Closing the loop: the integration of long-term ambient vibration monitoring in structural engineering design

his study investigated the integration of long-term monitoring into the structural engineering design process to improve the design and operation of civil structures. A survey of civil and structural engineering professionals, conducted as part of this research, identified the cost and complexity of in-situ monitoring as key barriers to their implementation in practice. Therefore, the research focused on the use of ambient vibration monitoring as it is offers a low cost and unobtrusive method for instrumenting new and existing structures. The research was structured around the stages of analysing ambient vibration data using operational modal analysis (OMA), defined in this study as: i) pre-selection of analysis parameters, ii) pre-processing of the data, iii) estimation of the modal parameters, iv) identification of modes of vibration within the modal estimates, and v) using modal parameter estimates as a basis for understanding and quantifying in-service structural behaviour. A method was developed for automating the selecting of the model order, the number of modes of vibrations assumed to be identifiable within the measured dynamic response. This method allowed the modal estimates from different structures, monitoring periods or analysis parameters to be compared, and removed part of the subjectivity identified within current OMA methods. Pre-processing of ambient acceleration responses through filtering was identified as a source of bias within OMA modal estimates. It was shown that this biasing was a result of filtering artefacts within the processed data. Two methods were proposed for removing or reducing the bias of modal estimates induced by filtering artefacts, based on exclusion of sections of the response corrupted by the artefacts or fitting of the artefacts as part of the modal analysis. A new OMA technique, the short-time random decrement technique (ST-RDT) was developed on the basis of the survey of industry perceptions of long-term monitoring and limitations of existing structural monitoring techniques identified within the literature. Key advantages of the ST-RDT are that it allows the uncertainty of modal estimates and any changes in modal behaviour to be quantified through subsampling theory. The ST-RDT has been extensively validated with numerical, experimental and real-world case studies including multi-storey timber buildings and the world's first 3D printed steel bridge. Modal estimates produced using the ST-RDT were used as a basis for developing an automated method of identifying modes of vibration using a probabilistic mixture model. Identification of modes of vibration within OMA estimates was previously a specialized skill. The procedure accounts for the inherent noise associated with ambient vibration monitoring and allows the uncertainty within the modal estimates associated with each mode of vibration to be quantified. Methods of identifying, isolating and quantifying weak non-linear modal behaviour, changes in dynamic behaviour associated with changes in the distributions of mass or stiffness within a structure have been developed based on the fundamental equations of structural dynamics. These methods allow changes in dynamic behaviour associated with thermally-induced changes in stiffness or changes in static loading to be incorporated within the automated identification of modes of vibration. These methods also allow ambient vibration monitoring to be used for estimating structural parameters usually measured by more complex, expensive or delicate sensors. Examples of this include estimating the change in elastic modulus of simple structures with temperature or estimating the location and magnitude of static loads applied to a structure in-service. The methods developed in this study are applicable to a wide range of structural monitoring technologies, are accessible to non-specialist audiences and may be adapted for the monitoring of any civil structure

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

Processed Lake Tahoe and Lake Geneva Water Levels

This processed data set contains the water level elevation in Lake Tahoe (2013-2015) and Lake Geneva (1974-2013). The higher frequency oscillations in the data show the periods and damping of the surface seiches in both lakes. In Geneva, there are three points where this data was measured and how the water level at the different points at the same time varies indicates further properties of the seiche.The time series data used in the Random Decrement Technique (RDT) analysis of Lake Geneva took the form of water elevation data collected at three locations; location 2026, 2027 and 2028. This data had a sampling rate of 10 minutes and was continuously collected between 00:00 on 1 January 1974 and 23:50 on 7 January 2013. The RDT analysis for Lake Tahoe was carried out using water pressure head data collected at a buoy located at Homewood. Three time-series data sets were utilized in the analysis; 30 July 2013 00:00:00 to 6 December 2013 23:59:30, 1 January 2014 00:00:00 to 10 May 2014 23:59:30, 6 January 2015 00:00:00 to 15 May 2015 23:59:30. All data had a sampling rate of 30 seconds.The raw Lake Geneva data was collected by the Swiss Federal Office for the Environment and was provided by Damien Bouffard of the Swiss Federal Institute of Aquatic Science and Technology. The files were received as ascii files, cleaned, and converted to the Matlab files provided here. The raw Lake Tahoe data was provided as csv files by Geoff Schladow of the University of California at Davis and converted to the Matlab files here for the RDT analysis.The processed data is in Matlab format and must be viewed using Matlab. For access to the original data files, please contact Damien Bouffard or Geoff Schladow

Processed Lake Tahoe and Lake Geneva Water Levels

This processed data set contains the water level elevation in Lake Tahoe (2013-2015) and Lake Geneva (1974-2013). The higher frequency oscillations in the data show the periods and damping of the surface seiches in both lakes. In Geneva, there are three points where this data was measured and how the water level at the different points at the same time varies indicates further properties of the seiche

Identification of seven new prostate cancer susceptibility loci through a genome-wide association study

Prostate cancer (PrCa) is the most frequently diagnosed male cancer in developed countries. To identify common PrCa susceptibility alleles, we have previously conducted a genome-wide association study in which 541, 129 SNPs were genotyped in 1,854 PrCa cases with clinically detected disease and 1,894 controls. We have now evaluated promising associations in a second stage, in which we genotyped 43,671 SNPs in 3,650 PrCa cases and 3,940 controls, and a third stage, involving an additional 16,229 cases and 14,821 controls from 21 studies. In addition to previously identified loci, we identified a further seven new prostate cancer susceptibility loci on chromosomes 2, 4, 8, 11, and 22 (P=1.6×10−8 to P=2.7×10−33)