Inverse reliability analysis and design for tunnel face stability considering soil spatial variability

The traditional deterministic analysis for tunnel face stability neglects the uncertainties of geotechnical parameters,while the simplified reliability analysis which models the potential uncertainties by means of random variables usually fails to account for soil spatial variability.To overcome these limitations,this study proposes an efficient framework for conducting reliability analysis and reliability-based design(RBD)of tunnel face stability in spatially variable soil strata.The three-dimensional(3D)rotational failure mechanism of the tunnel face is extended to account for the soil spatial variability,and a probabilistic framework is established by coupling the extended mechanism with the improved Hasofer-Lind-Rackwits-Fiessler recursive algorithm(iHLRF)as well as its inverse analysis formulation.The proposed framework allows for rapid and precise reliability analysis and RBD of tunnel face stability.To demonstrate the feasibility and efficacy of the proposed framework,an illustrative case of tunnelling in frictional soils is presented,where the soil's cohesion and friction angle are modelled as two anisotropic cross-correlated lognormal random fields.The results show that the proposed method can accurately estimate the failure probability(or reliability index)regarding the tunnel face stability and can efficiently determine the required supporting pressure for a target reliability index with soil spatial variability being taken into account.Furthermore,this study reveals the impact of various factors on the support pressure,including coefficient of variation,cross-correlation between cohesion and friction angle,as well as autocorrelation distance of spatially variable soil strata.The results also demonstrate the feasibility of using the forward and/or inverse first-order reliability method(FORM)in high-dimensional stochastic problems.It is hoped that this study may provide a practical and reliable framework for determining the stability of tunnels in complex soil strata.

Spatial Analysis of the Aging Population and Socio-economic Factors of China:Global and Local Perspectives

Population aging has become an inevitable trend and exerted profound influences on socio-economic development in China.In this study,we utilized data from national population census and statistical yearbooks in 2010 and 2020 to explore spatio-temporal patterns of aging population and its coupling correlations with socio-economic factors from both global and local perspectives.The results from Local Indicators of Spatial Association(LISA)uncover notable spatial disparities in aging population rates,with higher rates concentrated in the eastern regions and lower rates in the western areas of the Chinese mainland.The results from the global correlation analysis with the changes in aging population rates show significant positive correlations with government interventions and industrial structures,but negatively correlated with economic development,social consumption,and medical facilities.From a local perspective,a Geographically Weighted(GW)correlation analysis is employed to uncover local correlations between aging trends and socio-economic factors.The insights gained from this technique not only underscore the complexity and diversity of economic implications stemming from population aging,but also provide invaluable guidance for crafting region-specific economic policies tailored to various stages of population aging.

Transformer-Aided Deep Double Dueling Spatial-Temporal Q-Network for Spatial Crowdsourcing Analysis

With the rapid development ofmobile Internet,spatial crowdsourcing has becomemore andmore popular.Spatial crowdsourcing consists of many different types of applications,such as spatial crowd-sensing services.In terms of spatial crowd-sensing,it collects and analyzes traffic sensing data from clients like vehicles and traffic lights to construct intelligent traffic prediction models.Besides collecting sensing data,spatial crowdsourcing also includes spatial delivery services like DiDi and Uber.Appropriate task assignment and worker selection dominate the service quality for spatial crowdsourcing applications.Previous research conducted task assignments via traditional matching approaches or using simple network models.However,advanced mining methods are lacking to explore the relationship between workers,task publishers,and the spatio-temporal attributes in tasks.Therefore,in this paper,we propose a Deep Double Dueling Spatial-temporal Q Network(D3SQN)to adaptively learn the spatialtemporal relationship between task,task publishers,and workers in a dynamic environment to achieve optimal allocation.Specifically,D3SQNis revised through reinforcement learning by adding a spatial-temporal transformer that can estimate the expected state values and action advantages so as to improve the accuracy of task assignments.Extensive experiments are conducted over real data collected fromDiDi and ELM,and the simulation results verify the effectiveness of our proposed models.

