Logic Tree Research Articles

Wildlife Conservation National Selection Study Based on Logic Tree Analysis with TOPSIS

Wildlife Conservation National Selection Study Based on Logic Tree Analysis with TOPSIS

The 2023 US National Seismic Hazard Model: Subduction ground-motion models

The US Geological Survey National Seismic Hazard Models (NSHMs) are used to calculate earthquake ground-shaking intensities for design and rehabilitation of structures in the United States. The most recent 2014 and 2018 versions of the NSHM for the conterminous United States included major updates to ground-motion models (GMMs) for active and stable crustal tectonic settings; however, the subduction zone GMMs were largely unchanged. With the recent development of the next generation attenuation-subduction (NGA-Sub) GMMs, and recent progress in the utilization of “M9” Cascadia earthquake simulations, we now have access to improved models of ground shaking in the US subduction zones and the Seattle basin. The new NGA-Sub GMMs support multi-period response spectra calculations. They provide global models and regional terms specific to Cascadia and terms that account for deep-basin effects. This article focuses on the updates to subduction GMMs for implementation in the 2023 NSHM and compares them to the GMMs of previous NSHMs. Individual subduction GMMs, their weighted averages, and their impact on the estimated mean hazard relative to the 2018 NSHM are discussed. The updated logic trees include three of the new NGA-Sub GMMs and retain two older models to represent epistemic uncertainty in both the median and standard deviation of ground-shaking intensities at all periods of interest. Epistemic uncertainty is further represented by a three-point logic tree for the NGA-Sub median models. Finally, in the Seattle region, basin amplification factors are adjusted at long periods based on the state-of-the-art M9 Cascadia earthquake simulations. The new models increase the estimated mean hazard values at short periods and short source-to-site distances for interface earthquakes, but decrease them otherwise, relative to the 2018 NSHM. On softer soils, the new models cause decreases to the estimated mean hazard for long periods in the Puget Lowlands basin but increases within the deep Seattle portion of this basin for short periods relative to the 2018 NSHM.

Regional and Site-Specific Ground Motion Model for Probabilistic Seismic Hazard Analysis in Taiwan: A Case Study of I-Lan

In this study, a probabilistic seismic hazard analysis (PSHA) using a traditional approach with a simple logic tree that handles a single-site sigma is developed to present a site-specific ground motion hazard. The analysis relies on the accelerometer data collected from the I-Lan Plain, a deep sedimentary basin in Northeastern Taiwan where hundreds of earthquake sequences (with [Formula: see text] = 4.0–7.6) were recorded by the Taiwan Strong Motion Instrument Program (TSMIP) network between 1992 and 2016. The performance of PSHA on soil and hard sites is evaluated from two representative instrumented sites (ILA048 and ILA025), using residual measures available from a ground motion model (GMM) developed by Phung et al. [ 2023 ] (referred to as Ph23), from which the uncertainties in site terms ([Formula: see text] and single-site sigma ([Formula: see text] are estimated. We address key conceptual issues in the current PSHA approach and introduce a new region- and site-specific PSHA approach in which (1) site-to-site variability ([Formula: see text] is estimated as a random variance in a mixed effects GMM regression and (2) the GMM site-specific single-site sigma ([Formula: see text] is replaced with a generic site-corrected aleatory variability ([Formula: see text]). Comparison of the region- and site-specific hazard curves from our method against the traditional method estimates at two well-recorded sites in the I-Lan region shows an approximate 50% difference in prediction ground motion values considering for 2% and 10% probability of exceedance in 50 years.

