scholarly journals Bayesian updating of subsurface spatial variability for improved prediction of braced excavation response

2019 ◽  
Vol 56 (8) ◽  
pp. 1169-1183 ◽  
Author(s):  
M.K. Lo ◽  
Y.F. Leung
Keyword(s):  
Spatial Variability ◽  
Random Field ◽  
Bayesian Updating ◽  
Field Measurements ◽  
Longitudinal Direction ◽  
Prediction Intervals ◽  
Model Bias ◽  
Efficient Manner ◽  
Actual Performance ◽  
Hypothetical Scenario

This paper introduces an approach that utilizes field measurements to update the parameters characterizing spatial variability of soil properties and model bias, leading to refined predictions for subsequent construction stages. It incorporates random field simulations and a surrogate modeling technique into the Bayesian updating framework, while the spatial and stage-dependent correlations of model bias can also be considered. The approach is illustrated using two cases of multi-stage braced excavations, one being a hypothetical scenario and the other from a case study in Hong Kong. Making use of all the deflection measurements along an inclinometer, the principal components of the random field and model bias factors can be updated efficiently as the instrumentation data become available. These various sources of uncertainty do not only cause discrepancies between prior predictions and actual performance, but can also lead to response mechanisms that cannot be captured by deterministic approaches, such as distortion of the wall along the longitudinal direction of the excavation. The proposed approach addresses these issues in an efficient manner, producing prediction intervals that reasonably encapsulate the response uncertainty as shown in the two cases. The capability to continuously refine the response estimates and prediction intervals can help support the decision-making process as the construction progresses.

Investigation of Spatial Variability on Strut Diameters of Additively Manufactured Lattice Structures

2020 ◽  
Author(s):  
Recep M. Gorguluarslan ◽  
O. Utku Gungor
Keyword(s):  
Spatial Variability ◽  
Random Field ◽  
Extrusion Process ◽  
Optical Microscope ◽  
Measured Data ◽  
Good Representation ◽  
Lattice Structures ◽  
Spatial Correlations ◽  
Correlation Lengths ◽  
Geometric Uncertainties

Abstract In this study, the influence of the spatial variability of geometric uncertainties on the strut members of the lattice structures fabricated by additive manufacturing is investigated. Individual struts are fabricated with various printing angles and diameters using a material extrusion process and PLA material. The diameter values of the fabricated samples are measured along the printing and radial directions at each layer under an optical microscope. Spatial correlations are characterized based on the measurements using the experimental autocorrelation function. Candidate autocorrelation functions are fitted to the measured data to identify the best fitted one for each diameter parameter and the corresponding correlation lengths are evaluated for random field. The applicability of the Karhunen-Loeve expansion (KLE) is investigated to reduce the dimensionality of the random field discretization. The results show that the diameters of the strut members at each layer are spatially dependent and the KLE method was found to give a good representation of the random field.

scholarly journals Geostatistical integration to improve representativeness of satellite precipitation estimates and field measurements

10.29007/glj1 ◽  
2019 ◽  
Author(s):  
Felipe-Omar Tapia-Silva
Keyword(s):  
Spatial Variability ◽  
Satellite Data ◽  
Field Measurements ◽  
Auxiliary Variable ◽  
Measured Parameter ◽  
Geostatistical Methods ◽  
Variability Pattern ◽  
Distance Weighted ◽  
Precipitation Estimates ◽  
Leave One Out

Since the network of rainfall gauges and ground radars is generally not dense enough, satellite data have been used to estimate Precipitation (P). These data have the ability to capture the spatial variability pattern of the parameter, but are often inaccurate in relation to the value of the field measured parameter. Therefore, geostatistical methods were evaluated to improve the spatial representativeness of field measurements (FM) and satellite estimates. The work has been made for a hydrological sub region in the Mexican tropic. The geostatistical methods used to interpolate P-FM were ordinary kriging (KO), universal kriging (KU) and regression kriging (RK) as well as the Inverse Distance Weighted (IDW) mechanical interpolator for comparison purposes. Furthermore, the values at the pixel centers of the Tropical Rainfall Monitoring Mission (TRMM) images were interpolated using OK and evaluated using leave-one-out cross validation (LOO-CV). The best LOO-CV evaluated method consisted of the RK interpolation of the point FM taking as auxiliary variable the OK interpolation of the TRMM cell centers. It is concluded that the geostatistical integration between rainfall estimates from satellite data and FM data is promising because satellite information has the ability to capture spatial variability and the point FM add accuracy to the results. These characteristics combined can produce a P product useful for modeling activities and environmental management.