Optimizing Spatial Pattern Analysis in Serial Remote Sensing Images through Empirical Mode Decomposition and Ant Colony Optimization

Serial remote sensing images offer a valuable means of tracking the evolutionary changes and growth of a specific geographical area over time.Although the original images may provide limited insights,they harbor considerable potential for identifying clusters and patterns.The aggregation of these serial remote sensing images(SRSI)becomes increasingly viable as distinct patterns emerge in diverse scenarios,such as suburbanization,the expansion of native flora,and agricultural activities.In a novel approach,we propose an innovative method for extracting sequential patterns by combining Ant Colony Optimization(ACD)and Empirical Mode Decomposition(EMD).This integration of the newly developed EMD and ACO techniques proves remarkably effective in identifying the most significant characteristic features within serial remote sensing images,guided by specific criteria.Our findings highlight a substantial improvement in the efficiency of sequential pattern mining through the application of this unique hybrid method,seamlessly integrating EMD and ACO for feature selection.This study exposes the potential of our innovative methodology,particularly in the realms of urbanization,native vegetation expansion,and agricultural activities.

Geospatial Analysis of Urban Heat Island Effects and Tree Equity

In recent decades, Urban Heat Island Effects have become more pronounced and more widely examined. Despite great technological advances, our current societies still experience great spatial disparity in urban forest access. Urban Heat Island Effects are measurable phenomenon that are being experienced by the world’s most urbanized areas, including increased summer high temperatures and lower evapotranspiration from having impervious surfaces instead of vegetation and trees. Tree canopy cover is our natural mitigation tool that absorbs sunlight for photosynthesis, protects humans from incoming radiation, and releases cooling moisture into the air. Unfortunately, urban areas typically have low levels of vegetation. Vulnerable urban communities are lower-income areas of inner cities with less access to heat protection like air conditioners. This study uses mean evapotranspiration levels to assess the variability of urban heat island effects across the state of Tennessee. Results show that increased developed land surface cover in Tennessee creates measurable changes in atmospheric evapotranspiration. As a result, the mean evapotranspiration levels in areas with less tree vegetation are significantly lower than the surrounding forested areas. Central areas of urban cities in Tennessee had lower mean evapotranspiration recordings than surrounding areas with less development. This work demonstrates the need for increased tree canopy coverage.

Probabilistic back-analysis of rainfall-induced landslides for slope reliability prediction with multi-source information

Probabilistic back-analysis is an important means to infer the statistics of uncertain soil parameters,making the slope reliability assessment closer to the engineering reality.However,multi-source information(including test data,monitored data,field observation and slope survival records)is rarely used in current probabilistic back-analysis.Conducting the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction under rainfalls by integrating multi-source information is a challenging task since thousands of random variables and high-dimensional likelihood function are usually involved.In this paper,a framework by integrating a modified Bayesian Updating with Subset simulation(mBUS)method with adaptive Conditional Sampling(aCS)algorithm is established for the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction.Within this framework,the high-dimensional probabilistic back-analysis problem can be easily tackled,and the multi-source information(e.g.monitored pressure heads and slope survival records)can be fully used in the back-analysis.A real Taoyuan landslide case in Taiwan,China is investigated to illustrate the effectiveness and performance of the established framework.The findings show that the posterior knowledge of soil parameters obtained from the established framework is in good agreement with the field observations.Furthermore,the updated knowledge of soil parameters can be utilized to reliably predict the occurrence probability of a landslide caused by the heavy rainfall event on September 12,2004 or forecast the potential landslides under future rainfalls in the Fuhsing District of Taoyuan City,Taiwan,China.

Environmental Analysis Using Integrated GIS and Spatial Configurations in Israel

The scope for environmental analysis constitutes a critical factor in recent times, yet demanding importance due to the concerns of environmental sustainability. The study aims at analysing the prospects of implementing an integrated GIS and spatial configuration for environment analysis in Israel. The study adopts an empirical study design to consider the multi-dimensional utilisation of an integrated GIS and spatial configuration for environment analysis. The study considers the materials and methods of the GIS system modelling as well, consisting of satellite imagery, GPS-based location identification, Esri ArcGIS, CyberGIS, and BIM integration to present a comprehensive system for the environmental analysis of Israel. The results of the study indicate that the threats of natural disasters and climate change can be identified based on the synergy of spatial data within an integrated GIS modelling. In many cases, it is also used in collaboration with a BIM to ensure that planning and decision-making processes are sustainable, economically beneficial and environmentally considered. Thus, it is concluded that environmental analysis through the projection of visually represented satellite imagery within an integrated GIS with spatial configurations in Israel can minimise the conflicts between the infrastructural designs, human activities, and environmental sustainability.

