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In order to provide a theoretical basis for the precise finite element method (FEM) analysis of the long-range operation structural behavior of concrete faced rockfill dams (CFRDs), an improved creep model and the parameter spatial estimation method under digital construction were proposed. Creep characteristics of rockfill materials were studied by large-scale indoor triaxial creep test. The precision of traditional creep model was analyzed, and an improved model was proposed. The average fitting accuracy of the test results improved by 32.9% compared to the traditional model, which greatly improves the accuracy of creep characterization. Then, two models were applied for the FEM calculations of the dam under different porosities. The maximum relative difference of dam settlement brought by the two models was 60.6%. On this basis, the abnormal early warning curves of dam deformation and face deflection were proposed. Furthermore, the regression models between porosity and improved model parameters were established. Finally, the parameter spatial estimation of the improved model was achieved relying on the digital construction technology. This method takes into account the spatial variations of the mechanical parameters of dam materials, thus aligning more closely with the actual conditions. [ABSTRACT FROM AUTHOR]

Accurate spatial estimation of extremes is an increasingly important topic in environmental research and risk assessment. Conditional bias (CB)-penalized kriging (CBPK) improves such estimation by minimizing linearly weighted sum of error variance and variance of Type-II error. However, CBPK requires skillful prescription of the weight for the CB penalty which is a significant challenge in practice. In this paper, we describe an extension of CBPK, referred to herein as adaptive conditional bias-penalized kriging (ACBPK), which objectively prescribes the weight for improved estimation of extremes without deteriorating performance in the unconditional mean squared error sense. For comparative evaluation in the real world, cross validation experiments were carried out for precipitation estimation using hourly rain gauge data in the Arkansas-Red River Basin (AB), central Texas (TX) and southeastern US (SE) areas. The results show that CB is detected for about 26, 24 and 25% of all data points in the AB, TX and SE cases, respectively, and that, given detection of CB, ACBPK reduces root mean square error of hourly precipitation exceeding 12.7 mm by 15, 21 and 9% and hourly precipitation exceeding 25.4 mm by 14, 26 and 10% relative to ordinary kriging (OK) for the AB, TX and SE cases, respectively. The overall findings indicate that, if accurate spatial estimation in the tails of the distribution is important or accurate modeling of spatiotemporally-varying correlation structure is a challenge, ACBPK should be favored over OK. [ABSTRACT FROM AUTHOR]

Air pollutants harm human health and the environment. Nowadays, deploying an air pollution monitoring network in many urban areas could provide real-time air quality assessment. However, these networks are usually sparsely distributed and the sensor calibration problems that may appear over time lead to missing and wrong measurements. There is an increasing interest in developing air quality modelling methods to minimize measurement errors, predict spatial and temporal air quality, and support more spatially-resolved health effect analysis. This research aims to evaluate the ability of three feed-forward neural network architectures for the spatial prediction of air pollutant concentrations using the measures of an air quality monitoring network. In addition to these architectures, Support Vector Machines and geostatistical methods (Inverse Distance Weighting and Ordinary Kriging) were also implemented to compare the performance of neural network models. The evaluation of the methods was performed using the historical values of seven air pollutants (Nitrogen monoxide, Nitrogen dioxide, Sulphur dioxide, Carbon monoxide, Ozone, and particulate matters with size less than or equal to 2.5 μ m and to 10 μ m) from an urban air quality monitoring network located at the metropolitan area of Madrid (Spain). To assess and compare the predictive ability of the models, three estimation accuracy indicators were calculated: the Root Mean Squared Error, the Mean Absolute Error, and the coefficient of determination. FFNN-based models are superior to geostatistical methods and slightly better than Support Vector Machines for fitting the spatial correlation of air pollutant measurements. [ABSTRACT FROM AUTHOR]

Mineral resource estimation is an integral part of making informed decisions while evaluating a mining operation's feasibility. Geostatistical tools estimate geological features with the assumptions of first and second-order stationarity. Kriging is considered the best linear unbiased estimation technique for modelling geological features; however, in domains where data is non-Gaussian, and features are complex, the assumption of stationarity and the linearity of kriging can lead to suboptimal estimates. This manuscript presents a hybrid machine learning and geostatistical algorithm to improve estimation in complex domains. Elliptical radial basis function neural networks (ERBFN) take advantage of non-stationary functions to generate geological estimates. An ERBFN does not require the assumption of stationarity, and the only input features required are the spatial coordinates of the known data. The proposed hybrid estimation considers the machine learning estimate as exhaustive secondary data in ordinary intrinsic collocated cokriging, taking advantage of kriging's exactitude while including the non-stationary features modelled in the ERBFN. The principle results of integrating geostatistics and machine learning indicate an improved estimation technique in domains with complex features, poorly defined domains, or non-Gaussian data. The major conclusion from this paper is that using the proposed hybrid algorithm can improve mineral resource estimations. [ABSTRACT FROM AUTHOR]