Detection of Incipient Faults in Power Transformers using Fuzzy Logic and Decision Tree Models Based on Dissolved Gas Analysis

This paper proposes an integrated approach utilizing Fuzzy Logic and Decision Tree algorithms to diagnose early-stage faults in power transformers based on Dissolved Gas Analysis (DGA) test results of transformer insulation oil. Overcoming limitations in conventional methods such as Duval Triangle, Key Gas Analysis, Rogers Ratio, IEC Ratio, and Doernenburg Ratio, our Fuzzy Logic and Decision Tree models address issues like inaccurate diagnosis, inconsistent diagnosis, lack of decisions or out-of-code results, and time-intensive manual calculations for large DGA datasets. The Decision Tree algorithm, a machine learning technique is applied to categorize faults into thermal and electrical types. Trained with over 300 DGA samples from transformers with known faults, the models exhibit robust performance during testing with different datasets. Notably, the Duval Triangle decision tree model attains the highest accuracy among the ten developed models, achieving a 98% accuracy rate when tested with 50 samples with known faults. Moreover, Decision Tree models for KGA, Doernenburg, Rogers, and IEC also demonstrate substantial prediction accuracy at 92%, 86%, 92%, and 90% respectively underscoring the efficacy of artificial intelligence methods over traditional approaches.

Probabilistic tsunami hazard assessment for the makran subduction zone using logic tree and stochastic rupture sources

ABSTRACT The Makran Subduction Zone (MSZ) in the northwestern Indian Ocean can generate large tsunamigenic thrust earthquakes affecting the coastal regions of Pakistan, Iran, Oman, and western India. In this paper, a probabilistic tsunami hazard assessment is conducted for the MSZ using stochastic tsunami simulations of moment magnitude (Mw) of 7.7–9.1 earthquake scenarios. This study investigates uncertainties associated with earthquake occurrence rate, single-segment (eastern and western MSZ) or two-segment (full MSZ) rupture scenarios, source geometry, and slip heterogeneity. The total number of simulated source models is 15,000. This study presents two major categories of results: stochastic source models and ranges of 475, 975, and 2475-year tsunami heights. For instance, tsunami heights generated by Mw 8.5‒8.7 stochastic sources of western MSZ vary between 1 m and 10 m with a mean of ~ 4.5 m in the affected areas. The tsunami heights are sensitive to the source models’ characteristics, such as location of the large slip areas, bathymetry of the nearshore area, and the location of bays. Considering different occurrence rates results in significant variability in the estimated 475, 975, and 2475-year tsunami heights. For example, 2475-year tsunami height in Chabahar is in the range of 3‒7.4 m at 10 m water depth.

Concept logic trees: enabling user interaction for transparent image classification and human-in-the-loop learning

Interpretable deep learning models are increasingly important in domains where transparent decision-making is required. In this field, the interaction of the user with the model can contribute to the interpretability of the model. In this research work, we present an innovative approach that combines soft decision trees, neural symbolic learning, and concept learning to create an image classification model that enhances interpretability and user interaction, control, and intervention. The key novelty of our method relies on the fusion of an interpretable architecture with neural symbolic learning, allowing the incorporation of expert knowledge and user interaction. Furthermore, our solution facilitates the inspection of the model through queries in the form of first-order logic predicates. Our main contribution is a human-in-the-loop model as a result of the fusion of neural symbolic learning and an interpretable architecture. We validate the effectiveness of our approach through comprehensive experimental results, demonstrating competitive performance on challenging datasets when compared to state-of-the-art solutions.

Characterization of historical megathrust earthquake ruptures in Central Chile using logic tree analysis

Characterization of historical megathrust earthquake ruptures in Central Chile using logic tree analysis

Empirical Earthquake Source Scaling Relations for Maximum Magnitudes Estimations in Central America