Stochastic analysis of excavation-induced wall deflection and box culvert settlement considering spatial variability of soil stiffness

In this study, a three-dimensional (3D) finite element modelling (FEM) analysis is carried out to investigate the effects of soil spatial variability on the response of retaining walls and an adjacent box culvert due to a braced excavation. The spatial variability of soil stiffness is modelled using a variogram and calibrated by high-quality experimental data. Multiple random field samples (RFSs) of soil stiffness are generated using geostatistical analysis and mapped onto a finite element mesh for stochastic analysis of excavation-induced structural responses by Monte Carlo simulation. It is found that the spatial variability of soil stiffness can be described by an exponential variogram, and the associated vertical correlation length is varied from 1.3 m to 1.6 m. It also reveals that the spatial variability of soil stiffness has a significant effect on the variations of retaining wall deflections and box culvert settlements. The ignorance of spatial variability in 3D FEM can result in an underestimation of lateral wall deflections and culvert settlements. Thus, the stochastic structural responses obtained from the 3D analysis could serve as an effective aid for probabilistic design and analysis of excavations.

Seismic data analysis based on spatial subsets

There are some limitations when we apply conventional methods to analyze the massive amounts of seismic data acquired with high-density spatial sampling since processors usually obtain the properties of raw data from common shot gathers or other datasets located at certain points or along lines. We propose a novel method in this paper to observe seismic data on time slices from spatial subsets. The composition of a spatial subset and the unique character of orthogonal or oblique subsets are described and pre-stack subsets are shown by 3D visualization. In seismic data processing, spatial subsets can be used for the following aspects： （1） to check the trace distribution uniformity and regularity; （2） to observe the main features of ground-roll and linear noise; （3） to find abnormal traces from slices of datasets; and （4） to QC the results of pre-stack noise attenuation. The field data application shows that seismic data analysis in spatial subsets is an effective method that may lead to a better discrimination among various wavefields and help us obtain more information.

Spatial Characteristic Analysis of the Comparative Advantage of Watermelon and Melon Production in Hainan Province

[Objective] This study investigated the spatial characteristics of counties （cities） with comparative advantages in watermelon and melon production to provide reference bases in formulating strategies for the development of watermelon and melon industries in Hainan Province. [Method] By using the sowing area, total yield, and yield per unit area of watermelon and melon in Hainan Province as research u- nits, the yield comparative advantage （YCA）, efficiency comparative advantage （E- CA）, scale comparative advantage （SCA）, concentration ratio comparative advantage （CRCA）, comprehensive comparative advantage （CCA）, ratio of yield per unit area （RYPA）, sowing area ratio （SAR）, and distribution characteristics of watermelon and melon were systematically analyzed. By referring to the agricultural statistic data of 18 counties （cities） in Hainan Province, indexes for each research unit （i.e., the YCA index, ECA index, SCA index, CRCA index, CCA index, RYPA index, and SAR index） were established and calculated to determine the comparative advantage of watermelon and melon production in Hainan Province. A spatial expression of the research result on a map was conducted by using GIS software. [Result] Seven counties （cities） exhibited comparative advantages in watermelon production, namely, Lingshui, Wanning, Wenchang, Dongfang, Sanya, Ledong, and Changjiang. The Eastern and Southern Hainan Provinces had CCAs, and the Western and Northern Hainan Provinces could be reserved for future development. For melon production, four counties （cities） exhibited comparative advantages, namely, Ledong, Lingshui, Sanya, and Dongfang. The Southern Hainan Province had CCA, whereas the West- ern Hainan Province could be reserved for later development. [Conclusion] The result has showed that establishing watermelon and melon as dominant agricultural prod- ucts is necessary for the future development of the industry and for the formulation of a layout of regions with advantages, where key support and construction should be provided preferentially with the aim to raise the yield, quality, and market com- petitiveness of products.

