Vibration based seismic damage identification procedure for a steel concentrically braced frame structure

An approach for seismic damage identification of a single-storey steel concentrically braced frame (CBF) structure is presented through filtering and double integration of a recorded acceleration signal. A band-pass filter removes noise from the acceleration signal followed by baseline correction being used to reduce the drift in velocity and displacement during numerical integration. The pre-processing achieves reliable numerical integration that predicts the displacement response accurately when compared to the measured lateral in-plane displacement of the CBF structure. The lateral displacement of the CBF structure is used to infer buckling and yielding of bracing members through seismic tests. The level of interstorey drift of the CBF during a seismic excitation allows the yield and buckling of the bracing members to be identified and indirectly detects damage based on exceedance of calculated displacement limits. The calculated buckling and yielding displacement threshold limits used to identify damage are demonstrated to accurately identify initial buckling and yielding in the bracing members.

Two-stage mixed discrete-continuous identification of radial basis function (RBF) neural models for nonlinear systems

The identification of nonlinear dynamic systems using radial basis function (RBF) neural models is studied in this paper. Given a model selection criterion, the main objective is to effectively and efficiently build a parsimonious compact neural model that generalizes well over unseen data. This is achieved by simultaneous model structure selection and optimization of the parameters over the continuous parameter space. It is a mixed-integer hard problem, and a unified analytic framework is proposed to enable an effective and efficient two-stage mixed discrete-continuous; identification procedure. This novel framework combines the advantages of an iterative discrete two-stage subset selection technique for model structure determination and the calculus-based continuous optimization of the model parameters. Computational complexity analysis and simulation studies confirm the efficacy of the proposed algorithm.

A two-stage algorithm for identification of nonlinear dynamic systems

This paper investigates the two-stage stepwise identification for a class of nonlinear dynamic systems that can be described by linear-in-the-parameters models, and the model has to be built from a very large pool of basis functions or model terms. The main objective is to improve the compactness of the model that is obtained by the forward stepwise methods, while retaining the computational efficiency. The proposed algorithm first generates an initial model using a forward stepwise procedure. The significance of each selected term is then reviewed at the second stage and all insignificant ones are replaced, resulting in an optimised compact model with significantly improved performance. The main contribution of this paper is that these two stages are performed within a well-defined regression context, leading to significantly reduced computational complexity. The efficiency of the algorithm is confirmed by the computational complexity analysis, and its effectiveness is demonstrated by the simulation results.

Convergence of the iterative algorithm for a general Hammerstein system identification

The convergence of the iterative identification algorithm for a general Hammerstein system has been an open problem for a long time. In this paper, it is shown that the convergence can be achieved by incorporating a regularization procedure on the nonlinearity in addition to a normalization step on the parameters.

Fast automatic two-stage nonlinear model identification based on the extreme learning machine

It is convenient and effective to solve nonlinear problems with a model that has a linear-in-the-parameters (LITP) structure. However, the nonlinear parameters (e.g. the width of Gaussian function) of each model term needs to be pre-determined either from expert experience or through exhaustive search. An alternative approach is to optimize them by a gradient-based technique (e.g. Newton’s method). Unfortunately, all of these methods still need a lot of computations. Recently, the extreme learning machine (ELM) has shown its advantages in terms of fast learning from data, but the sparsity of the constructed model cannot be guaranteed. This paper proposes a novel algorithm for automatic construction of a nonlinear system model based on the extreme learning machine. This is achieved by effectively integrating the ELM and leave-one-out (LOO) cross validation with our two-stage stepwise construction procedure [1]. The main objective is to improve the compactness and generalization capability of the model constructed by the ELM method. Numerical analysis shows that the proposed algorithm only involves about half of the computation of orthogonal least squares (OLS) based method. Simulation examples are included to confirm the efficacy and superiority of the proposed technique.