A growing number of studies in the spatial estimation of geological features use machine learning (ML) models, as these models promise to provide efficient solutions for estimation especially in non-Gaussian, non-stationary and complex cases. However, these models have two major limitations: (1) the data are considered to be independent and identically distributed (or spatially uncorrelated), and (2) the data are not reproduced at their locations. Kriging, on the other hand, has a long history of generating unbiased estimates with minimum error variance at unsampled locations. Kriging assumes stationarity and linearity. This study proposes a methodology that combines kriging and ML models to mitigate the disadvantages of each method and obtain more accurate estimates. In the proposed methodology, a stacked ensemble model, which is also referred to as the super learner (SL) model, is applied for ML modeling. We have shown how the estimates generated by the SL model and estimates obtained from kriging can be combined through a weighting function based on a kriging variance. The weights are optimized using the sequential quadratic programming. The proposed methodology is demonstrated in two synthetic case studies containing data with non-stationarity and non-Gaussian features; a real case study using a dataset from an oilsands deposit is also presented. The performance of the combined model is compared with the SL model and kriging using the coefficient of determination (R-squared), root mean squared error, and mean absolute error criteria. The combined model appears to yield more accurate estimates than the ones generated by SL model and kriging in all cases. [ABSTRACT FROM AUTHOR]

Finding accurate methods for estimating and mapping land prices at the macro-scale based on publicly accessible and low-cost spatial data is an essential step in producing a meaningful reference for regional planners. This asset would assist them in making economically justified decisions in favor of key investors for development projects and post-disaster recovery efforts. Since 2005, the Ministry of Land, Infrastructure, and Transport of Japan has made land price data open to the public in the form of observations at dispersed locations. Although this data is useful, it does not provide complete information at every site for all market participants. Therefore, estimating and mapping land prices based on sound statistical theories is required. This paper presents a comparative study of spatial prediction of land prices in 2015 in Fukushima prefecture based on geostatistical methods and machine learning algorithms. Land use, elevation, and socioeconomic factors, including population density and distance to railway stations, were used for modeling. Results show the superiority of the random forest algorithm. Overall, land prices are distributed unevenly across the prefecture with the most expensive land located in the western region characterized by flat topography and the availability of well-connected and highly dense economic hotspots. [ABSTRACT FROM AUTHOR]

Soil drainage classes spatial mapping is of great interest since drainage has direct effects on crop productivity and hydrological modelling. However, the prediction of this categorical variable often requires a laborious and expensive sampling over large areas. There is thus a need for a methodology that is able to combine several sources of information to improve the prediction. Bayesian maximum entropy (BME) has become a complete framework in the context of space-time prediction. This method proposes solutions to combine several sources of data no matter what the nature of information is. However, the various attempts that were made for adapting the BME methodology to categorical variables and mixed random fields faced some limitations, as a high computational burden. The main objective of this paper is to overcome this limitation by generalizing the Bayesian data fusion (BDF) theoretical framework to categorical variables, which is a simplification of the BME method through the conditional independence hypothesis. The BDF methodology for categorical variables is first described and then applied to a practical case study: the estimation of soil drainage classes using a soil map and point observations around Mechelen (Belgium). The BDF approach is compared to BME along with more classical approaches, as indicator cokriging (ICK) and logistic regression. Estimators are compared using various indicators, namely the percentage of correctly classified locations and the average highest probability. Although BDF methodology for categorical variables is a simplification of BME approach, both methods lead to very close results and have strong advantages compared to ICK and logistic regression. [ABSTRACT FROM AUTHOR]

A common assumption in geostatistics is that the underlying joint distribution of possible values of a geological attribute at different locations is stationary within a homogeneous domain. This joint distribution is commonly modeled as multi-Gaussian, with correlations defined by a stationary covariance function. This results in attribute maps that fail to reproduce local changes in the mean, in the variance and, particularly, in the spatial continuity. The proposed alternative is to build local distributions, variograms, and correlograms. These are inferred by weighting the samples depending on their distance to selected locations. The local distributions are locally transformed into Gaussian distributions embedding information on the local histogram. The distance weighted experimental variograms and correlograms are able to adapt to local changes in the direction and range of spatial continuity. The automatically fitted local variogram models and the local Gaussian transformation parameters are used in spatial estimation algorithms assuming local stationarity. The resulting maps are rich in nonstationary spatial features. The proposed process implies a higher computational effort than traditional stationary techniques, but if data availability allows for a reliable inference of the local distributions and statistics, a higher accuracy of estimates can be achieved. [ABSTRACT FROM AUTHOR]

This paper describes the optimization of the beam stiffeners attached to plates in an eigenfrequency problem. The solution space is estimated using the Kriging method. A finite element analysis is carried out to evaluate the objective function at the sample points used for the estimation. The gradient method is used as a local optimizer. The Kriging estimation incurs relatively low cost, and is easy to combine with the gradient method. In this paper, we solve eigenfrequency optimization problems for a fully supported plate to maximize the minimum eigenfrequency and the difference between the 1st- and 2nd-order eigenfrequency. An optimization problem for an L-shaped plate with an eigenfrequency constraint is also solved. Good solutions are obtained for each example, and all the optimizations for these problems can be done at a lower computational cost. The results highlight the effectiveness of the method to solve the eigenfrequency optimization problems for the stiffened plate. [ABSTRACT FROM AUTHOR]