Digital near source accelerograms recorded by instrumental arrays in Tangshan, China. Part I (1982.7-1984.12)

The ultimate goal of earthquake hazard mitigation research is to gain sufficient understanding of the phenomena involved in an earthquake to minimize the loss of life and property resulting from such an event. In order to design safe, economical structures and facilities in seismic areas, it is necessary to understand the nature of the ground motion generated by an earthquake. This understanding can ultimately come only from the measurement of the strong ground motion resulting from actual damaging earthquakes. In order to facilitate the acquisition of strong ground motion data world-wide, an International Workshop on Strong Motion Earthquake Instrument Arrays was held in 1978 in Hawaii. Participants in the Workshop appealed to the earthquake-threatened countries of the world to undertake a concerted effort to establish strong-motion accelerograph arrays and networks. In response to the appeal of these experts in earthquake hazard mitigation, and in accord with the "China-U.S. Protocol for Scientific and Technical Cooperation in Earthquake Studies," a joint research project on strong ground motion measurement has been established in China. In the first phase of this project, from April 1981 to December 1984, 22 Kinemetrics PDR-1 Digital Event Recorders equipped with FBA-13 Force Balance Accelerometers, and 18 Kinemetrics SMA-1 Analog Accelerographs were deployed in China. Of this total, 13 PDR-1 and 3 SMA1 instruments were deployed in a surface array and a three-dimensional array in the aftershock region of the 1976 Tangshan earthquake. These two arrays recorded a total of 1053 near-source accelerograms from 416 earthquakes with magnitudes ranging from ML = 1.2 to 5.7. The source-station distances ranged from 2 to 45 kilometers. Most of the records contain the complete P- and S-wave motion along with accurate absolute time. Both the volume and quality of the accelerograms are much greater than ever before obtained in China. The largest event recorded was the ML = 5.7 Lulong earthquake of October 19, 1982. Nine instruments were triggered by this event. The epicentral distance from the recording stations ranged from 5 to 41 kilometers, and the corresponding peak horizontal acceleration ranged from 0.217 to 0.008g. Accelerograms were recorded by the three-dimensional array from twenty-eight events. Measurements were made to a depth of 900 meters below the ground surface. The records obtained provide a unique source of data for the study of the propagation of seismic waves near the earth's surface. In order to make these data more useful, they will be published along with site data in a separate volume. In this report, 218 of the most significant accelerograms; are published. The data was obtained from earthquakes with magnitudes ranging from ML = 2.3 to 5.7. All of the data reproduced in this report is available on 9-track computer tape

Introducing Adaptive Incremental Dynamic Analysis: A New Tool for Linking Ground Motion Selection and Structural Response Assessment

Adaptive Incremental Dynamic Analysis (AIDA) is a novel ground motion selection scheme that adaptively changes the ground motion suites at different ground motion intensity levels to match hazardconsistent properties for structural response assessment. Incremental DynamicAnalysis (IDA), a current dynamic response history analysis practice in Performance-Based Earthquake Engineering (PBEE), uses the same suite of ground motions at all Intensity Measure (IM) levels to estimate structural response. Probabilistic Seismic Hazard Analysis (PSHA) deaggregation tells us, however, that the target distributions of important ground motion properties change as the IM levels change. To match hazard-consistent ground motion properties, ground motions can be re-selected at each IM level, but ground motion continuity is lost when using such “stripes” (i.e., individual analysis points at each IM level). Alternatively, the data from the same ground motions in IDA can be re-weighted at various IM levels to match their respective target distributions of properties, but this implies potential omission of data and curse of dimensionality. Adaptive Incremental Dynamic Analysis, in contrast, gradually changes ground motion records to match ground motion properties as the IM level changes, while also partially maintaining ground motion continuity without the omission of useful data. AIDA requires careful record selection across IM levels. Potential record selection criteria include ground motion properties from deaggregation, or target spectrum such as the Conditional Spectrum. Steps to perform AIDA are listed as follows: (1) obtain target ground motion properties for each IM level; (2) determine “bin sizes” (i.e., tolerance for acceptable ground motion properties) and identify all candidate ground motions that fall within target bins; (3) keep ground motions that are usable at multiple IM levels, to maintain continuity; (4) use each ground motion for IDA within its allowable IM range. As a result, if we keep increasing the “bin sizes”, AIDA will approach IDA asymptotically; on the other hand, if we decrease the “bin sizes”, AIDA will approach the other end of “stripes”. This paper addresses the challenges of changing records across various IM levels. Different ground motion selection schemes are compared with AIDA to demonstrate the advantages of using AIDA. Example structural analyses are used to illustrate the impact of AIDA on the estimation of structural response in PBEE. By combining the benefits of IDA and PSHA without the omission of useful data, AIDA is a promising new tool for linking ground motion selection and structural response assessment