Connectivity Mapping for Candidate Therapeutics Identification Using Next Generation Sequencing RNA-Seq Data

The advent of next generation sequencing technologies (NGS) has expanded the area of genomic research, offering high coverage and increased sensitivity over older microarray platforms. Although the current cost of next generation sequencing is still exceeding that of microarray approaches, the rapid advances in NGS will likely make it the platform of choice for future research in differential gene expression. Connectivity mapping is a procedure for examining the connections among diseases, genes and drugs by differential gene expression initially based on microarray technology, with which a large collection of compound-induced reference gene expression profiles have been accumulated. In this work, we aim to test the feasibility of incorporating NGS RNA-Seq data into the current connectivity mapping framework by utilizing the microarray based reference profiles and the construction of a differentially expressed gene signature from a NGS dataset. This would allow for the establishment of connections between the NGS gene signature and those microarray reference profiles, alleviating the associated incurring cost of re-creating drug profiles with NGS technology. We examined the connectivity mapping approach on a publicly available NGS dataset with androgen stimulation of LNCaP cells in order to extract candidate compounds that could inhibit the proliferative phenotype of LNCaP cells and to elucidate their potential in a laboratory setting. In addition, we also analyzed an independent microarray dataset of similar experimental settings. We found a high level of concordance between the top compounds identified using the gene signatures from the two datasets. The nicotine derivative cotinine was returned as the top candidate among the overlapping compounds with potential to suppress this proliferative phenotype. Subsequent lab experiments validated this connectivity mapping hit, showing that cotinine inhibits cell proliferation in an androgen dependent manner. Thus the results in this study suggest a promising prospect of integrating NGS data with connectivity mapping. © 2013 McArt et al.

Impact of Testing Procedure on Permeability of Compacted Soils

The commonly used British Standard constant head triaxial permeability (BS) test, for permeability testing of fine grained soils, is known to have a relatively long test duration. Consequently, a reduction in the required time for permeability test provides potential cost savings, to the construction industry (specifically, for use during Construction Quality Control (CQA) of landfill mineral liners). The purpose of this article is to investigate and evaluate alternative short duration testing methods for the measurement of the permeability of fine grained soils.

As part of the investigation the feasibility of an existing method of short duration permeability test, known as the Accelerated Permeability (AP) test was assessed and compared with permeability measured using British Standard method (BS) and Ramp Accelerated Permeability (RAP). Four different fine grained materials, of a variety of physical properties were compacted at various moisture contents to produced analogous samples for testing using three the three different methodologies. Fabric analysis was carried out on specimens derived from post-test samples using Mercury Intrusion Porosimetry (MIP) and Scanning Electron Microscope (SEM) to assess the effects of testing methodology on soil structure. Results showed that AP testing in general under predicts permeability values derived from the BS test due to large changes in structure of the soil caused by AP test methodology, which is also validated using MIP and SEM observations. RAP testing, in general provides an improvement to the AP test but still under-predicts permeability values. The potential savings in test duration are shown to be relatively minimal for both the AP and RAP tests.

Identification of a high-risk haplotype for the dystrobrevin binding protein 1 (DTNBP1) gene in the Irish study of high-density schizophrenia families

A recent report showed significant associations between several SNPs in a previously unknown EST cluster with schizophrenia. (1). The cluster was identified as the human dystrobrevin binding protein 1 gene (DTNBP1) by sequence database comparisons and homology with mouse DTNBP1. (2). However, the linkage disequilibrium (LD) among the SNPs in DTNBP1 as well as the pattern of significant SNP-schizophrenia association was complex. This raised several questions such as the number of susceptibility alleles that may be involved and the size of the region where the actual disease mutation(s) could be located. To address these questions, we performed different single-marker tests on the 12 previously studied and 2 new SNPs in DTNBP1 that were re-scored using an improved procedure, and performed a variety of haplotype analyses. The sample consisted of 268 Irish multiplex families selected for high density of schizophrenia. Results suggested a simple structure where the LD in the target region could be explained by 6 haplotypes that together accounted for 96% of haplotype diversity in the whole sample. From these six, a single high-risk haplotype was identified that showed a significant association with schizophrenia and explained the pattern of significant findings in the analyses with individual markers. This haplotype was 30 kb long, had a large effect, could be measured with two tag SNPs only, had a frequency of 6% in our sample, seemed to be of relatively recent origin in evolutionary terms, and was equally distributed over Ireland. Implications of these findings for follow-up and replication studies are discussed.

A new and less destructive laboratory procedure for the physical separation of distal glass tephra shards from sediments