scholarly journals Development of an analytical model to predict oil reservoirs performance using mechanical waves propagation

2021 ◽  
Author(s):  
Hesham A. Abu Zaid ◽  
◽  
Sherif A. Akl ◽  
Mahmoud Abu El Ela ◽  
Ahmed El-Banbi ◽  
...  
Keyword(s):  
Oil Recovery ◽  
Field Measurements ◽  
Field Trials ◽  
Oil Field ◽  
Reservoir Properties ◽  
Actual Performance ◽  
Reservoir Performance ◽  
Mechanical Waves ◽  
Incremental Oil Recovery ◽  
Wave Induced

The mechanical waves have been used as an unconventional enhanced oil recovery technique. It has been tested in many laboratory experiments as well as several field trials. This paper presents a robust forecasting model that can be used as an effective tool to predict the reservoir performance while applying seismic EOR technique. This model is developed by extending the wave induced fluid flow theory to account for the change in the reservoir characteristics as a result of wave application. A MATLAB program was developed based on the modified theory. The wave’s intensity, pressure, and energy dissipation spatial distributions are calculated. The portion of energy converted into thermal energy in the reservoir is assessed. The changes in reservoir properties due to temperature and pressure changes are considered. The incremental oil recovery and reduction in water production as a result of wave application are then calculated. The developed model was validated against actual performance of Liaohe oil field. The model results show that the wave application increases oil production from 33 to 47 ton/day and decreases water-oil ratio from 68 to 48%, which is close to the field measurements. A parametric analysis is performed to identify the important parameters that affect reservoir performance under seismic EOR. In addition, the study determines the optimum ranges of reservoir properties where this technique is most beneficial.

scholarly journals Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks

2014 ◽  
Vol 11 (23) ◽  
pp. 6827-6840 ◽  
Author(s):  
M. Réjou-Méchain ◽  
H. C. Muller-Landau ◽  
M. Detto ◽  
S. C. Thomas ◽  
T. Le Toan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Spatial Scales ◽  
Forest Biomass ◽  
Remote Sensing Data ◽  
Field Measurements ◽  
Forest Degradation ◽  
Forest Carbon ◽  
Mitigation Strategies ◽  
Large Plot

Abstract. Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8–50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mg ha–1) at spatial scales ranging from 5 to 250 m (0.025–6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20–400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial "dilution" bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise.

Stochastic finite element analysis of rockfill dam considering spatial variability of dam material porosity

2019 ◽  
Vol 36 (9) ◽  
pp. 2929-2959
Author(s):  
Hui Chen ◽  
Donghai Liu
Keyword(s):  
Finite Element ◽  
Constitutive Model ◽  
Spatial Variability ◽  
Random Field ◽  
Model Parameters ◽  
Mechanical Parameters ◽  
Stochastic Finite Element ◽  
Content Type ◽  
Rockfill Dam ◽  
Stochastic Fem

Purpose The purpose of this study is to develop a stochastic finite element method (FEM) to solve the calculation precision deficiency caused by spatial variability of dam compaction quality. Design/methodology/approach The Choleski decomposition method was applied to generate constraint random field of porosity. Large-scale laboratory triaxial tests were conducted to determine the quantitative relationship between the dam compaction quality and Duncan–Chang constitutive model parameters. Based on this developed relationship, the constraint random fields of the mechanical parameters were generated. The stochastic FEM could be conducted. Findings When the fully random field was simulated without the restriction effect of experimental data on test pits, the spatial variabilities of both displacement and stress results were all overestimated; however, when the stochastic FEM was performed disregarding the correlation between mechanical parameters, the variabilities of vertical displacement and stress results were underestimated and variation pattern for horizontal displacement also changed. In addition, the method could produce results that are closer to the actual situation. Practical implications Although only concrete-faced rockfill dam was tested in the numerical examples, the proposed method is applicable for arbitrary types of rockfill dams. Originality/value The value of this study is that the proposed method allowed for the spatial variability of constitutive model parameters and that the applicability was confirmed by the actual project.