ABSTRACT Central America is a seismically active region where six tectonic plates (North America, Caribbean, Cocos, Nazca, Panama, and South America) interact in a subduction zone with transform faults and two triple points. This complex tectonic setting makes the maximum magnitude—Mmax—estimation a challenging task, with the crustal fault earthquakes being the most damaging in the seismic history of Central America. The empirical source scaling relations (ESSR) allow the Mmax of faults to be determined from rupture parameters. In this study, we use a dataset of well-characterized earthquakes in the region, comprising 64 events from 1972 to 2021 with magnitudes between Mw 4.1 and 7.7. The dataset incorporates records of rupture parameters (length, width, area, slip, and magnitude) and information on the faults and aftershocks associated. This database is an important product in itself, and through its use we determine which global relations fit best to our data via a residual analysis. Moreover, based on the best-quality records, we develop scaling relations for Central America (CA-ESSR) for rupture length, width, and area. These new relations were tested and compared with recent earthquakes, and logic trees are proposed to combine the CA-ESSR and the best-fit global relations. Therefore, we estimate the Mmax for 30 faults using the logic tree for rupture length, considering a total rupture of the fault and multifault scenarios. Our results suggest that in Central America rupture areas larger than other regions are required to generate the same magnitudes. We associate this with the shear modulus (μ), which seems to be lower (∼30% less) than the global mean values for crustal rocks. Furthermore, considering multifault ruptures, we found several fault systems with potential Mmax≥Mw 7.0. These findings contribute to a better understanding of regional seismotectonics and to the efficient characterization of fault rupture models for seismic hazards.

Probabilistic seismic hazard assessment framework for Uganda: a stochastic event-based modelling approach

Uganda lies between the eastern and western arms of the East African Rift System, the largest seismically active rift above sea level. With increasing population, urbanisation and rapid construction, seismic risk in the country is escalating fast and is compounded by the high vulnerability of the building stock and inadequate disaster prevention and mitigation strategies. Hence, there is an urgent need to assess Uganda’s resilience against seismic risks. This paper presents a Monte-Carlo based probabilistic seismic hazard model for Uganda, as the first step towards the development of a seismic risk and resilience assessment framework for the country. In addition to fault segment data, earthquake catalogues are compiled for the period between 1900 and 2022 to estimate recurrence parameters for source zones in the area of interest. Area source zones incorporating focal mechanisms are used to stochastically model a national hazard framework for Uganda. A logic tree approach is applied to implement four ground motion prediction equations for both stable continental and active shallow crust geologies. Mean hazard curves, uniform hazard spectra, earthquake disaggregation and spectral pseudo-accelerations for major Ugandan cities are derived in addition to hazard maps for the country. The findings are largely consistent with previous regional studies and confirm that western Uganda is exposed to the highest level of seismicity. The model presented herein can be used to kick-start the update and continuous improvement of Uganda Seismic Design Code and the National Policy for Disaster Preparedness and Management.

Suitability Analysis of Ground Motion Prediction Equations for Western and Central Himalayas and Indo-Gangetic Plains

ABSTRACT Ground motion prediction equations (GMPEs) play a key role in seismic hazard assessment (SHA). Considering the seismo-tectonic, geophysical, and geotectonic characteristics of a target region, all the GMPEs may not be suitable in predicting the observed ground motion effectively. With a fairly large number of published GMPEs, the selection and ranking of suitable GMPEs for the design of logic trees in SHA for a particular target region have become a necessity of late. This paper presents a detailed quantitative evaluation of performance of 16 GMPEs against recorded ground motion data in two target regions, characterized by distinct seismo-tectonic, geophysical, and geotectonical nature. The dataset comprises 465 three-component spectral accelerograms corresponding to 122 earthquake events. The suitability of a GMPE is tested by two widely accepted data-driven statistical methods, namely, likelihood (LH) and log-likelihood (LLH) method. Different suites of GMPEs are shown suitable for different periods of interest. The results will be useful to scientists and engineers for microzonation and estimation of seismic design parameters for the design of earthquake-resistant structures in these regions.

The Seismicity Rate Model for the 2022 Aotearoa New Zealand National Seismic Hazard Model

