Review on seismic performance of reinforced concrete interior and exterior beam-column joints
Beam-column joints represent critical structural components of a reinforced concrete structure. They play a pivotal role in maintaining the structural integrity of an infrastructure. Damage to the beam-column joint could result in partial collapse and even complete structural failure. Joint shea...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/177267 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-177267 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1772672024-05-24T15:34:52Z Review on seismic performance of reinforced concrete interior and exterior beam-column joints Nur Hanis Binti Mokhtar Li Bing School of Civil and Environmental Engineering CBLi@ntu.edu.sg Engineering Beam-column joints Seismic performance Reinforced concrete Beam-column joints represent critical structural components of a reinforced concrete structure. They play a pivotal role in maintaining the structural integrity of an infrastructure. Damage to the beam-column joint could result in partial collapse and even complete structural failure. Joint shear strength emerges as the paramount indicator for evaluating the structural integrity and efficacy of joint design, to ensure the robustness and reliability of the overall structural framework. This study aims to identify the best performing joint shear strength model that is capable of producing predicted joint shear strength with the greatest accuracy. Additionally, the study seeks to utilize the most appropriate machine learning model to estimate the damage index of the joint based on displacement. A comprehensive database of beam-column joint data collated over 33 research articles are utilized in the analysis of joint shear strength. The dataset includes essential parameters such as concrete compressive strength, yield strength of various reinforcement types, and the experimental joint shear strength that is used in the evaluation of predicted joint shear strength. Several machine learning methods were incorporated to estimate the damage index. The results were substantiated by two main error estimators RMSE and MAE which solidified Kassem’s joint shear strength model as the most accurate and reliable model alongside identifying Gradient Boosting as the optimal machine learning technique for forecasting damage index. Bachelor's degree 2024-05-24T07:08:48Z 2024-05-24T07:08:48Z 2024 Final Year Project (FYP) Mokhtar, N. H. (2024). Review on seismic performance of reinforced concrete interior and exterior beam-column joints. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/177267 https://hdl.handle.net/10356/177267 en application/pdf Nanyang Technological University |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
Engineering Beam-column joints Seismic performance Reinforced concrete |
spellingShingle |
Engineering Beam-column joints Seismic performance Reinforced concrete Nur Hanis Binti Mokhtar Review on seismic performance of reinforced concrete interior and exterior beam-column joints |
description |
Beam-column joints represent critical structural components of a reinforced concrete structure.
They play a pivotal role in maintaining the structural integrity of an infrastructure. Damage to the
beam-column joint could result in partial collapse and even complete structural failure. Joint shear
strength emerges as the paramount indicator for evaluating the structural integrity and efficacy of
joint design, to ensure the robustness and reliability of the overall structural framework. This
study aims to identify the best performing joint shear strength model that is capable of producing
predicted joint shear strength with the greatest accuracy. Additionally, the study seeks to utilize
the most appropriate machine learning model to estimate the damage index of the joint based on
displacement. A comprehensive database of beam-column joint data collated over 33 research
articles are utilized in the analysis of joint shear strength. The dataset includes essential
parameters such as concrete compressive strength, yield strength of various reinforcement types,
and the experimental joint shear strength that is used in the evaluation of predicted joint shear
strength. Several machine learning methods were incorporated to estimate the damage index. The
results were substantiated by two main error estimators RMSE and MAE which solidified
Kassem’s joint shear strength model as the most accurate and reliable model alongside identifying
Gradient Boosting as the optimal machine learning technique for forecasting damage index. |
author2 |
Li Bing |
author_facet |
Li Bing Nur Hanis Binti Mokhtar |
format |
Final Year Project |
author |
Nur Hanis Binti Mokhtar |
author_sort |
Nur Hanis Binti Mokhtar |
title |
Review on seismic performance of reinforced concrete interior and exterior beam-column joints |
title_short |
Review on seismic performance of reinforced concrete interior and exterior beam-column joints |
title_full |
Review on seismic performance of reinforced concrete interior and exterior beam-column joints |
title_fullStr |
Review on seismic performance of reinforced concrete interior and exterior beam-column joints |
title_full_unstemmed |
Review on seismic performance of reinforced concrete interior and exterior beam-column joints |
title_sort |
review on seismic performance of reinforced concrete interior and exterior beam-column joints |
publisher |
Nanyang Technological University |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/177267 |
_version_ |
1800916215767498752 |