A Framework for Evaluating Motion Segmentation Algorithms

There have been many proposals for algorithms segmenting human whole-body motion in the literature. However, the wide range of use cases, datasets, and quality measures that were used for the evaluation render the comparison of algorithms challenging. In this paper, we introduce a framework that puts motion segmentation algorithms on a unified testing ground and provides a possibility to allow comparing them. The testing ground features both a set of quality measures known from the literature and a novel approach tailored to the evaluation of motion segmentation algorithms, termed Integrated Kernel approach. Datasets of motion recordings, provided with a ground truth, are included as well. They are labelled in a new way, which hierarchically organises the ground truth, to cover different use cases that segmentation algorithms can possess. The framework and datasets are publicly available and are intended to represent a service for the community regarding the comparison and evaluation of existing and new motion segmentation algorithms

Ground Motion Modeling Wilayah Sumatera Selatan Berdasarkan Analisis Bahaya Gempa Probabilistik

Ground motion modeling dapat dipergunakan untuk menentukan besarnya bahaya gempa pada batuan dasar di suatu site dan untuk menentukan sumber gempa yang memberikan dampak paling dominan pada suatu site. Dengan menggunakan prinsip Probabilistic Seismic Hazard Analysis (PSHA) dan dengan menggunakan software USGS akan didapatkan nilai peak ground acceleration (PGA) pada batuan dasar, yang kemudian dengan menggunakan Nonlinear Earthquake site Response Analyses (NERA) akan didapatkan ground motion modeling pada permukaan. Dengan melakukan metode tersebut pada suatu site di wilayah Sumatera Selatan, BH-01 dan BH-08 didapatkan hasil nilai PGA pada site BH-01 sebesar 0.248g dan pada BH-08 sebesar 0.2711g dengan masing masing memiliki maksimum kekuatan gempa sebesar 7.2 SR dan 7 SR serta sumber gempa yang memberikan dampak bahaya paling dominan adalah sumber gempa background. Dan untuk ground motion modeling pada permukaan untuk site BH-01 sebesar 0.41g dan pada site BH-08 sebesar 0.49g

Effect of Ground Motion Characteristics on the Seismic Response of Torsionally Coupled Elastic Systems

This study presents a systematic investigation of the effects of ground motion characteristics, especially its multi-directional character, on the response of torsionally coupled elastic structural systems. The ground motion model is probabilistic and is founded on the assumption of the existence of ground motion principal directions. The structural systems considered are single-story and multi-story elastic shear beam models with stiffness eccentricity.National Science Foundation Grants ENV 77-07190 and PFR 80-0258

Automated quantification and evaluation of motion artifact on coronary CT angiography images

