Seismic retrofitting of a deteriorated RC building

Selim, Mohamed Fouad; Ibrahim, Yasser E.; Emara, Mohamed

doi:10.1016/j.cscm.2022.e01758

Cited by 3 publications

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because China is notorious for seismic disasters, strong earthquakes seriously threaten cities and communities since building collapse has historically been a significant factor in human fatalities and economic losses [2]. Reinforced concrete (RC) structure strengthening and retrofitting has been viewed as a joint research area for several reasons, including increasing the structural load-carrying capacity, mitigating environmental degradation, and satisfying the requirements of new design codes, particularly when it comes to seismic provisions [3].…”

Section: Introductionmentioning

confidence: 99%

Seismic Risk Assessment and Rehabilitation Method of Existing RCC Structures Using Micro Concrete

Yin,

Tang,

Chen

et al. 2023

Civ Eng J

View full text Add to dashboard Cite

Aging reinforced concrete (RC) building structures typically experience more severe damage and are prone to collapse during earthquakes, constituting a primary factor in casualties and direct economic losses. To enhance the seismic performance of these old structures, this paper proposes a seismic risk assessment and a micro-concrete restoration method. It applies the process to an existing three-story reinforced concrete structure. A practical framework for mitigating structural vulnerabilities in seismic-prone regions was proposed. Then an as-built survey was conducted to create as-built architectural and structural drawings. Concrete core tests, ferroscans, and rebar tests were also performed. Based on field surveys and test data, nonlinear static and dynamic analyses have been used to evaluate structural safety. Concrete column jacketing was used to strengthen weak existing columns with micro-concrete. In assessing the structural response of retrofitted buildings, a comparison was made to their initial state. The comparison shows that applying concrete column jacketing with micro concrete can reduce other structural elements' demand capacity ratio (DCR), minimize maximum displacements, and enhance overall stiffness. The results indicate that the proposed method effectively evaluates the seismic risk of aging structures and enhances seismic resilience in existing buildings. Moreover, the application to the actual structure demonstrates that micro-concrete is highly durable and compatible with parent-concrete. Doi: 10.28991/CEJ-2023-09-12-04 Full Text: PDF

show abstract

Section: Introductionmentioning

confidence: 99%

Seismic Risk Assessment and Rehabilitation Method of Existing RCC Structures Using Micro Concrete

Yin,

Tang,

Chen

et al. 2023

Civ Eng J

View full text Add to dashboard Cite

show abstract

A machine learning approach to predict demand-to-capacity ratio for reinforced concrete jacketing of columns in seismic-deficient buildings

Singh,

Dutta,

Agrawal

et al. 2024

J Build Rehabil

View full text Add to dashboard Cite

A machine learning approach to predict demand-to- capacity ratio for reinforced concrete jacketing of columns in seismic-deficient buildings

Singh,

Dutta,

Agrawal

et al. 2024

Preprint

View full text Add to dashboard Cite

Existing reinforced concrete (RC) buildings risk seismic damage because they were not constructed in compliance with seismic design standards and may have irregular mass distribution and construction defects. Typically, columns in these buildings are designed to withstand only gravity loads, making them vulnerable to damage or collapse during earthquakes. Retrofitting these columns using an RC jacket system is a standard way to enhance seismic resilience. However, conventional parametric modeling for RC jacketed structures using physics-based (finite element) modeling can be time-consuming and non-intuitive. To address this challenge, the present study proposes a novel data-driven machine-learning approach to predict RC jacketed columns' demand-to-capacity ratio (DCR), aiming for a reasonably accurate design with reduced computational time. Various design parameters related to RC column jacketing are considered when predicting the DCR. The datasets generated in post-processing are used to train Graphical Neural Network (GNN) and Gaussian Mixture Model (GMM). The dataset encompasses parameterization of design variables, including retrofit location, concrete compressive strength, cross-sectional dimensions, jacket thickness, longitudinal and transverse reinforcement areas, yielding reinforcement strength, and slenderness ratio. Subsequently, both models are fitted and evaluated against a test dataset to identify the optimal performer, using a multiple scorer performance index as the model evaluation metric. The analysis indicates that the GMM model emerges as the most suitable regressor for DCR estimation, exhibiting lower residual error than the GNN model.

show abstract

Seismic retrofitting of a deteriorated RC building

Cited by 3 publications

References 21 publications

Seismic Risk Assessment and Rehabilitation Method of Existing RCC Structures Using Micro Concrete

Seismic Risk Assessment and Rehabilitation Method of Existing RCC Structures Using Micro Concrete

A machine learning approach to predict demand-to-capacity ratio for reinforced concrete jacketing of columns in seismic-deficient buildings

A machine learning approach to predict demand-to- capacity ratio for reinforced concrete jacketing of columns in seismic-deficient buildings

Contact Info

Product

Resources

About