Ration Spatial Characteristics Analysis of Soil Loss Based on GIS and RUSLE in Jinzhou City

Supported by RS technology and GIS technology, the amount of soil loss and soil erosion intensity in Jinzhou City in 2010 were quantitatively evaluated by the modified RUSLE model. The characteristics of the spatial distribution of soil loss in Jinzhou City were analyzed. The results showed that the soil erosion area of Jinzhou City in 2010 was 7 284.87 km2, accounting for 70.72% of the total area of Jinzhou City. The average soil erosion modulus was 18.27 t/（hm2＆#183;a）, belonging to mild erosion. Two slope belts of 15＆#176;-25＆#176; and 6＆#176;-15＆#176; were the main soil erosion re-gions in Jinzhou City. Soil erosion in Jinzhou City was mainly concentrated in the rural residential land and the dry land, and the soil erosion amount of these two land types accounted for 60.97%of the total soil erosion amount in Jinzhou City in 2010. It was suggested that the treatment of these two land types should be strengthened and be main treatment object for soil and water conservation in future. The research could provide scientific basis for the governments to make policies about soil loss.

Analysis on Features of Spatial and Temporal Distribution of Thunderstorm in Shanghai

Based in 11 daily weather observation station data in Shanghai from 1971 to 2008,a careful research and analysis on the features of thunderstorms spatial and temporal distribution and thunderstorm movement in Shanghai was carried out by using the statistical software of SAS,the method of Mann-Kendall test and wavelets. The results showed that the average annual numbers of thunderstorms days were 26.1,and inter-annual thunderstorm variability was obvious,the annual number of thunderstorm days had a decreasing trend,its value of decreasing days was about-0.418 5 d/10 a. Mann-Kendall test showed that there was an abrupt change in 2000. The seasonal variation of thunderstorm in Shanghai was explicit. The period from March to September was the season when thunderstorm occurred most frequently,about 64.9% of the thunderstorms in a year took place in summer. The results from wavelets analysis showed that the variation cycle period of the annual number of thunderstorms days was about 3,5,12 and 20 years.

GIS Analysis of Spatial Distribution of Crop Incidence

Using GIS spatial statistical analysis method, with ArcGIS software as an analysis tool, taking the diseased maize in Hedong District of Linyi City as the study object, the distribution characteristic of the diseased crops this time in spatial location was analyzed. The results showed that the diseased crops mainly dis- tributed along with river tributaries and downstream of main rivers. The correlation between adjacent diseased plots was little, so the infection of pests and diseases were excluded, and the major reason of incidence might be river pollution.

Study on the Temporal and Spatial Distribution Characteristic of Acid Rain in Guangxi Based on EOF Analysis

Based on acid rain data from ten monitoring sites in Guangxi from 2003 to 2009,the temporal and spatial distribution characteristic of acid rain in Guangxi were analyzed by means of empirical orthogonal function resolution(EOF).The results showed that there was fluctuating change of acid rain frequency in Guangxi,and acid rain pollution became severer in 2004-2008;acid rain frequency changed conformably in the whole region and it was obviously higher in eastern and northwestern Guangxi,while acid rain pollution became severe in western Guangxi;acid rain frequency varied out of phase between northeastern and southwestern Guangxi in an individual year.

Analysis on the Spatial and Temporal Distribution of Extremely Maximum Temperature in Liaoning Province Based on REOF

By selecting the daily maximum temperatures during 1961-2005 in 35 representative stations in Liaoning Province, the temporal and spatial distribution characteristics of extremely maximum temperature event were studied. By using REOF, the mean-square deviation and so on, the variation and distribution situation of extremely maximum temperature in the different regions of Liaoning were reflected. The results showed that the extremely maximum temperature in Liaoning Province could be divided into 3 regions where were respectively the northeast area, the west and the northwest area, the south and the southeast area. The distribution characteristic of extremely maximum temperature threshold value in Liaoning Province was basically consistent with the distribution characteristic of average temperature. The zone where the extremely maximum temperature threshold was relatively high was in the northwest area of Liaoning, and the low threshold zone was in the southeast area and most areas in the east. The variation of extremely maximum temperature in winter was the greatest and in summer was the smallest. The variation of extremely maximum temperature days was the greatest in summer and wasn’t great in spring, autumn, winter.