scholarly journals Retrieval and Validation of Water Turbidity at Metre-Scale Using Pléiades Satellite Data: A Case Study in the Gironde Estuary

2020 ◽  
Vol 12 (6) ◽  
pp. 946 ◽  
Author(s):  
Yafei Luo ◽  
David Doxaran ◽  
Quinten Vanhellemont
Keyword(s):  
Spatial Variability ◽  
Satellite Data ◽  
Near Infrared ◽  
Atmospheric Correction ◽  
Field Measurements ◽  
Quality Parameters ◽  
Satellite Constellations ◽  
Water Turbidity ◽  
Gironde Estuary ◽  
The Difference

This study investigated the use of frequent metre-scale resolution Pléiades satellite imagery to monitor water quality parameters in the highly turbid Gironde Estuary (GE, SW France). Pléiades satellite data were processed and analyzed in two representative test sites of the GE: 1) the maximum turbidity zone and 2) the mouth of the estuary. The main objectives of this study were to: (i) validate the Dark Spectrum Fitting (DSF) atmospheric correction developed by Vanhellemont and Ruddick (2018) applied to Pléiades satellite data recorded over the GE; (ii) highlight the benefits of frequent metre-scale Pléiades observations in highly turbid estuaries by comparing them to previously validated satellite observations made at medium (250/300 m for MODIS, MERIS, OLCI data) and high (20/30 m for SPOT, OLI and MSI data) spatial resolutions. The results show that the DSF allows for an accurate retrieval of water turbidity by inversion of the water reflectance in the near-infrared (NIR) and red wavebands. The difference between Pléiades-derived turbidity and field measurements was proven to be in the order of 10%. To evaluate the spatial variability of water turbidity at metre scale, Pléiades data at 2 m resolution were resampled to 20 m and 250 m to simulate typical coarser resolution sensors. On average, the derived spatial variability in the GE is lower than or equal to 10% and 26%, respectively, in 20-m and 250-m aggregated pixels. Pléiades products not only show, in great detail, the turbidity features in the estuary and river plume, they also allow to map the turbidity inside ports and capture the complex spatial variations of turbidity along the shores of the estuary. Furthermore, the daily acquisition capabilities may provide additional advantages over other satellite constellations when monitoring highly dynamic estuarine systems.

scholarly journals Combining Performance Testing and Metadata Models to Support Fault Detection and Diagnostics in Smart Buildings

2019 ◽  
Vol 2 (3) ◽  
pp. 28
Author(s):  
Elena Markoska ◽  
Aslak Johansen ◽  
Mikkel Baun Kjærgaard ◽  
Sanja Lazarova-Molnar ◽  
Muhyiddine Jradi ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Fault Detection ◽  
Performance Test ◽  
Performance Testing ◽  
Performance Tests ◽  
Actual Performance ◽  
Hypothetical Scenario ◽  
Wide Range ◽  
Promising Practice

Performance testing of components and subsystems of buildings is a promising practice for increasing energy efficiency and closing gaps between intended and actual performance of buildings. A typical shortcoming of performance testing is the difficulty of linking a failing test to a faulty or underperforming component. Furthermore, a failing test can also be linked to a wrongly configured performance test. In this paper, we present Building Metadata Performance Testing (BuMPeT), a method that addresses this shortcoming by using building metadata models to extend performance testing with fault detection and diagnostics (FDD) capabilities. We present four different procedures that apply BuMPeT to different data sources and components. We have applied the proposed method to a case study building, located in Denmark, to test its capacity and benefits. Additionally, we use two real case scenarios to showcase examples of failing performance tests in the building, as well as discovery of causes of underperformance. Finally, to examine the limits to the benefits of the applied procedure, a detailed elaboration of a hypothetical scenario is presented. Our findings demonstrate that the method has potential and it can serve to increase the energy efficiency of a wide range of buildings.

