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• W2995493314 abstract "Spatio-temporal data presents many challenging problems as they require implementation of a proper stochastic model and estimation procedure. Spatio-temporal data are often indirectly observed through non-linear measurement operators, inherently multivariate with complex cor- relation structures, and collected in huge quantities. An example of such data is the roller measurement value (RMV) sequentially collected by modern earthwork compaction rollers. Modern earthwork compaction rollers collect a virtually continuous stream of data that can be used for quality assurance (QA) and quality control (QC) of the compaction process. RMVs can also be used for intelligent compaction (IC), adjusting operation parameters during construction to achieve homogeneous compaction. The goal of IC is to reduce compaction time and to improve compaction quality by identifying soft spots. A statistical model of the site is needed to build software with a robust implementation to identify these soft spots “on the fly”. First, a stochastic model of one compaction layer is developed and two potential estimation procedures are discussed. The first procedure discussed is penalized likelihood using a smooth- ing parameter chosen by generalized cross validation. Second, a spatial backfitting estimation procedure is proposed. Backfitting is an iterative estimation procedure where fixed effects and random effects are repeatedly updated until convergence of the estimates. Investigating the complex covariance structures of proposed models for RMVs led to developing an extension of the rank one update of Moore–Penrose pseudoinverses to larger rank updates. The Moore–Penrose pseudoinverse of A + X1 X∗ , where A, X1 , X2 are complex matrices are 2 given under various assumptions. We use the result to derive the Moore–Penrose pseudoinverse for the quasi-Kronecker structured matrix bdiag(Ak ) + uv∗ ⊗ E with p complex matrices Ak of dimension n × m, two complex p-vectors u and v and a complex matrix E of dimension n × m. We next propose a sequential, spatial mixed-effects model and a sequential, spatial backfitting routine for estimation of the modeling terms. Estimation of sequential, spatial processes is quite complex and several backfitting routines are presented utilizing quasi-Kronecker structured ma- trices and the previously developed Moore–Penrose pseudoinverses. Next, the estimated fields produced from the sequential, spatial backfitting procedure are ana- lyzed using a multiresolution scale space analysis. This image analysis is proposed as a viable solution to improved IC and QA of the compaction process for RMVs. Finally, an atypically compacted test bed of atypical dimensions is investigated to ascertain the influence of driving direction on RMVs. Exploratory analysis is performed and empirical semivariograms estimated. Then the sequential, spatial backfitting procedure is applied to the data to test the importance of driving direction." @default.
• W2995493314 created "2019-12-26" @default.
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• W2995493314 date "2013-01-01" @default.
• W2995493314 modified "2023-09-25" @default.
• W2995493314 title "On the modeling and analysis of sequential observations of spatial processes with application to modern earthwork compaction" @default.
• W2995493314 doi "https://doi.org/10.5167/uzh-164270" @default.
• W2995493314 hasPublicationYear "2013" @default.
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