Seismic isolation analysis of FPS bearings in spatial lattice shell structures

A theoretical model of a friction pendulum system （FPS） is introduced to examine its application for the seismic isolation of spatial lattice shell structures. An equation of motion of the lattice shell with FPS bearings is developed. Then, seismic isolation studies are performed for both double-layer and single-layer lattice shell structures under different seismic input and design parameters of the FPS. The influence of frictional coefficients and radius of the FPS on seismic performance are discussed. Based on the study, some suggestions for seismic isolation design of lattice shells with FPS bearings are given and conclusions are made which could be helpful in the application of FPS.

Spatial analysis and evaluation of a coal deposit by coupling AHP & GIS techniques

Nowadays,the evaluation of coal deposits becomes crucial,due to many uncontrollable factors,which affect the energy sector.A comparative evaluation of coal deposits is essential for their hierarchical classification regarding their sustainable exploitation,when compared to other coal deposits or competitive fuels,which may be used as alternative solutions for electricity generation.In this paper,a method for spatial analysis and evaluation of a lignite deposit is proposed,by creating four spatial key indicators via GIS analysis,which are then aggregated by applying a weighted linear combination.The analytical hierarchy process is applied to estimate the relative weights of the indicators,in order to perform a weighted cartographic overlay.Through the synthesis of the indicators,an overall,total spatial quality indicator is calculated.The weighted analysis was shown to be more effective compared to the unweighted one,because it can provide more reliable results regarding the exploitation of the examined lignite deposit.The implementation of GIS-based analytical hierarchy process in spatial analysis and evaluation of lignite deposits,in terms of sustainable exploitation,demonstrates that this method can be extensively applied for evaluating the economic potential of mineral deposits.

Application of Integration of Spatial Statistical Analysis with GIS to Regional Economic Analysis

This paper summarizes a few spatial statistical analysis methods for to measuring spatial autocorrelation and spatial association, discusses the criteria for the identification of spatial association by the use of global Moran Coefficient, Local Moran and Local Geary. Furthermore, a user-friendly statistical module, combining spatial statistical analysis methods with GIS visual techniques, is developed in Arcview using Avenue. An example is also given to show the usefulness of this module in identifying and quantifying the underlying spatial association patterns between economic units.

The SIA method for spatial analysis of precipitation in the upper-middle reaches of the Yangtze River

Using geographic information system (GIS) techniques and the newest seasonal and annual average precipitation data of 679 meteorological stations from 1971 to 2000, the multiple regressions equations of the precipitation and topographical variables are established to extract the effect of topography on the annual and seasonal precipitation in the upper-middle reaches of the Yangtze River. Then, this paper uses a successive interpolation approach (SIA), which combines GIS techniques with the multiple regressions, to improve the accuracy of the spatial interpolation of annual and seasonal rainfall. The results are very satisfactory in the case of seasonal rainfall, with the relative error of 6.86%, the absolute error of 13.07 mm, the average coefficient of variation of 0.070, and the correlation coefficient of 0.9675; in the case of annual precipitation, with the relative error of 7.34%, the absolute error of 72.1 mm, the average coefficient of variation of 0.092, and the correlation coefficient of 0.9605. The analyses of annual mean precipitation show that the SIA calculation of 3-5 steps considerably improves the interpolation accuracy, decreasing the absolute error from 211.0 mm to 62.4 mm, the relative error from 20.74% to 5.97%, the coefficient of variation from 0.2312 to 0.0761, and increasing the correlation coefficient from 0.5467 to 0.9619. The SIA iterative results after 50 steps identically converge to the observed precipitation.

An operational modal analysis method in frequency and spatial domain

A frequency and spatial domain decomposition method （FSDD） for operational modal analysis （OMA） is presented in this paper, which is an extension of the complex mode indicator function （CMIF） method for experimental modal analysis （EMA）. The theoretical background of the FSDD method is clarified, Singular value decomposition is adopted to separate the signal space from the noise space. Finally, an enhanced power spectrum density （PSD） is proposed to obtain more accurate modal parameters by curve fitting in the frequency domain. Moreover, a simulation case and an application case are used to validate this method.