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This article aims at identifying the optimal scalar- and vector-valued intensity measures (IMs) for predicting liquefaction triggering and the consequence of liquefaction (i.e. permanent deformation of sloping grounds), respectively. A total of 169 ground motions were selected from the NGA-West2 database. Using the selected ground motions as input, one-dimensional dynamic analyses were conducted for generic sloping ground soil profiles consisting of a loose-sand liquefiable layer implemented in OpenSees. The excess pore water pressure ratio and the liquefaction-induced lateral displacement were regarded as the engineering demand parameters (EDPs) of interest. A total of 20 scalar-IMs and 21 vector-IMs representative of various characteristics of seismic loading were evaluated as candidate IMs. The criteria for identifying optimal IMs include consideration for the efficiency, sufficiency, and predictability of the given IMs. An error propagation approach was employed to evalute the overall uncertainty of EDPs under scenario earthquakes using different IMs as predictor variables. Based on the results of extensive comparative analyses, acceleration spectrum intensity (ASI), and the vector of peak ground acceleration and spectral acceleration at 0.3 s (peak ground acceleration (PGA) and Sa(0.3 s), respectively) are identified as the optimal scalar- and vector-IM for liquefaction triggering evaluation, respectively. However, Arias intensity ( Ia) and vectors of [ Ia, cumulative absolute velocity] and [PGA, Ia] are identified as the optimal scalar- and vector-IM for predicting the liquefaction-induced lateral displacements. The optimal IMs identified could be used in performance-based liquefaction hazard evaluations, to assess the liquefaction triggering and consequence of liquefiable sites.

The identification of thermal load/thermal shock of aircrafts during the service is beneficial for collecting information of the service environment and avoiding risks. In the paper, a method based on multivariate information fusion and physics-guided neural network is developed for the inverse problem of thermal load identification of honeycomb sandwich structures. Two thermal feature parameters: temperature gradient and temperature variation rate are used to build the dataset. A 16-layers physics-guided neural network is presented to achieve the predicted results consistent with physical knowledge. In the work, laser irradiation is used as the thermal load, and two laser parameters are to be identified, i.e., spot diameter, power. Simulations and experiments are conducted to verify the effectiveness of the proposed method. The effects of physics-guided loss function and multivariate information fusion are discussed, and it is found that the results based on the proposed method are much better than the results based on the method without physical model. Besides, results based on multivariate information fusion are better than results based on single temperature response. Then, the effects of network models and hyper parameters on the proposed method are also discussed.

With the rapid advancements of the mineral industry, the data generated by this industry chain have increased dramatically. To reduce the growing pressure of data storage and security risks, we design a credible on-chain and off-chain collaborative dual storage system that integrates blockchain technology and Interplanetary file system (IPFS), also construct a traceable and revocable multi-authority ciphertext-policy attributed-based encryption (CP-ABE) algorithm to meet the demand of privacy protection and dynamic fine-grained access control. Furthermore, the multi-authority layered authorization with a central authority model distributes system overhead while enabling the platform can be regulated. More importantly, our scheme achieves accurate trace of the malicious users by white-box traceability and capable of implementing indirect immediate user and attribute revocation without requiring key or ciphertext updates. Finally, the proposed scheme is indistinguishability under chosen-plaintext attack (IND-CPA) secure in the standard model. And the performance analysis demonstrates that our scheme is feature-rich, practical and efficient.

Ground motion intensity measures (IMs) were observed to be spatially correlated during past earthquakes. In this article, a new spatial cross-correlation model for a vector-IM, which consists of spectral acceleration (SA) ordinates at 17 periods and six non-SA IMs (e.g. peak ground velocity, Arias intensity, cumulative absolute velocity, and significant durations), is proposed using principal component analysis (PCA) and geostatistical analysis. A total of 3797 ground motion records are selected from the NGA-West2 database for such analyses. PCA is used to transform the spatially correlated within-event residuals into uncorrelated principal components; a permissible function is then proposed to fit the empirical semivariograms calculated by the principal components. It is evident that the proposed model performs well in capturing the spatial variability characteristics of the multiple ground motion IMs. A simple example is presented to illustrate the use of the proposed model in realizing spatially correlated ground motion residuals of multiple IMs. The model developed enables one to simulate spatially cross-correlated IMs over a large area in a rapid way.

