Seismic Tests of Full Scale Reinforced Concrete T Joints with Light External Continuous Composite Rope Strengthening—Joint Deterioration and Failure Assessment

Karabini, Martha A.; Rousakis, Theodoros; Golias, Emmanouil; Karayannis, Chris G.

doi:10.3390/ma16072718

Cited by 9 publications

(5 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 14 shows the effect of different damage-control fuses on the ductility of the beam [30]. The structural design of the fuses greatly impacts the joints.…”

Section: Ductility Analysismentioning

confidence: 99%

See 1 more Smart Citation

Seismic Behavior of Demountable Reinforced Concrete (RC) Beam-to-Column Joints with Damage-Control Fuses

Li,

Fan,

Jiang

et al. 2023

Buildings

View full text Add to dashboard Cite

In this paper, a new type of assembled RC beam–column joint with a beam-end steel cover-plate connection is proposed to achieve seismic toughness and damage control of the joint. Energy-dissipation plates with different structural forms are proposed, and a series of seismic performance indexes of the joints are calculated and analyzed by using the finite element method. The energy-dissipation plate with an arc notch can reach the yield condition faster, and the ultimate bearing capacity of the joint reaches the maximum. The bending design of energy-dissipation plates is carried out by calculating the demand bending moment, and energy-dissipation plates of different structural forms are simulated and verified. The results show that the proposed design formula can ensure that the bending moment at the beam end still maintains elastic deformation when the energy-dissipation plate yields. The important parameters affecting the bending moment of the weakened part in the middle of the energy-dissipation plate are analyzed. Finally, this paper also analyzes the important parameters affecting the seismic performance of the joints. The results show that the seismic performance of the newly assembled RC beam–column joints proposed in this paper is better than that of cast-in-place joints. Increasing the longitudinal reinforcement ratio appropriately can greatly improve the ultimate bearing capacity and ductility of the joints. Increasing the thickness of the energy-dissipation plate, increasing the strength of the energy-dissipation plate, increasing the axial compression ratio of the column, increasing the strength of the concrete, and increasing the strength of the shear web can improve the ultimate bearing capacity of the joints but also reduce their ductility. Under different axial compression ratios, the strain in the core area of the joints is low, and the compressive damage of the concrete is zero, which verifies the effectiveness of the damage-control design of the proposed model.

show abstract

“…Figure 14 shows the effect of different damage-control fuses on the ductility of the beam [30]. The structural design of the fuses greatly impacts the joints.…”

Section: Ductility Analysismentioning

confidence: 99%

“…The yield load and peak load of each specimen were calculated using the Park method [29], and the results are shown in Figure 13 (Point A Figure 14 shows the effect of different damage-control fuses on the ductility of the beam [30]. The structural design of the fuses greatly impacts the joints.…”

Section: Ductility Analysismentioning

confidence: 99%

Seismic Behavior of Demountable Reinforced Concrete (RC) Beam-to-Column Joints with Damage-Control Fuses

Li,

Fan,

Jiang

et al. 2023

Buildings

View full text Add to dashboard Cite

show abstract

“…In this study, a representative example of the above category is investigated. The examined structure is an eight-story RC frame (Figure 2a) designed by Hatzigeorgiou and Liolios [91], only for gravity loads, without seismic provisions [92] or retrofitting [93][94][95][96][97][98]. Additionally, according to the design methodology adopted in Greece before 1984, this building lacks capacity design and shear walls.…”

Section: Description and Modelling Of The Examined Buildingmentioning

confidence: 99%

Interpretable Machine Learning for Assessing the Cumulative Damage of a Reinforced Concrete Frame Induced by Seismic Sequences

Lazaridis,

Kavvadias,

Demertzis

et al. 2023

Sustainability

View full text Add to dashboard Cite

Recently developed Machine Learning (ML) interpretability techniques have the potential to explain how predictors influence the dependent variable in high-dimensional and non-linear problems. This study investigates the application of the above methods to damage prediction during a sequence of earthquakes, emphasizing the use of techniques such as SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDPs), Local Interpretable Model-agnostic Explanations (LIME), Accumulated Local Effects (ALE), permutation and impurity-based techniques. Following previous investigations that examine the interdependence between predictors and the cumulative damage caused by a seismic sequence using classic statistical methods, the present study deploy ML interpretation techniques to deal with this multi-parametric and complex problem. The research explores the cumulative damage during seismic sequences, aiming to identify critical predictors and assess their influence on the cumulative damage. Moreover, the predictors contribution with respect to the range of final damage is evaluated. Non-linear time history analyses are applied to extract the seismic response of an eight-story Reinforced Concrete (RC) frame. The regression problem’s input variables are divided into two distinct physical classes: pre-existing damage from the initial seismic event and seismic parameters representing the intensity of the subsequent earthquake, expressed by the Park and Ang damage index (DIPA) and Intensity Measures (IMs), respectively. In addition to the interpretability analysis, the study offers also a comprehensive review of ML methods, hyperparameter tuning, and ML method comparisons. A LightGBM model emerges as the most efficient, among 15 different ML methods examined. Among the 17 examined predictors, the initial damage, caused by the first shock, and the IMs of the subsequent shock—IFVF and SIH—emerged as the most important ones. The novel results of this study provide useful insights in seismic design and assessment taking into account the structural performance under multiple moderate to strong earthquake events.

show abstract

Section: Description and Modelling Of The Examined Buildingmentioning

confidence: 99%

Interpretable Machine Learning for Assessing the Cumulative Damage of a Reinforced Concrete Frame Induced by Seismic Sequences

Lazaridis¹,

Kavvadias²,

Demertzis³

et al. 2023

Preprint

View full text Add to dashboard Cite

This study investigates the interpretability of machine learning (ML) models applied to cumulative damage prediction during a sequence of earthquakes, emphasizing the use of techniques such as SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDPs), Local Interpretable Model-agnostic Explanations (LIME), Accumulated Local Effects (ALE), Permutation and Impurity-based technique. The research explores the cumulative damage during seismic sequences, aiming to identify critical predictors and assess their influence on the cumulative damage. Moreover, the predictors contribution in respect with the range of final damage is evaluated. Nonlinear time history analyses are applied to extract the seismic response of an eight-story Reinforced Concrete (RC) frame. The regression problem’s input variables are divided into two distinct physical classes: pre-existing damage from the initial seismic event and seismic parameters representing the intensity of the subsequent earthquake, expressed by Park and Ang damage index (DIPA) and Intensity Measures (IMs), respectively. The study offers a comprehensive review of cutting-edge ML methods, hyperparameter tuning, and ML method comparisons. A LightGBM model emerges as the most efficient, among 15 different ML methods examined, with critical predictors for final damage being the initial damage caused by the first shock and the IMs of the subsequent shock: IFVF and SIH. The importance of these predictors is supported by feature importance analysis and local/global explanation methods, enhancing the interpretability and practical utility of the developed model.

show abstract

Seismic Tests of Full Scale Reinforced Concrete T Joints with Light External Continuous Composite Rope Strengthening—Joint Deterioration and Failure Assessment

Cited by 9 publications

References 38 publications

Seismic Behavior of Demountable Reinforced Concrete (RC) Beam-to-Column Joints with Damage-Control Fuses

Seismic Behavior of Demountable Reinforced Concrete (RC) Beam-to-Column Joints with Damage-Control Fuses

Interpretable Machine Learning for Assessing the Cumulative Damage of a Reinforced Concrete Frame Induced by Seismic Sequences

Interpretable Machine Learning for Assessing the Cumulative Damage of a Reinforced Concrete Frame Induced by Seismic Sequences

Contact Info

Product

Resources

About