ABSTRACT A seismicity rate model (SRM) has been developed as part of the 2022 Aotearoa New Zealand National Seismic Hazard Model revision. The SRM consists of many component models, each of which falls into one of two classes: (1) inversion fault model (IFM); or (2) distributed seismicity model (DSM). Here we provide an overview of the SRM and a brief description of each of the component models. The upper plate IFM forecasts the occurrence rate for hundreds of thousands of potential ruptures derived from the New Zealand Community Fault Model version 1.0 and utilizing either geologic- or geodetic-based fault-slip rates. These ruptures are typically less than a couple of hundred kilometers long, but can exceed 1500 km and extend along most of the length of the country (albeit with very low probabilities of exceedance [PoE]). We have also applied the IFM method to the two subduction zones of New Zealand and forecast earthquake magnitudes of up to ∼Mw 9.4, again with very low PoE. The DSM combines a hybrid model developed using multiple datasets with a non-Poisson uniform rate zone model for lower seismicity regions of New Zealand. Forecasts for 100 yr are derived that account for overdispersion of the rate variability when compared with Poisson. Finally, the epistemic uncertainty has been modeled via the range of models and parameters implemented in an SRM logic tree. Results are presented, which indicate the sensitivity of hazard results to the logic tree branches and that were used to reduce the overall complexity of the logic tree.

On the Use of Instrumental and Macroseismic Data to Evaluate Ground-Motion Models: The 2019 Mw 6.4 Durres, Albania, Earthquake Sequence

Abstract In this study, we analyze the existing ground-motion models (GMMs) applicable in Albania for horizontal peak ground acceleration (PGA) and spectral acceleration (SA) using instrumental ground motions, and also incorporate online citizen responses from “Did you feel it?” (DYFI) to compensate for the sparse distribution of strong-motion stations and provide better constraints for near-fault motions. Our evaluation focuses primarily on the damaging 26 November 2019 Mw 6.4 Durres earthquake, incorporating 1360 DYFI online citizen responses collected after the Durres mainshock event, along with two significant September foreshocks and two large November aftershocks with a moment magnitude Mw>5.0. In general, the DYFI intensities exhibit higher values than instrumentation data, and we find that SA at 0.3 s better represents the observed macroseismic intensities for all events. In the meantime, the reversible relationships between macroseismic intensities and PGA/SA, as established by Oliveti et al. (2022) based on a dataset from the European region (Italy), show a better fit for the converted DYFI observations when compared to instrumental data, in contrast to the fit of the converted DYFI observations by Worden et al. (2012). This underscores the importance of regional characterization when considering the datasets from online citizen responses. The extensive DYFI intensities set, particularly in near-fault regions, significantly improves the evaluation of GMMs due to the sparse distribution of instrumentation data. Moreover, we account for data variance, and applied the log-likelihood approaches to select and rank a candidate set of GMMs. In addition to recommending a set of GMMs suitable for the Albania region, our study highlights the valuable applications of using online citizen responses like DYFI for ground-motion estimations, which are crucial in regions with limited instrumental station coverage. These online citizen response datasets contribute to better constraining the selection of GMMs, although careful consideration is necessary when relating intensity to ground motion for regional characterization. Our study makes a significant contribution to GMM selection and provides a valuable reference for the logic tree structure in subsequent seismic hazard assessments on both national and regional scales.

The Ground-Motion Characterization Model for the 2022 New Zealand National Seismic Hazard Model

ABSTRACT This article summarizes the ground-motion characterization (GMC) model component of the 2022 New Zealand National Seismic Hazard Model (2022 NZ NSHM). The model development process included establishing a NZ-specific context through the creation of a new ground-motion database, and consideration of alternative ground-motion models (GMMs) that have been historically used in NZ or have been recently developed for global application with or without NZ-specific regionalizations. Explicit attention was given to models employing state-of-the-art approaches in terms of their ability to provide robust predictions when extrapolated beyond the predictor variable scenarios that are well constrained by empirical data alone. We adopted a “hybrid” logic tree that combined both a “weights-on-models” approach along with backbone models (i.e., metamodels), the former being the conventional approach to GMC logic tree modeling for NSHM applications using published models, and the latter being increasingly used in research literature and site-specific studies. In this vein, two NZ-specific GMMs were developed employing the backbone model construct. All of the adopted subduction GMMs in the logic tree were further modified from their published versions to include the effects of increased attenuation in the back-arc region; and, all but one model was modified to account for the reduction in ground-motion standard deviations as a result of nonlinear surficial site response. As well as being based on theoretical arguments, these adjustments were implemented as a result of hazard sensitivity analyses using models without these effects, which we consider gave unrealistically high hazard estimates.