In recent years, energy-based seismic design methodology has received increasing attention because it takes into account not only the force and displacement behavior of a structure but also the cumulative damage effect caused by seismic loading. Specifically, as a fundamental parameter, input energy parameters (both absolute and relative measures) are directly related to the cumulative damage potential; therefore, they are commonly used in energy-based seismic design and seismic risk assessment. This study thus proposes new spatial cross-correlation models for absolute and relative elastic input energy parameters, using 2219 ground-motion records selected from 12 earthquake events. The normalized within-event residuals for both absolute and relative measures are first calculated. Semivariogram analysis is then conducted to quantify the spatial correlation of residuals for the input energy parameters at multiple sites and multiple periods. The linear model of coregionalization (LMC) approach is adopted to fit the empirical data; it is observed that the proposed LMC-based function performs reasonably well in capturing the spatial variability of the input energy measures. The influence of regional site conditions on the spatial cross correlation of input energy parameters is also investigated, and generic models are proposed using the averaged standardized coregionalization matrices of 12 events. The spatial cross-correlation models developed for input energy parameters can be used in regional seismic risk assessment within an energy-based framework.

Multivariate normality of logarithmic intensity measures (IMs) is conventionally assumed in earthquake engineering applications. This article introduces a vine copula approach as a useful tool for multivariate modeling of IMs. This approach provides a flexible way to decompose a joint distribution into individual marginal distributions and multiple dependences characterized by a cascade of bivariate copulas (pair-copulas), whereas the conventional multivariate normality can be considered as a special case of the vine copula model. Based on the Next Generation Attenuation-West1 database and various combinations of ground-motion prediction equations (GMPEs), the optimal dependence structures among peak ground acceleration, peak ground velocity, and Arias intensity, as well as that for spectral accelerations at four periods, are identified. The joint normality assumption for the two vector sets of logarithmic IMs is examined from the perspective of copula theory. The results illustrate that the normality assumption is generally adequate for bivariate IMs but may not be optimal for multivariate IMs. Using the same set of GMPEs (developed by the same researchers) may improve the joint normality for logarithmic IMs. Furthermore, the impact of dependence structures among IMs on probabilistic seismic slope displacement hazard analysis is explored. The results indicate that using the same Pearson correlation coefficients but different dependence structures for IMs produces different hazard results and this difference is generally enlarged with increasing hazard levels. As hazard difference from different dependence structures is generally not significant, the multivariate normality distribution for logarithmic IMs is judged to be an acceptable assumption in engineering practice. Alternatively, engineers may make a choice between the joint normal distribution and the vine copula tool depending on the specific situation because of the better generality of the latter.

Seismic hazard assessment of metropolitan areas is an important topic in catastrophe risk management and disaster prevention. In this study, a probabilistic seismic hazard analysis (PSHA) is performed to estimate the seismic hazard for Singapore. The PSHA approach has been conducted based on the OpenQuake engine. An updated earthquake catalog for the neighboring region has been compiled from various sources, and three major source zones are identified using the updated earthquake catalog: Sumatra subduction zone, Sumatran strike–slip fault, and deformation–background zone. Also, 10 published ground motion prediction equations are incorporated in the logic-tree framework, which properly accounts for the epistemic uncertainty of ground motion intensity measures. The peak ground acceleration (PGA) values in the mean hazard maps at bedrock sites of Singapore for 10% and 2% exceedance probability in 50 years range from 0.04 to 0.05 g and from 0.14 to 0.15 g, respectively. The uniform hazard spectrum consisting of PGA and seven spectral accelerations is generated for rock sites in Singapore. Disaggregation analysis shows that the deformation zone identified contributes dominantly to the PGA hazard of Singapore, whereas the Sumatran strike–slip fault contributes dominantly to the long-period (spectral acceleration at 1.0 s) hazard. Besides, the seismic hazard maps for representative site conditions of Singapore are briefly presented.

