Self-centring damper with multi-energy-dissipation mechanisms: Insights and structural seismic demand perspective

Life-cycle benefits estimation of self-centering building structures

The hybrid self‐centering braced frames (HSBFs) with shape memory alloy‐based braces (SMABs) and viscous dampers (VDs) are an emerging structural system developed for improving the seismic resilience of traditional braced frames (e.g., buckling restrained braced frames [BRBFs]) and self‐centering braced frames (SCBFs) by reducing residual inter‐story drifts (RIDs) and floor acceleration responses simultaneously. Nevertheless, the excellent performance of HSBFs is captured by introducing high initial construction costs. This paper aims to highlight the benefits of HSBFs compared with BRBFs and SCBFs by investigating the earthquake‐induced life‐cycle costs. To this end, 6‐story BRBF, SCBF, and HSBF are designed separately to achieve the same maximum inter‐story drifts under design basis earthquakes (DBE) for a fair comparison. The advanced numerical models of the considered systems were developed, and nonlinear dynamic analyses were conducted to obtain the structural seismic responses under various intensities. Monte Carlo simulation (MCS) was adopted for considering the modeling uncertainty. The life‐cycle costs were subsequently estimated based on the revised HAZUS loss estimation method. The influence of RIDs, initial construction costs of SMABs and VDs, and values of contents inside buildings were comprehensively investigated. It has been found that HSBF can achieve better performance in reducing seismic loss than BRBF and SCBF due to its excellent capability of controlling structural and nonstructural damage. SCBF may show higher seismic loss than BRBFs, given high RID limits for demolition or high building contents’ value, highlighting the importance of controlling floor acceleration responses of SCBFs. The life‐cycle benefits of BRBFs, SCBFs, and HSBFs are highly influenced by the RIDs, initial construction costs of SMABs and VDs, building contents’ values, and building's lifetime. HSBFs show better life‐cycle benefits than SCBFs when the building's lifetime is not lower than 50 years. Compared to BRBF, the life‐cycle cost effectiveness of SCBF can be only achieved when the initial cost of SMABs is lower than 1.21 times that of BRBs for the building with 50 years’ lifetime; the life‐cycle cost effectiveness of HSBF6 can be only achieved when the initial cost of SMABs or VDs is lower than 1.84 times that of BRBs for the building with 50 years’ lifetime. It has been concluded that reducing the initial costs of SMABs and VDs is critical and essential for increasing the life‐cycle economic benefits of SCBFs and HSBFs and promoting their real‐world applications.

Life‐cycle benefits estimation for hybrid seismic‐resistant self‐centering braced frames

Nowadays, due to improvements in seismic codes and computational devices, retrofitting buildings is an important topic, in which, permanent deformation of buildings, known as Residual Interstory Drift Ratio (RIDR), plays a crucial role. To provide an accurate yet reliable prediction model, 32 improved Machine Learning (ML) algorithms were considered using the Python software to investigate the best method for estimating Maximum Interstory Drift Ratio (IDRmax) and RIDR of 384 Steel Moment-Resisting Frames (SMRFs). In addition, the curve plot ability of methods was investigated to provide an estimation of Median of IDA curve (IDAMed) and Seismic Failure Probability curve (SFPCurve) considering Soil-Structure Interaction (SSI) effects. It is noteworthy that ML algorithms were improved with a pipeline-based hyper-parameters Fine-Tuning (FT) method followed by forward and backward feature selection methodologies to avoid overfitting and data leakage issues. The improved methods were evaluated to find the best prediction model regarding seismic demands. The results show that proposed methods have higher prediction accuracy and curve fitting ability (i.e. more than 95%) that can be used to estimate IDAMed and SFPCurve of a structure to accelerate the seismic risk assessment. A prediction tool is introduced to use the methods of this study for estimating abovementioned seismic demands. 

Machine learning-based prediction of residual drift and seismic risk assessment of steel moment-resisting frames considering soil-structure interaction

Additive manufacturing oriented parametric topology optimization design and numerical analysis of steel joints in gridshell structures

Machine learning-aided peak and residual displacement-based design method for enhancing seismic performance of steel moment-resisting frames by installing self-centering braces

Owing to their sufficient ductility, conventional steel moment-resisting frames (MRFs) are widely used as seismic-resistant systems in building structures. However, the ductile behavior introduces significant residual displacement after earthquakes, leading to massive economic losses caused by difficult or even unfeasible repairs. This paper intends to propose a machine learning-driven probabilistic residual displacement-based design method for retrofitting MRFs using self-centering braces (SCBs) and improving the post-earthquake repairability by reducing the residual displacement to a target level. The influence of the SCB's design parameters on the peak and residual displacements of the enhanced MRF are first investigated through parametric dynamic analysis of single-degree-of-freedom (SDOF) systems. The probabilistic residual displacement prediction models are developed using different machine learning algorithms. Analysis results indicate that the artificial neural network (ANN) models demonstrate the highest accuracy with the greatest coefficient of determination (R2) and lowest root mean squared error (RMSE). A software was developed based on the ANN models for predicting the probabilistic peak and residual displacement responses of the retrofitted MRF (RMRF). Then the proposed probabilistic residual displacement-based design steps are introduced. Finally, a three-story benchmark MRF is retrofitted using the proposed design method with two different performance objectives. Nonlinear static and dynamic analyses are conducted to investigate the seismic responses of the designed RMRFs. The analysis results show that the RMRFs can achieve the desired performance objective, confirming the effectiveness of the proposed design method. The SCB can significantly reduce the residual displacement of the RMRFs, where the RMRFs are fully recoverable (with a probability of 100%) without any need for repair after maximum considered earthquakes. 

Machine learning-driven probabilistic residual displacement-based design method for improving post-earthquake repairability of steel moment-resisting frames using self-centering braces

Explainable machine learning models for probabilistic buckling stress prediction of steel shear panel dampers

Machine learning-aided peak displacement and floor acceleration-based design of hybrid self-centering braced frames

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