ALBERT

Description: Lateral stiffness of structural components, such as reinforced concrete (RC) columns, plays an important role in resisting the lateral earthquake loads. The lateral stiffness relates the lateral force to the lateral deformation, having a critical effect on the accuracy of the lateral seismic response predictions. The classical methods (e.g. fiber beam–column model) to estimate the lateral stiffness require calculations from section, element, and structural levels, which is time-consuming. Moreover, the shear deformation and bond-slip effect may also need to be included to more accurately calculate the lateral stiffness, which further increases the modeling difficulties and the computational cost. To reduce the computational time and enhance the accuracy of the predictions, this article proposes a novel data-driven method to predict the laterally seismic response based on the estimated lateral stiffness. The proposed method integrates the machine learning (ML) approach with the hysteretic model, where ML is used to compute the parameters that govern the nonlinear properties of the lateral response of target structural components directly from a training set composed of experimental data (i.e. data-driven procedure) and the hysteretic model is used to directly output the lateral stiffness based on the computed parameters and then to perform the seismic analysis. We apply the proposed method to predict the lateral seismic response of various types of RC columns subjected to cyclic loading and ground motions. We present the detailed model formulation for the application, including the developments of a modified hysteretic model, a hybrid optimization algorithm, and two data-driven seismic response solvers. The results predicted by the proposed method are compared with those obtained by classical methods with the experimental data serving as the ground truth, showing that the proposed method significantly outperforms the classical methods in both generalized prediction capabilities and computational efficiency.

Description: The PEER NGA-Sub ground-motion intensity measure database is used to develop new conditional ground-motion models (CGMMs), a set of scenario-based models, and non-conditional models to estimate the cumulative absolute velocity ([Formula: see text]) of ground motions from subduction zone earthquakes. In the CGMMs, the median estimate of [Formula: see text] is conditioned on the estimated peak ground acceleration ([Formula: see text]), the time-averaged shear-wave velocity in the top 30 m of the soil ([Formula: see text]), the earthquake magnitude ([Formula: see text]), and the spectral acceleration at the period of 1 s ([Formula: see text]). Multiple scenario-based [Formula: see text] models are developed by combining the CGMMs with pseudo-spectral acceleration ([Formula: see text]) ground-motion models (GMMs) for [Formula: see text] and [Formula: see text] to directly estimate [Formula: see text] given an earthquake scenario and site conditions. Scenario-based [Formula: see text] models are capable of capturing the complex ground-motion effects (e.g. soil non-linearity and regionalization effects) included in their underlying [Formula: see text]/[Formula: see text] GMMs. This approach also ensures the consistency of the [Formula: see text] estimates with a [Formula: see text] design spectrum. In addition, two non-conditional [Formula: see text] GMMs are developed using Bayesian hierarchical regressions. Finally, we present comparisons between the developed models. The comparisons show that if non-conditional GMMs are properly constrained, they are consistent with scenario-based GMMs. The [Formula: see text] GMMs developed in this study advance the performance-based earthquake engineering practice in areas affected by subduction zone earthquakes.

Description: Currently, conventional remediation of liquefaction triggering may have many environmental effects, and this important issue has led researchers to look for more sustainable methods. In this research, one of the new bio-improvement methods (biogas) has been used to generate gas bubbles within a soil, susceptible to liquefaction. Using this method, two bio materials create ammonium ions and carbonate, in which ammonium ion is converted into nitrate due to the presence of bacteria in water, and they are eventually converted to nitrogen gas in an anaerobic condition. The nitrogen bubbles created in water reduce the soil’s degree of saturation, which in effect increases the soil’s resistance to liquefaction occurrence. In this study, two sources of urease enzyme were used to reduce the soil degree of saturation. The effects of various parameters, including the optimum concentration of each substance for optimum time to generate gas bubbles, as well as the effect of the oxygen amount in water were investigated using monotonic triaxial tests. The results illustrated that the addition of the mentioned two substances to the oxab (water with 60 ppm oxygen) or tap water decreased the pore water pressure due to desaturation. Finally, the energy approach was used to test the substance containing the amount of oxab with the highest decrease in pore water generation, here called “optimum selection,” in the cyclic triaxial device, and the results were analyzed to evaluate liquefaction occurrence. The outcome of these results revealed that compared with the strain energy of the non-treated sample, the treated sample had a much higher strain energy; in other words, the treated sample needed a larger amount of loading to trigger liquefaction.

Description: This article investigates three different approaches to generate seismic input compatible with RotD100 design spectra: (1) separately matching each horizontal component to the target spectrum, (2) separately matching and then scaling-down the records to improve the match and (3) directly pursuing the match of RotD100 by simultaneously modifying both horizontal components. We examine the strong motion characteristics of the resulting records individually and their variability as suites of input records. The records generated, along with a set of amplitude-scaled records, are used as input for bi-directional non-linear response history analyses of idealized single column reinforced concrete bridge piers with different geometric and reinforcement characteristics. It is shown that the records generated pursuing a direct match of the target spectrum attain the closest match, retain better the strong motion characteristics of the seed records and their horizontal components exhibit a spectral variability comparable to suites of amplitude-scaled records. Regarding the effect on seismic response, the suites constructed separately matching each component consistently imposed larger peak inelastic and total energy demands than all other suites. Directly pursuing the match of RotD100 generated responses close but consistently below the expected from amplitude-scaled suites. The best results were obtained using the direct match methodology but using as target 110% the RotD100 spectrum as required in ASCE 7-16.