Design displacement for lifelines at fault crossings: the code-based approach for Europe

The earthquake-resistant design of lifelines, such as pipelines, tunnels and bridges, is based on the reliable representation and estimation of the seismic loading. In the case of lifeline–fault crossings, the design fault displacement is typically derived from estimates based on fault dimensions via empirical fault scaling relations for a given “design” scenario event. This approach comes with an unknown level of safety because the fault productivity and the actual distribution of earthquake events are essentially disregarded. To overcome this challenge, a simplified approach is proposed by statistically analyzing the outcome of probabilistic fault displacement hazard analyses (PFDHAs). A selection of faults from the 2020 European Fault-Source Model is used to build the logic tree and to set the range of parameters considered in the PFDHAs. The methodology allows the (mostly conservative) approximation of the fault displacement corresponding to any given return period based on readily available data, namely fault productivity, fault mechanism, fault length, and lifeline crossing location on the fault. The proposed methodology has been proposed and adopted as an informative Annex in prEN 1998-4:2022.

The USGS 2023 Conterminous U.S. Time-Independent Earthquake Rupture Forecast

ABSTRACT We present the 2023 U.S. Geological Survey time-independent earthquake rupture forecast for the conterminous United States, which gives authoritative estimates of the magnitude, location, and time-averaged frequency of potentially damaging earthquakes throughout the region. In addition to updating virtually all model components, a major focus has been to provide a better representation of epistemic uncertainties. For example, we have improved the representation of multifault ruptures, both in terms of allowing more and less fault connectivity than in the previous models, and in sweeping over a broader range of viable models. An unprecedented level of diagnostic information has been provided for assessing the model, and the development was overseen by a 19-member participatory review panel. Although we believe the new model embodies significant improvements and represents the best available science, we also discuss potential model limitations, including the applicability of logic tree branch weights with respect different types of hazard and risk metrics. Future improvements are also discussed, with deformation model enhancements being particularly worthy of pursuit, as well as better representation of sampling errors in the gridded seismicity components. We also plan to add time-dependent components, and assess implications with a wider range of hazard and risk metrics.

Online control synthesis for uncertain systems under signal temporal logic specifications

Signal temporal logic (STL) formulas have been widely used as a formal language to express complex robotic specifications, thanks to their rich expressiveness and explicit time semantics. Existing approaches for STL control synthesis suffer from limited scalability with respect to the task complexity and lack of robustness against the uncertainty, for example, external disturbances. In this paper, we study the online control synthesis problem for uncertain discrete-time systems subject to STL specifications. Different from existing techniques, we propose an approach based on STL, reachability analysis, and temporal logic trees. First, based on a real-time version of STL semantics, we develop the notion of tube-based temporal logic tree (tTLT) and its recursive (offline) construction algorithm. We show that the tTLT is an under-approximation of the STL formula, in the sense that a trajectory satisfying a tTLT also satisfies the corresponding STL formula. Then, an online control synthesis algorithm is designed using the constructed tTLT. It is shown that when the STL formula is robustly satisfiable and the initial state of the system belongs to the initial root node of the tTLT, it is guaranteed that the trajectory generated by the control synthesis algorithm satisfies the STL formula. We validate the effectiveness of the proposed approach by several simulation examples and further demonstrate its practical usability on a hardware experiment. These results show that our approach is able to handle complex STL formulas with long horizons and ensure the robustness against the disturbances, which is beyond the scope of the state-of-the-art STL control synthesis approaches.

