Article
Engineering, Geological
Andres Abarca, Ricardo Monteiro, Gerard J. O'Reilly
Summary: The lack of structural information on existing bridges poses a common challenge for engineers in regional seismic risk assessment. The reliance on incomplete exposure knowledge and macro taxonomy-based approaches in bridge risk studies results in unknown levels of uncertainty. This study demonstrates the importance of detailed structural information and suggests that machine learning models can outperform traditional approaches when sufficient inventory knowledge is available.
BULLETIN OF EARTHQUAKE ENGINEERING
(2022)
Article
Engineering, Geological
Sotiria P. Stefanidou, Elias A. Paraskevopoulos, Vassilis K. Papanikolaou, Andreas J. Kappos
Summary: This paper presents a toolkit for bridge-specific fragility analysis, which is implemented on an online platform. It includes software for online bridge-specific fragility curve derivation and an option to select generic fragility curves. Three alternative options for estimating component-specific limit state thresholds are provided, and details regarding the software and methodology for bridge-specific fragility curves are presented and discussed.
BULLETIN OF EARTHQUAKE ENGINEERING
(2022)
Article
Engineering, Geological
Gerard J. O'Reilly
Summary: This study explores the efficient assessment of simple bridge structures characteristic of the European context by examining different seismic intensity measures (IMs) such as Sa(T), peak ground velocity (PGV), and average spectral acceleration (AvgSa). The results show that peak ground acceleration (PGA) and peak ground velocity (PGV) are inefficient IMs compared to other IMs of similar complexity, especially at serviceability limit states, for the bridge structures evaluated. Meanwhile, AvgSa, which is based on a simple combination of Sa(T) values across a range of periods, exhibits very good predictive power and robustness in terms of risk estimates for structural response across all ranges.
BULLETIN OF EARTHQUAKE ENGINEERING
(2021)
Article
Engineering, Civil
Shuai Li, Jing-quan Wang, M. Shahria Alam
Summary: The study found that the novel bridge system with SMA-reinforced piers and SMA restrainers is more effective in reducing residual deformation and damage probability, compared to bridges with only SMA-reinforced piers or only SMA restrainers. However, the efficiency of bridges with only SMA-reinforced piers is lower, and bridges with only SMA restrainers increase the damage probability of piers.
EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS
(2021)
Article
Engineering, Civil
Miles Akbarnezhad, Mohammad Salehi, Reginald DesRoches
Summary: This paper evaluates the seismic fragility of a two-span reinforced concrete bridge with SMA-restrained rocking columns through ML techniques. The effects of design parameters and ambient temperature on the seismic performance of SRR columns are investigated. Multi-parameter fragility functions are developed and compared with other bridge designs. The results show that increasing SMA link strain and decreasing self-centering coefficient can reduce overall bridge damage.
Article
Construction & Building Technology
Ye Xia, Xiaoming Lei, Peng Wang, Limin Sun
Summary: This study proposes a comprehensive data-driven framework for network-level bridge condition assessment. By analyzing periodic bridge inspection reports in China, the future condition of bridges can be predicted, and maintenance strategies can be optimized to meet economic and performance constraints.
STRUCTURAL CONTROL & HEALTH MONITORING
(2022)
Article
Multidisciplinary Sciences
Fevzi Saritas
Summary: This paper evaluates the seismic performance of an isolated reinforced concrete bridge under different earthquakes using nonlinear analyses. The results show that the bridge meets the performance requirements for strong quakes, but the deformation of the isolators exceeds the allowable limits. It is also shown that the user-defined plastic hinge model can represent the hinging mechanism and yield reasonable results according to code regulations.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2022)
Article
Engineering, Civil
Deepak Kumar Sahu, Pradip Sarkar, Robin Davis, Sujith Mangalathu
Summary: This paper presents a new nonstatistical metamodel-based approach for assessing the seismic response of reinforced concrete box girder bridges. By using high-dimensional model representation and considering the uncertain input variables, simplified seismic fragility curves are developed, resulting in reduced computational effort.
JOURNAL OF BRIDGE ENGINEERING
(2022)
Article
Acoustics
Ming Ma, Xiaoguang Wang, Naidong Liu, Shuai Song, Shuai Wang
Summary: This paper proposes a method of overall seismic vulnerability analysis using nested copula model, considering the correlations between multiple components. By simulating the correlations between multiple components, the overall seismic vulnerability of multispan bridge system can be accurately assessed.
SHOCK AND VIBRATION
(2022)
Article
Engineering, Civil
Ji-Gang Xu, De-Cheng Feng, Sujith Mangalathu, Jong-Su Jeon
Summary: This paper proposes an approach for regional seismic performance assessment of reinforced concrete bridges using machine-learning techniques. The results show that the extreme gradient boosting (XGBoost) model has the best performance, accurately predicting the damage states of the bridges.
EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS
(2022)
Article
Environmental Sciences
Ye Xia, Xiaoming Lei, Peng Wang, Limin Sun
Summary: This paper proposes an artificial intelligence-based methodology for condition assessment of regional bridges, which includes data integration, condition assessment, and maintenance optimization. The regional bridge condition deterioration model is established by neural networks, and the impact of the maintenance scheme on the future condition of bridges is quantified.
Article
Engineering, Civil
Ji-Gang Xu, Zhong-Kui Cai, De-Cheng Feng
Summary: The paper presents seismic fragility analysis of aging RC bridges in a life-cycle context considering failure mode shift of the bridge columns. A numerical model capturing shear capacity deterioration and flexure-shear coupling behaviors of the corroded columns is developed and validated with experimental tests. The results highlight the importance of considering the potential shear failure of the bridge columns in life-cycle seismic performance assessment of aging RC bridges.
ENGINEERING STRUCTURES
(2021)
Article
Engineering, Civil
Yong Huang, Jing He, Zhihui Zhu
Summary: This study uses machine learning and empirical vulnerability curves to predict earthquake damage and assess risk for railway bridges, creating a rapid risk assessment procedure. By collecting and tallying seismic damage data from 335 damaged railway bridges in the Tangshan and Menyuan earthquakes, six variables were identified that had a significant impact on seismic risk outcomes, and the damage levels were categorized into five categories. Four algorithms were tested, with Random Forest (RF) being the most effective method. The study describes in detail the rapid assessment procedure for 269 randomly selected bridges, including predicting the seismic damage rating using RF and drawing the empirical vulnerability curve using a two-parameter normal distribution function. The findings of this study can guide the selection of machine learning approaches and inputs to build a rapid assessment model for railway bridges.
INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS
(2023)
Article
Construction & Building Technology
Mengdie Chen, Sujith Mangalathu, Jong-Su Jeon
Summary: Transportation networks are crucial for emergency response and recovery, but they can be disrupted by seismic hazards. This study proposes a computationally efficient method using machine learning techniques to evaluate the seismic reliability of bridge networks, providing information for ranking bridges and prioritizing retrofit plans.
JOURNAL OF STRUCTURAL ENGINEERING
(2022)
Article
Environmental Sciences
Yefeng Jiang, Qihao You, Xueyao Chen, Xiaolin Jia, Kang Xu, Qianqian Chen, Songchao Chen, Bifeng Hu, Zhou Shi
Summary: An accurate and inexpensive preliminary risk assessment framework for industrial enterprise sites in the Yangtze River Delta region was proposed in this study. The framework integrated text and spatial analyses, machine learning, and source-pathway-receptors, and was validated with a large number of samples. The results showed that the random forest model performed well for risk assessment, and provided a preliminary risk ranking for industrial enterprise sites in the region.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Construction & Building Technology
Bilal Ahmed, Sujith Mangalathu, Jong-Su Jeon
Summary: The study introduces a novel stacked long short-term memory (LSTM) network for early and accurate damage evaluation after earthquakes, reducing training time and enhancing prediction accuracy by overlapping data and generating new features.
JOURNAL OF BUILDING ENGINEERING
(2022)
Article
Engineering, Civil
Ji-Gang Xu, De-Cheng Feng, Sujith Mangalathu, Jong-Su Jeon
Summary: This paper proposes an approach for regional seismic performance assessment of reinforced concrete bridges using machine-learning techniques. The results show that the extreme gradient boosting (XGBoost) model has the best performance, accurately predicting the damage states of the bridges.
EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS
(2022)
Article
Engineering, Civil
Mengdie Chen, Sujith Mangalathu, Jong-Su Jeon
Summary: This paper highlights the importance of transportation networks in post-earthquake emergency response and recovery. It proposes a novel seismic reliability indicator, the travel time and connectivity reliability (TTCR), and a framework for computing the TTCR of transportation networks. Furthermore, it suggests a retrofit plan based on the TTCR to reduce the seismic risk of transportation networks.
EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS
(2022)
Article
Construction & Building Technology
Mengdie Chen, Sujith Mangalathu, Jong-Su Jeon
Summary: Transportation networks are crucial for emergency response and recovery, but they can be disrupted by seismic hazards. This study proposes a computationally efficient method using machine learning techniques to evaluate the seismic reliability of bridge networks, providing information for ranking bridges and prioritizing retrofit plans.
JOURNAL OF STRUCTURAL ENGINEERING
(2022)
Article
Construction & Building Technology
Chang Seok Lee, Jong-Su Jeon
Summary: This study derived empirical drift limit state expressions for rectangular concrete columns reinforced with nickel-titanium SMA bars and examined the influential parameters on ductility of the columns. It also investigated the precondition for ductile behavior of the SMA-reinforced concrete columns. Additionally, nonlinear static and dynamic analyses were conducted for an SMA-reinforced concrete building frame to demonstrate the application of the proposed drift LSs.