Dispersion curve inversion is a key step of analyzing data from multichannel surface wave method (MASW) for investigating the shear wave velocity-depth (vs-h) profile. The profile is usually simplified to be a stratification model consisting of horizontal and homogenous layers. Model parameters include the number (N) and thicknesses (h) of layers and shear wave velocity (vs) in each layer. The N represents model complexity. The larger N value is, the more complex the model is. A model that is too complex is prone to overfitting. The opposite is true for too simple models that underfit. Mathematically, the dispersion curve inversion problem is ill-posed, that is, there are a number of stratification models of the vs-h profile with different N, h, and vs values resulting in identical, or at least, similar dispersion curves. Because the N value is usually unknown during the inversion, the model selection is necessary in which a model fitting well with data and proper model complexity is identified in a pool of competitive models.Nonetheless, research is rare that addresses the model selection issue in dispersion curve inversion problems. Bayesian framework can be used in model selection by quantifying the uncertainty in the stratification model. In this study, critical issues of Bayesian frameworks for quantifying the uncertainty in stratification model selection are discussed, including Bayesian inversion with a variable or a fixed number of layers in stratification models. Then, the major difficulties in computing the posterior distribution and Bayesian model evidence for determining N are demonstrated and discussed, which are mainly caused by the high-dimensional and highly nonlinear likelihood function. Finally, the sensitivity of model complexity to the error associated with the dispersion curve is explored preliminarily.

Permanent sliding displacement is a parameter that is used widely to evaluate the seismic performance of earthen slopes, and the inherent variability of soil strength parameters is consider...

This study quantitatively investigates the duration effect on structural collapse assessment, using hazard-consistent ground motion suites selected based on the generalized-conditional-intensity-measure approach. Sixteen scenario cases with varying magnitudes, distances, and conditioning periods are considered as targets. For each target case, four hazard-consistent ground motion suites with different distributions of duration (one base-duration (actual target) suite and three longer-duration suites respectively) are selected. Collapse fragility curves are derived by conducting incremental dynamic analysis on numerical models of four steel frames. Comparative results demonstrate that the duration effect on structural collapse capacity mainly depends on the magnitude of the mean significant duration (Ds5–75) ratio of two (longer/base) groups. The duration effect is statistically insignificant for cases where the mean Ds5–75 ratio is smaller than 1.4, while it is more significant for cases with mean Ds5–75 ratios larger than 1.4, with a reduction in median collapse capacity of, on average, about 10% when using the longer-duration suites as compared to the base-duration one. It is also found that a longer duration record suite with more cumulative intensity contained has a higher possibility to cause a reduction of structural collapse capacity.

This paper presents an algorithm to select records from a ground motion database for multiple sites under unconditional or conditional earthquake scenarios. Major steps in the selection algorithm a...

Accurate prediction of hysteresis loop of reinforced concrete (RC) columns in different failure modes is of utmost importance for the assessment of inelastic seismic performance of structures. In this paper, a practical approach is proposed by adopting the Bouc-Wen-Baber-Noori (BWBN) model to describe the typical hysteresis characteristics of RC columns, and applying the Artificial Neural Network (ANN) model to evaluate the BWBN model parameters. The governing equation of the BWBN model is first presented in terms of inelastic restoring force-translational displacement relationship according to the experimental data of RC columns under quasi-static cyclic testing. The structural performance database compiled by Pacific Earthquake Engineering Research (PEER) center is then adopted to determine the BWBN model parameters using the differential evolution (DE) algorithm. Furthermore, the ANN model is implemented to associate the identified BWBN model parameters with dimensionless physical parameters of RC columns failing in different modes. According to the investigation, it is found that the magnitude of BWBN model parameters is considerably affected by specific failure mode of RC columns. The accuracy of the estimated BWBN model parameters using the ANN model is examined, in which the hysteresis loop of RC columns failing in different modes can be reasonably quantified by the proposed approach.

This study investigated the small-strain shear modulus behavior of compacted loess under isotropic consolidation. The small-strain shear modulus was obtained from bender element tests duri...

In this paper, the performance of amplify and forward (AF) serial relaying coherent orthogonal frequency division multiplexing (OFDM) free space optical communication (FSO) system is analyzed over an aggregated atmospheric turbulence attenuation channel, which considers path loss, pointing error and M distribution channel. In this FSO system, it is assumed that channel state information (CSI) of transmitter and receiver is known, quadrature amplitude modulation (QAM) is used as the subcarrier mapping mode of OFDM. With the help of Meijer’s G function, the closed-form expressions of outage probability and average symbol error probability (ASER) for the FSO system under consideration are derived. The effects of subcarriers, the number of hops and turbulence intensity on the performance of coherent OFDM FSO systems are analyzed through simulation.