A note on two alternative probabilistic methods to address parametric uncertainty in magnitude frequency distribution (MFD) logic trees (BEEE-D-22-00704)

A note on two alternative probabilistic methods to address parametric uncertainty in magnitude frequency distribution (MFD) logic trees (BEEE-D-22-00704)

Deterministic Seismic Hazard Map Using Region-Specific GMPEs of Maharashtra State, India

Maharashtra stands out as a crucial state in India, demonstrating significant progress in infrastructural development and industrialization. Several prominent cities, including Mumbai, Pune, Nagpur, etc., are significantly contributing to the Indian economy. Considering the importance of the state, a deterministic seismic hazard analysis is executed to reduce the damages to critical and important structures and fatalities caused due to earthquakes. Past earthquakes data are collected within and around the state to prepare a homogenised earthquake catalogue. Seven seismic zones are prepared using K- mean cluster analysis. Independent earthquake events i.e., mainshocks are identified using four renowned declustering methods. Additionally, with the help of mainshocks from each zone, the maximum observed earthquake magnitude ( m^max) and positive correction factor (Δ) are estimated. By superimposing all the mainshocks (after adding Δ) onto the fault map, the maximum observed possible earthquake magnitude of all faults ( M^max) are assigned to each fault. M^max value is used to estimate surface rupture length (RLD) and consecutively, the maximum magnitude (MMax) from fault sources are estimated. Three region-specific ground motion prediction equations (GMPEs) are adopted in the logic trees assigning a proper weightage to each GMPE. A seismic hazard contour maps are prepared at bedrock level, C, and D-type soil sites for Maharashtra. In the western part of the study area, the maximum PGA value is found to be 0.58 g, 0.70 g, and 0.33 g at bedrock level, C, and D-type sites, respectively.

Research on Organizing Strategies for the Olympic Games Based on the TOPSIS Method

Fewer and fewer countries have bid to host the Olympic Games in recent years because of the negative short- and long-term impacts that bidding to host the Games has had on the country. A sustainable and healthy Olympic Games will have a huge positive impact on the country, so it is quite meaningful to assess the ability of an Olympic Games to be sustainable. In order to assess the success of the Olympic Games, we designed the Olympic Sustainability Index (OSI) as a primary indicator. Then, using McKinsey Logic Tree Analysis, we constructed a three-level evaluation index system, and we designed three secondary indicators and 10 tertiary indicators. The entropy weight method was used to determine the weights between the three-level indicators, and CRITIC weighting method was used to determine the weights between the second-level indicators, and finally an OSI evaluation model was built. After that, we collected the data of tertiary indicators from 1992 to 2020 Winter Olympics and Summer Olympics, and based on the above evaluation model, we got the OSI of each Olympic Games, among which the Vancouver Winter Olympics and Beijing Summer Olympics had the highest index. Since the OSI of the Olympic Games fluctuates greatly, in order to attenuate it, we propose the strategy of "fixed host city", i.e., selecting Vancouver and Beijing as the host cities of the Winter Olympic Games and the Summer Olympic Games, respectively.

Effectiveness Analysis of Hydraulic Torque Wrench Machine Using Failure Mode and Effect Analysis (FMEA) and Logic Tree Analysis Study Case on Heavy Equipment Manufacturing

Production quality in terms of process efficiency and quality in the manufacturing industry must always be improved in order to maintain customer confidence. PT XYZ as a heavy equipment assembly company is one of the companies that depends on process reliability for a smooth production process. Based on this, the problems raised in this study focus on increasing the efficiency of the process of installing bolts on slew bearings. By using Overall Equipment Efficiency (OEE) and Process Capability (CpK) as the main benchmarks for measuring the quality of the production process where the latest data shows the average OEE value is at 18.53%, while the CpK value for the bolt tightening process is at 0 ,67. The OEE and CpK figures obtained show that the process quality is still not optimal and needs to be improved. The purpose of this research is to identify and prevent as many factors as possible that can lead to process failure. The methods used to evaluate processes and to identify where and how a process might fail are Failure Mode and Effect Analysis (FMEA) and Logic Tree Analysis (LTA). Both methods are used to identify and prevent as many factors as possible that can lead to a process failure. The results of research using the FMEA and LTA methods show that in the process of installing slew bearing bolts there is a process that needs to be improved because the RPN value is quite high, namely above 125. Some suggestions for improvement such as the use of a manipulator arm on a torque tool and the implementation of a manufacturing execution system (MES) can reduce the RPN value from above 125 to 28, where the process obtained is better than before.