FEM calibrated ARMAX model updating method for time domain damage identification

Due to environmental loads, mechanical damages, structural aging and human factors, civil infrastructure inevitably deteriorate during their service lives. Since their damage may claim human lives and cause significant economic losses, how to identify damages and assess structural conditions timely and accurately has drawn increasingly more attentions from structural engineering community worldwide. In this study, a fast and sensitive time domain damage identification method will be developed. To do this, a finite element model of a steel pipe laid on the soil is built and the structural responses are simulated under different damage scenarios. Based on the simulated data, an Auto Regressive Moving Average Exogenous (ARMAX) model is then built and calibrated. The calibrated ARMAX model is used to identify different damage scenarios through model updating process using clonal selection algorithm (CSA). The results demonstrate the application potential of the proposed method in identifying the pipeline conditions. To further verify its performance, laboratory tests of a steel pipe laid on the soil with and without soil support (free span damage) are carried out. The identification results of pipe-soil system show that the proposed method is capable of identifying damagein a complex structural system. Therefore, it can be applied to identifying pipeline conditions.

Cycling time to failure is better maintained by cold than contrast or thermoneutral lower-body water immersion in normothermia

Purpose
To examine the effects of four commonly used recovery treatments applied between two bouts of intense endurance cycling on the performance of the second bout in normothermia (~21 °C).

Methods
Nine trained men completed two submaximal exhaustive cycling bouts (Ex1 and Ex2: 5 min at ~50 % V˙O2 peak, followed by 5 min at ~60 % V˙O2 peak and then ~80 % V˙O2 peak to failure) separated by 30 min of (a) cold water immersion at 15 °C (C15), (b) contrast water therapy alternating 2.5 min at 8 °C and 2.5 min at 40 °C (CT), (c) thermoneutral water immersion at 34 °C (T34) and (d) cycling at ~40 % V˙O2 peak (AR).

Results
Exercise performance, cardiovascular and metabolic responses during Ex1 were similar among all trials. However, time to failure (~80 % V˙O2 peak bout) during Ex2 was significantly (P < 0.05) longer in C15 (18.0 ± 1.6) than in CT (14.5 ± 1.5), T34 (12.4 ± 1.4) and AR (10.6 ± 1.0); and it was also longer (P < 0.05) in CT than AR. Core temperature and heart rate were significantly (P < 0.05) lower during the initial ~15 min of Ex2 during C15 compared with all other conditions but they reached similar levels at the end of Ex2.

Conclusions
A 30 min period of C15 was more beneficial in maintaining intense submaximal cycling performance than CT, T34 and AR; and CT was also more beneficial than T34 and AR. These effects were not mediated by the effect of water immersion per se, but by the continuous (C15) or intermittent (CT) temperature stimulus (cold) applied throughout the recovery.

Biomedical time series analysis based on bag-of-words model

This research proposes a number of new methods for biomedical time series classification and clustering based on a novel Bag-of-Words (BoW) representation. It is anticipated that the objective and automatic biomedical time series clustering and classification technologies developed in this work will potentially benefit a wide range of applications, such as biomedical data management, archiving, retrieving, and disease diagnosis and prognosis in the future.

Managing ICT costs in e-commerce organizations with the time-driven activity-based costing model: a note

As online business activities continue to grow, it is important for organizations to understand the implications of being an e-commerce organization and the appropriateness of applying existing business techniques to e-commerce organizations. This paper examines issues in managing information and communication technologies (ICT) costs in e-commerce organizations and the potentials of the Time-Driven Activity-Based Costing (TDABC) model as a solution to these issues. The reliance on ICT in value creation, the unique natures of ICT costs and the need to maintain updated costing models present new challenges to e-commerce organizations in management of ICT costs. The TDABC model, as a variant of the Activity-Based Costing (ABC) model, inherits the strengths of activity-based paradigm and has unique features that can provide additional benefits to e-commerce organizations.

Application of the embedded element technique to predict interlaminar failure

In this study a modelling technique, namely, the embedded element method is assessed to evaluate its ability to predict Mode I interlaminar failure. The embedded element technique takes advantage of the embedded constraint in ABAQUS and allows the two constituents, fibre and matrix, to be meshed independently. Since the two constituents can be meshed independently a contiguous mesh is not required and the time taken to create an acceptable mesh is significantly reduced. The embedded element technique has been used to model fibre-reinforced composite structures, however, to date no studies have been conducted which combine the embedded element technique with an interlaminar damage model. The work described herein evaluates the ability of the embedded element technique to predict mode I interlaminar failure. DCB specimens were modelled using the embedded element method and a traditional 3D solid FE modelling approach with the predictions compared against experimental data. Both modelling approaches provided good agreement with experimental results. The good agreement demonstrates that the embedded element technique is capable of providing a response that is equivalent to a traditional 3D solid FE models and is particularly suited to modelling thick composite structures with complex geometry.

A unit root model for trending time-series energy variables

In this paper, we propose a GARCH-based unit root test that is flexible enough to account for; (a) trending variables, (b) two endogenous structural breaks, and (c) heteroskedastic data series. Our proposed model is applied to a range of time-series, trending, and heteroskedastic energy variables. Our two main findings are: first, the proposed trend-based GARCH unit root model outperforms a GARCH model without trend; and, second, allowing for a time trend and two endogenous structural breaks are important in practice, for doing so allows us to reject the unit root null hypothesis.

A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry

Failure mode and effect analysis (FMEA) is a popular safety and reliability analysis tool in examining potential failures of products, process, designs, or services, in a wide range of industries. While FMEA is a popular tool, the limitations of the traditional Risk Priority Number (RPN) model in FMEA have been highlighted in the literature. Even though many alternatives to the traditional RPN model have been proposed, there are not many investigations on the use of clustering techniques in FMEA. The main aim of this paper was to examine the use of a new Euclidean distance-based similarity measure and an incremental-learning clustering model, i.e., fuzzy adaptive resonance theory neural network, for similarity analysis and clustering of failure modes in FMEA; therefore, allowing the failure modes to be analyzed, visualized, and clustered. In this paper, the concept of a risk interval encompassing a group of failure modes is investigated. Besides that, a new approach to analyze risk ordering of different failure groups is introduced. These proposed methods are evaluated using a case study related to the edible bird nest industry in Sarawak, Malaysia. In short, the contributions of this paper are threefold: (1) a new Euclidean distance-based similarity measure, (2) a new risk interval measure for a group of failure modes, and (3) a new analysis of risk ordering of different failure groups. © 2014 The Natural Computing Applications Forum.