Given that the biaxial hysteretic model of shear-critical reinforced concrete columns is usually developed by incorporating with the single-degree-of-freedom system to write its expression in differential equation of motion control, it is unfeasible to match with the experimental data of the biaxial quasi-static testing in terms of force-displacement relationship. Therefore, a simple and effective biaxial hysteretic model is proposed in this study based on the Bouc–Wen–Baber–Noori model for characterizing the biaxial quasi-static testing of shear-critical reinforced concrete columns. An incremental numerical algorithm is then proposed to solve the pair of coupled differential equations based on the backward Euler discretization method and Newton–Raphson iteration scheme. Moreover, the influence of critical model parameters affecting the typical hysteretic characteristics of shear-critical reinforced concrete columns, such as strength deterioration, stiffness degradation, pinching phenomena, and biaxial coupling effect, is investigated, respectively, to demonstrate the capability of the biaxial hysteretic model developed. Finally, a parameter calibration procedure using the differential evolution algorithm is introduced to calibrate the magnitude of involved model parameters by comparing with the experimental data of the biaxial quasi-static testing. According to the investigation, it is found that the developed biaxial hysteretic model can reasonably describe the typical hysteretic characteristics of shear-critical reinforced concrete columns under biaxial quasi-static excitation. Following the proposed parameter calibration procedure, the biaxial hysteretic model developed produces the biaxial hysteretic loops of shear-critical reinforced concrete columns, which is in good agreement with the experimental data once the magnitude of involved model parameters is calibrated.

The Newmark sliding block model is widely used in the evaluation of seismic slope stability. When using this model, the horizontal acceleration is usually considered to act parallel to the slope (downslope), and the vertical component of ground motions is commonly ignored. This study investigates the effect of vertical accelerations on the results of sliding displacement, and quantitatively compares the difference of the displacement DN calculated from the original Newmark's approach with the displacement DHV calculated from a more realistic case (horizontal and vertical accelerations properly considered). For this purpose, 4136 ground motion recordings are selected from the NGA-West2 database, and >2000 hypothetic slopes are simulated with critical accelerations ac ranging from 0 to 0.8 g. Comparison of DHV and DH shows that the incorporation of vertical accelerations only slightly increases the displacement. Besides, the calculated DHV/DN ratios are generally in the range of 1 to 5, indicating that DN significantly underestimates the slope displacement due to the simplification of seismic excitations. The DHV/DN ratios generally increase as ac increases. Simple predictive models are proposed to quantify the correction factor KD (KD = DHV/DN). They can be easily used in seismic landslide hazard assessment to improve the performance of the original Newmark's approach.

The effective number of cycles is an important ground motion parameter for the assessment of liquefaction potential. In this paper, empirical correlations for two measures of the effective number of cycles with seven amplitude-, cumulative-, and duration-based intensity measures (IMs) are studied and compared, based on the NGA strong motion database and several ground motion prediction equations. The adopted definitions of the effective number of cycles include an absolute measure ( N A ) and a relative measure ( N R ). It is shown that N A is highly correlated with high-frequency IMs, such as spectral acceleration (SA) at short periods, Arias intensity, and negatively correlated with signification durations (Ds). On the other hand, N R shows generally negative correlations with both amplitude- and cumulative-based IMs. N R also exhibits small-to-moderate positive correlations with Ds, which are commonly regarded as similar parameters to the effect number of cycles. Simple parametric functions are provided to describe the N A -SA and N R -SA correlations for various cases. The importance of considering multiple IMs rather than SA only in ground-motion selection is also briefly demonstrated.

Empirical correlations between the frequency-content parameters of earthquake ground motions and amplitude-, cumulative-, and duration-based intensity measures (IMs) are examined in this st...

Six rhodamine-based “turn-on” fluorescence chemosensors (L1–L6) with different substituents for mercury (Hg2+) were readily synthesized and investigated; they displayed high selectivity and chelation enhanced ratiometric fluorescence change and colorimetric change with Hg2+ among the metal ions examined. Based on UV and fluorescence spectral data, the effects of different substituents on spectral properties of the probes were presented and discussed. The detection limit of Hg2+ to probe L1 was as low as 50 nmol/L because of its electron-donating group. Theoretical calculation also supported the process of reaction. Confocal laser scanning microscopy experiments showed that probe could be used to detect Hg2+ in living cells.