ensure the seismic security of the building. Therefore, a model is proposed to estimate the shear strength of concrete walls using an artificial intelligence algorithm. The input parameters of the neural network include the thickness of the reinforced concrete shear wall, the wall length, the vertical reinforcement ratio, the transverse reinforcement ratio, the compressive strength of the concrete, the stresses of the transverse reinforcement, the stresses of the vertical reinforcement, the ratio of the dimensions. The target parameter is the shear strength of the reinforced concrete shear wall. A total of 58 laboratory data was collected on concrete shear walls. The results of the research show that optimum artificial neural network with a specific number of hidden neurons can accurately estimate the shear capacity of reinforced concrete shear walls. The results indicate that the highest percentage of effect and the lowest percentage of effect have a target function. Additionally, the error rate obtained for predicting shear capacity is 7%, which is an acceptable error in this regard.

Artificial Intelligence

2009 ◽  
Author(s):  
David L. Poole ◽  
Alan K. Mackworth
Keyword(s):  
Artificial Intelligence
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