Tephrochronology, a key tool in the correlation of Quaternary sequences, relies on the extraction of tephra shards from sediments for visual identification and high-precision geochemical comparison. A prerequisite for the reliable correlation of tephra layers is that the geochemical composition of glass shards remains unaltered by natural processes (e.g. chemical exchange in the sedimentary environment) and/or by laboratory analytical procedures. However, natural glasses, particularly when in the form of small shards with a high surface to volume ratio, are prone to chemical alteration in both acidic and basic environments. Current techniques for the extraction of distal tephra from sediments involve the ‘cleaning’ of samples in precisely such environments and at elevated temperatures. The acid phase of the ‘cleaning’ process risks alteration of the geochemical signature of the shards, while the basic phase leads to considerable sample loss through dissolution of the silica network. Here, we illustrate the degree of alteration and loss to which distal tephras may be prone, and introduce a less destructive procedure for their extraction. This method is based on stepped heavy liquid flotation and which results in samples of sufficient quality for analysis while preserving their geochemical integrity. In trials, this method out-performed chemical extraction procedures in terms of the number of shards recovered and has resulted in the detection of new tephra layers with low shard concentrations. The implications of this study are highly significant because (i) the current database of distal tephra records and their corresponding geochemical signatures may require refinement and (ii) the record of distal tephras may be incomplete due to sample loss induced by corrosive laboratory procedures. It is therefore vital that less corrosive laboratory procedures are developed to make the detection and classification of distal glass tephra more secure.

Identification of vegetable oil botanical speciation in refined vegetable oil blends using an innovative combination of chromatographic and spectroscopic techniques

European Regulation 1169/2011 requires producers of foods that contain refined vegetable oils to label the oil types. A novel rapid and staged methodology has been developed for the first time to identify common oil species in oil blends. The qualitative method consists of a combination of a Fourier Transform Infrared (FTIR) spectroscopy to profile the oils and fatty acid chromatographic analysis to confirm the composition of the oils when required. Calibration models and specific classification criteria were developed and all data were fused into a simple decision-making system. The single lab validation of the method demonstrated the very good performance (96% correct classification, 100% specificity, 4% false positive rate). Only a small fraction of the samples needed to be confirmed with the majority of oils identified rapidly using only the spectroscopic procedure. The results demonstrate the huge potential of the methodology for a wide range of oil authenticity work.

Identification of Road Irregularities via Vehicle Accelerations

A periodic monitoring of the pavement condition facilitates a cost-effective distribution of the resources available for maintenance of the road infrastructure network. The task can be accurately carried out using profilometers, but such an approach is generally expensive. This paper presents a method to collect information on the road profile via accelerometers mounted in a fleet of non-specialist vehicles, such as police cars, that are in use for other purposes. It proposes an optimisation algorithm, based on Cross Entropy theory, to predict road irregularities. The Cross Entropy algorithm estimates the height of the road irregularities from vehicle accelerations at each point in time. To test the algorithm, the crossing of a half-car roll model is simulated over a range of road profiles to obtain accelerations of the vehicle sprung and unsprung masses. Then, the simulated vehicle accelerations are used as input in an iterative procedure that searches for the best solution to the inverse problem of finding road irregularities. In each iteration, a sample of road profiles is generated and an objective function defined as the sum of squares of differences between the ‘measured’ and predicted accelerations is minimized until convergence is reached. The reconstructed profile is classified according to ISO and IRI recommendations and compared to its original class. Results demonstrate that the approach is feasible and that a good estimate of the short-wavelength features of the road profile can be detected, despite the variability between the vehicles used to collect the data.

Footbridge system identification using wireless inertial measurement units for force and response measurements

With the main focus on safety, design of structures for vibration serviceability is often overlooked or mismanaged, resulting in some high profile structures failing publicly to perform adequately under human dynamic loading due to walking, running or jumping. A standard tool to inform better design, prove fitness for purpose before entering service and design retrofits is modal testing, a procedure that typically involves acceleration measurements using an array of wired sensors and force generation using a mechanical shaker. A critical but often overlooked aspect is using input (force) to output (response) relationships to enable estimation of modal mass, which is a key parameter directly controlling vibration levels in service.

This paper describes the use of wireless inertial measurement units (IMUs), designed for biomechanics motion capture applications, for the modal testing of a 109 m footbridge. IMUs were first used for an output-only vibration survey to identify mode frequencies, shapes and damping ratios, then for simultaneous measurement of body accelerations of a human subject jumping to excite specific vibrations modes and build up bridge deck accelerations at the jumping location. Using the mode shapes and the vertical acceleration data from a suitable body landmark scaled by body mass, thus providing jumping force data, it was possible to create frequency response functions and estimate modal masses.

The modal mass estimates for this bridge were checked against estimates obtained using an instrumented hammer and known mass distributions, showing consistency among the experimental estimates. Finally, the method was used in an applied research application on a short span footbridge where the benefits of logistical and operational simplicity afforded by the highly portable and easy to use IMUs proved extremely useful for an efficient evaluation of vibration serviceability, including estimation of modal masses.