Abstract Purpose This study developed and validated a Motion Artifact Quantification algorithm to automatically quantify the severity of motion artifacts on coronary computed tomography angiography (CCTA) images. The algorithm was then used to develop a Motion IQ Decision method to automatically identify whether a CCTA dataset is of sufficient diagnostic image quality or requires further correction. Method The developed Motion Artifact Quantification algorithm includes steps to identify the right coronary artery (RCA) regions of interest (ROIs), segment vessel and shading artifacts, and to calculate the motion artifact score (MAS) metric. The segmentation algorithms were verified against ground‐truth manual segmentations. The segmentation algorithms were also verified by comparing and analyzing the MAS calculated from ground‐truth segmentations and the algorithm‐generated segmentations. The Motion IQ Decision algorithm first identifies slices with unsatisfactory image quality using a MAS threshold. The algorithm then uses an artifact‐length threshold to determine whether the degraded vessel segment is large enough to cause the dataset to be nondiagnostic. An observer study on 30 clinical CCTA datasets was performed to obtain the ground‐truth decisions of whether the datasets were of sufficient image quality. A five‐fold cross‐validation was used to identify the thresholds and to evaluate the Motion IQ Decision algorithm. Results The automated segmentation algorithms in the Motion Artifact Quantification algorithm resulted in Dice coefficients of 0.84 for the segmented vessel regions and 0.75 for the segmented shading artifact regions. The MAS calculated using the automated algorithm was within 10% of the values obtained using ground‐truth segmentations. The MAS threshold and artifact‐length thresholds were determined by the ROC analysis to be 0.6 and 6.25 mm by all folds. The Motion IQ Decision algorithm demonstrated 100% sensitivity, 66.7% ± 27.9% specificity, and a total accuracy of 86.7% ± 12.5% for identifying datasets in which the RCA required correction. The Motion IQ Decision algorithm demonstrated 91.3% sensitivity, 71.4% specificity, and a total accuracy of 86.7% for identifying CCTA datasets that need correction for any of the three main vessels. Conclusion The Motion Artifact Quantification algorithm calculated accurate

Evaluation of Motion Artifact Metrics for Coronary CT Angiography

Purpose This study quantified the performance of coronary artery motion artifact metrics relative to human observer ratings. Motion artifact metrics have been used as part of motion correction and best‐phase selection algorithms for Coronary Computed Tomography Angiography (CCTA). However, the lack of ground truth makes it difficult to validate how well the metrics quantify the level of motion artifact. This study investigated five motion artifact metrics, including two novel metrics, using a dynamic phantom, clinical CCTA images, and an observer study that provided ground‐truth motion artifact scores from a series of pairwise comparisons. Method Five motion artifact metrics were calculated for the coronary artery regions on both phantom and clinical CCTA images: positivity, entropy, normalized circularity, Fold Overlap Ratio (FOR), and Low‐Intensity Region Score (LIRS). CT images were acquired of a dynamic cardiac phantom that simulated cardiac motion and contained six iodine‐filled vessels of varying diameter and with regions of soft plaque and calcifications. Scans were repeated with different gantry start angles. Images were reconstructed at five phases of the motion cycle. Clinical images were acquired from 14 CCTA exams with patient heart rates ranging from 52 to 82 bpm. The vessel and shading artifacts were manually segmented by three readers and combined to create ground‐truth artifact regions. Motion artifact levels were also assessed by readers using a pairwise comparison method to establish a ground‐truth reader score. The Kendall\u27s Tau coefficients were calculated to evaluate the statistical agreement in ranking between the motion artifacts metrics and reader scores. Linear regression between the reader scores and the metrics was also performed. Results On phantom images, the Kendall\u27s Tau coefficients of the five motion artifact metrics were 0.50 (normalized circularity), 0.35 (entropy), 0.82 (positivity), 0.77 (FOR), 0.77(LIRS), where higher Kendall\u27s Tau signifies higher agreement. The FOR, LIRS, and transformed positivity (the fourth root of the positivity) were further evaluated in the study of clinical images. The Kendall\u27s Tau coefficients of the selected metrics were 0.59 (FOR), 0.53 (LIRS), and 0.21 (Transformed positivity). In the study of clinical data, a Motion Artifact Score, defined as the product of FOR and LIRS metrics, further improved agreement with reader scores, with a Kendall\u27s Tau coefficient of 0.65. Conclusion The metrics of FOR, LIRS, and the product of the two metrics provided the highest agreement in motion artifact ranking when compared to the readers, and the highest linear correlation to the reader scores. The validated motion artifact metrics may be useful for developing and evaluating methods to reduce motion in Coronary Computed Tomography Angiography (CCTA) images