An Effective Random Field Characterization for Probability Analysis and Design

2008 ◽  
Author(s):  
Zhimin Xi ◽  
Byeng D. Youn
Keyword(s):  
Spatial Variability ◽  
Random Field ◽  
Goodness Of Fit ◽  
Random Parameter ◽  
Probability Analysis ◽  
Field Variation ◽  
Random Parameters ◽  
Goodness Of Fit Test ◽  
Analysis And Design ◽  
Engineering Design Problems

So far manufacturing tolerance variability over samples has been widely considered in many engineering design problems. Traditionally the tolerance variability is modeled as a spatially independent random parameter although the variability is a function of spatial variables (x, y, and z) in many engineering applications. Little attention has been paid to spatial variability (or random field) in manufacturing and operational conditions, which may dominantly affect system performances in smaller scale applications. This paper presents an effective approach to characterize a random field for probability analysis and design. The Proper Orthogonal Decomposition (POD) method is employed to extract the important signatures of the random field over product samples. A normalized posteriori error is defined to automatically decide the minimal number of the important signatures while preserving a prescribed accuracy in approximating the random field. The random projected values of the spatial variability over the samples onto each important signature are modeled as a random parameter. The signatures and corresponding random parameters are thus used for modeling the random field. A Chi-Squae goodness-of-fit test is used for determining statistical models of random parameters. This proposed approach can facilitate to characterize the random field for probability analysis and design. By modeling the random field with the most significant random signatures, the Eigenvector Dimension Reduction (EDR) method can be employed for probability analysis because of its relatively high efficiency and accuracy. Two examples (one beam and Micro-Electro-Mechanical Systems (MEMS) bistable mechanism) are used to illustrate the effectiveness of the proposed approach while considering only a geometric random field. Compared to Monte Carlo Simulation (MCS), the proposed random field approach is appeared to be very accurate and efficient. Moreover, the results show that the random field variation cannot be neglected for probability analysis and design practices.

scholarly journals Understanding Intra-Annual Dynamics of Ecosystem Services Using Satellite Image Time Series

2020 ◽  
Vol 12 (4) ◽  
pp. 710 ◽  
Author(s):  
Trinidad del Río-Mena ◽  
Louise Willemen ◽  
Anton Vrieling ◽  
Andy Nelson
Keyword(s):  
Ecosystem Services ◽  
Spatial Variability ◽  
Ecosystem Service ◽  
Vegetation Cover ◽  
Agricultural Land ◽  
Supply And Demand ◽  
Satellite Image ◽  
Field Measurements ◽  
Temporal Variations ◽  
The Impact

Landscape processes fluctuate over time, influencing the intra-annual dynamics of ecosystem services. However, current ecosystem service assessments generally do not account for such changes. This study argues that information on the dynamics of ecosystem services is essential for understanding and monitoring the impact of land management. We studied two regulating ecosystem services (i. erosion prevention, ii. regulation of water flows) and two provisioning services (iii. provision of forage, iv. biomass for essential oil production) in thicket vegetation and agricultural fields in the Baviaanskloof, South Africa. Using models based on Sentinel-2 data, calibrated with field measurements, we estimated the monthly supply of ecosystem services and assessed their intra-annual variability within vegetation cover types. We illustrated how the dynamic supply of ecosystem services related to temporal variations in their demand. We also found large spatial variability of the ecosystem service supply within a single vegetation cover type. In contrast to thicket vegetation, agricultural land showed larger temporal and spatial variability in the ecosystem service supply due to the effect of more intensive management. Knowledge of intra-annual dynamics is essential to jointly assess the temporal variation of supply and demand throughout the year to evaluate if the provision of ecosystem services occurs when most needed.

Sign in / Sign up

Export Citation Format

Share Document