JOURNAL OF BUILDING ENGINEERING
(2022)
Article
Engineering, Civil
Seong-Hoon Hwang, Mehran Shokrabadi, Sujith Mangalathu, Jong-Su Jeon
Summary: The structural analysis of steel moment-resisting frame buildings often overlooks the participation of interior gravity frames and composite floor slab action in their lateral strength and stiffness, as well as the effects of aftershocks. This study investigates the impact of structural modeling assumptions and the inclusion of aftershocks on the seismic vulnerability and time-dependent lifetime seismic risk of a group of steel moment-resisting frame buildings.
Article
Instruments & Instrumentation
Eunsoo Choi, Alireza Ostadrahimi, Jong-Han Lee, Jong-Su Jeon
Summary: This paper investigates the efficiency of prestressing effect on the flexural performance of reinforced mortar beams through different heating methods. Internal heating sources using electric current were found to be faster and more uniform than external heating sources, resulting in a higher potential capacity and enhanced ductility and toughness of the composites.
SMART MATERIALS AND STRUCTURES
(2022)
Article
Engineering, Civil
Bilal Ahmed, Sujith Mangalathu, Jong-Su Jeon
Summary: In order to organize accurate and effective emergency responses after an earthquake, an early and precise assessment of damage to structures is vital. The use of fragility/vulnerability curves is an advanced approach for structural damage assessments. However, the analysis based on these curves can vary significantly depending on soil conditions, ground motion, and structural characteristics. To address this issue, a stacked long short-term memory network is proposed in this research.
EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS
(2023)
Article
Engineering, Civil
Nadia Saleem, Sujith Mangalathu, Bilal Ahmed, Jong-Su Jeon
Summary: This study presents a novel approach that integrates a geographic information system with a spatial data analysis-based machine learning PGA prediction model, achieving accurate results in predicting peak ground motion parameters and assessing seismic vulnerability of bridges.
EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS
(2023)
Article
Engineering, Civil
Mengdie Chen, Sujith Mangalathu, Jong-Su Jeon
Summary: This paper proposes a methodology based on origin-destination pair betweenness centrality to rank bridges in a complex network according to their importance. The methodology considers user equilibrium and topology, and significantly reduces computational time. The study also suggests two strategies, retrofitting bridges and constructing new ones, to mitigate the seismic risk of bridge networks.
ENGINEERING STRUCTURES
(2023)
Article
Engineering, Civil
Chang Seok Lee, Jong-Su Jeon
Summary: This paper proposes a risk-based seismic design method using shape memory alloy wires to enhance the performance of older concrete bridge classes. It adopts an existing SMA-SCB system to examine further applications to nonductile bridges. A sophisticated finite element model, probabilistic bridge models, and the total probability theorem are employed to develop the risk-based seismic design of the SMA-SCB.
ENGINEERING STRUCTURES
(2023)
Article
Materials Science, Multidisciplinary
Bilal Ahmed, Taehyo Park, Jong-Su Jeon
Summary: This study utilizes machine learning techniques to predict the maximum vertical displacement of reinforced concrete slabs subjected to air-blast loading. Different convolutional neural networks were trained using images transformed from tabular data, and most models demonstrated promising results.
INTERNATIONAL JOURNAL OF DAMAGE MECHANICS
(2023)
Article
Instruments & Instrumentation
Eunsoo Choi, Jong-Su Jeon, Jong-Han Lee
Summary: This study investigates the self-centering capacity of RC columns with martensitic SMA bars in the plastic hinge region through experiments. The results show that the RC column with SMA bars exhibits a plastic hinge around the couplers, demonstrating excellent self-centering capacity, but lower energy-dissipation capacity compared to conventional RC columns.
SMART MATERIALS AND STRUCTURES
(2023)
Article
Engineering, Civil
Chang Seok Lee, Jong-Su Jeon
Summary: This study introduces the seismic risk-based design of SMA devices for steel moment-resisting frame buildings. The traditional design method was found to have inconsistencies in response compared to actual test results. To address this issue, a seismic risk-based design approach was employed, using nonlinear time history analysis and optimization algorithm to determine the quantity of SMA devices, resulting in cost reduction and a significant decrease in the probability of collapse and demolition.
ENGINEERING STRUCTURES
(2023)
Article
Construction & Building Technology
Sergei Shturmin, Chang Seok Lee, Jong-Su Jeon
Summary: A simplified modeling approach based on nonlinear spring model is proposed to simulate the seismic behavior of concrete-filled steel tube columns. The approach was calibrated and validated using experimental data, and it showed better accuracy and reduced computational time compared to the existing fiber-based beam-column model.
JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH
(2023)