Description: Seismic risk evaluation studies for real estate portfolios conducted by technical professionals (often Civil and Structural Engineers) have become increasingly desirable and common in financial decisions. In this article, we develop a series of risk measures and ratings based on common outcomes from probabilistic portfolio seismic risk assessments. We first define two portfolio risk metrics: Portfolio Expected Loss (PELα) and Portfolio Upper Loss (PULα), where “α” is the annual exceedance probability, or the corresponding return period (“1/α”). PULα/PELα ratio characterizes the uncertainty in estimated portfolio risks which results from the uncertainty in seismic performance of the individual assets. Three uncertainty levels are defined, namely, low, medium, and high, based on the PULα/PELα. We then develop an asset risk metric, called Tail Contribution Index (TCIα), that characterizes the contribution of individual assets to the portfolio losses that fall within the high-consequence “tail” of the portfolio loss distribution. To describe the overall engineering efforts of a portfolio seismic risk study, we develop a portfolio risk metric, called Portfolio Level of Investigation (PLIα), that characterizes the effective level of engineering investigation. Three investigation levels are defined: low ( desktop), medium ( semi-engineered), and high ( engineered), based on the PLIα. Finally, based on the combination of uncertainty level and investigation level, we develop a rating scheme by which the quality (Qα) of a portfolio seismic risk study is characterized. Five quality levels are defined: very poor, poor, fair, good, and very good. These risk indices and ratings can help stakeholders and technical professionals better diagnose and communicate portfolio seismic risks, scope adequate studies, effectively utilize valuable resources, and base financial decisions on risk assessment results that have the desired reliability.

Description: This article proposes a new framework for rapid earthquake loss assessment based on a machine learning damage classification model and a representative sampling algorithm. A random forest classification model predicts a damage probability distribution that, combined with an expert-defined repair cost matrix, enables the calculation of the expected repair costs for each building and, in aggregate, of direct losses in the earthquake-affected area. The proposed building representation does not include explicit information about the earthquake and the soil type. Instead, such information is implicitly contained in the spatial distribution of damage. To capture this distribution, a sampling algorithm, based on K-means clustering, is used to select a minimal number of buildings that represent the area of interest in terms of its seismic risk, independently of future earthquakes. To observe damage states in the representative set after an earthquake, the proposed framework utilizes a local network of trained damage assessors. The model is updated after each damage observation cycle, thus increasing the accuracy of the current loss assessment. The proposed framework is exemplified using the 2010 Kraljevo, Serbia earthquake dataset.

Description: A framework for dynamically updating post-earthquake functional recovery forecasts is presented to reduce the epistemic uncertainty in the predictive model. A Bayesian Network (BN) model is used to provide estimates of the total recovery time, and a process-based discrete event simulation (PBDES) model generates forecasts of the complete recovery trajectory. Both models rely on component damage and duration-based input parameters that are dynamically updated using Bayes’ theorem, as information becomes available throughout the recovery process. The effectiveness of the proposed framework is demonstrated through an application to the pipe network of the City of Napa water distribution system. More specifically, pipe damage and repair data from the 2014 earthquake are used as a point of comparison for the dynamic forecasts. It is shown that, over time, the mean value of the total recovery duration generated by the BN-based model converges to the observed value and the dispersion is reduced. Also, despite a crude initial estimate, the median trajectory generated by the PBDES model provides a reasonable approximation of the observed recovery within 30 days following the earthquake. The proposed framework can be used by emergency managers to investigate the efficacy of post-event mitigation measures (e.g. crew allocation, resource prioritization) utilizing the most current data and knowledge.

Description: This article covers the impact of soil initial stress field heterogeneity (ISFH) in wave-passage analysis and in prescribed structural acceleration in the context of dynamic soil–structure interaction (DSSI) analysis. ISFH is directly related to the natural behavior of soil where a significant increase in net effective confinement, as is the case in the foundation soil under a building, tends to increase the soil’s modulus and strain. This creates a heterogeneous stress field in the vicinity of the foundation elements, which results in a modification of the dynamic behavior of the soil–structure system. A simple method for considering the impact of ISFH on the value of the soil’s modulus and strain was developed using the direct DSSI approach. The method was used to analyze numerical artifacts and its impact on the surface acceleration values of a nonlinear two-dimensional (2D) numerical soil deposit under transient loading. This analysis was followed by a sample application for a three-story, three-bay concrete moment-resisting frame structure erected on a deep soil deposit. Floor acceleration and relative displacement were used for comparison. The soil deposit was modeled using the typical geotechnical properties of fine-grained, post-glacial soil samples obtained in Eastern Canada from in situ geotechnical borehole drilling, geophysical surveys, and laboratory testing. Ground motion was based on eastern calibrated seismic signals. The results of the soil deposit analysis show that ISFH had a significant impact on surface acceleration values. The effect was found to be period-dependent and to have a direct impact on prescribed acceleration values at the base of structure. Thus, failure to take the effects of ISFH into consideration can lead to errors in calculating prescribed structural accelerations (i.e. over- or underestimation).