IRO Journals

Journal of Artificial Intelligence and Capsule Networks

Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3

Blockchain-Enabled Federated Learning on Kubernetes for Air Quality Prediction Applications
Volume-3 | Issue-3

Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
Volume-3 | Issue-4

Deniable Authentication Encryption for Privacy Protection using Blockchain
Volume-3 | Issue-3

Hybrid Parallel Image Processing Algorithm for Binary Images with Image Thinning Technique
Volume-3 | Issue-3

Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare
Volume-3 | Issue-4

QoS-aware Virtual Machine (VM) for Optimal Resource Utilization and Energy Conservation
Volume-3 | Issue-3

Probabilistic Neural Network based Managing Algorithm for Building Automation System
Volume-3 | Issue-4

Fusion based Feature Extraction Analysis of ECG Signal Interpretation - A Systematic Approach
Volume-3 | Issue-1

Artificial Bee Colony Optimization Algorithm for Enhancing Routing in Wireless Networks
Volume-3 | Issue-1

Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
Volume-3 | Issue-4

Deniable Authentication Encryption for Privacy Protection using Blockchain
Volume-3 | Issue-3

Real Time Anomaly Detection Techniques Using PySpark Frame Work
Volume-2 | Issue-1

Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3

Audio Tagging Using CNN Based Audio Neural Networks for Massive Data Processing
Volume-3 | Issue-4

Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare
Volume-3 | Issue-4

Frontiers of AI beyond 2030: Novel Perspectives
Volume-4 | Issue-4

Early Stage Detection of Crack in Glasses by Hybrid CNN Transformation Approach
Volume-3 | Issue-4

Artificial Intelligence Algorithm with SVM Classification using Dermascopic Images for Melanoma Diagnosis
Volume-3 | Issue-1

An Efficient Machine Learning based Model for Classification of Wearable Clothing
Volume-3 | Issue-4

Home / Archives / Volume-4 / Issue-1 / Article-1

Volume - 4 | Issue - 1 | march 2022

Prediction on Flexural strength of High Strength Hybrid Fiber Self Compacting Concrete by using Artificial Intelligence
Boppana Narendra Kumar  , Pariyada Pradeep Kumar  257  173
Pages: 1-16
Cite this article
Kumar, B. N. & Kumar, P. P. (2022). Prediction on Flexural strength of High Strength Hybrid Fiber Self Compacting Concrete by using Artificial Intelligence. Journal of Artificial Intelligence and Capsule Networks, 4(1), 1-16. doi:10.36548/jaicn.2022.1.001
Published
20 January, 2022
Abstract

The primitive focus of this research work is about the artificial intelligence methods engaged for creating an outlook for flexural strength of High Strength Hybrid Fiber Self Compacted Concrete (HSHFSCC), which is considered to be a special concrete in order to tackle both workability and durability without disturbing the strength of the concrete. It possesses not only the good deformability during fresh state but also put forward high aversion to segregation resulting in superior homogeneity and increase in productivity by altering the period of construction. While incorporating various fibers like glass, steel, carbon, synthetic, and quartz powder in plain concrete, directs in the enhancement of post-cracking, toughness, ductility and limits the detrimental effect of shrinkage. The current work is classified into two stages. 1) Development of HSHFSCC and High Strength Self Compacting Concrete (HSSCC). 2) Engaging different Machine Learning (ML) models to divide the obtained information into Train, Test and Validation followed by 19 types of different ML regression models accompanied with Artificial Neural Network for engaging the function to appropriate the flexural strength of HSHFSCC and HSSCC. The boundary conditions discussed as input includes Setting time, percentage of quartz and river sand. Total 25 number of datasets are used for 5-fold cross validations by adopting MATLAB ML and Deep learning toolkit and Python is adopted to validate the optimized models. The evaluation factors like R-square and Root mean square show a great level of accuracy and reliability of the model.

Keywords

Artificial Neural Networks (ANN) flexural strength HSSFCC HSHFSCC modeling MATLAB

Full Article PDF Download Article PDF 
×

Currently, subscription is the only source of revenue. The subscription resource covers the operating expenses such as web presence, online version, pre-press preparations, and staff wages.

Subscription Payment Details

townscript (INR / USD): click here

Subscription Fee

Annual Subscription 15,000 INR / 200 USD
Subscription form: click here