An efficient two-stage approach for structural damage detection using meta-heuristic algorithms and group method of data handling surrogate model

In this study, the performance of an efficient two-stage methodology which is applied in a damage detection system using a surrogate model of the structure has been investigated. In the first stage, in order to locate the damage accurately, the performance of the modal strain energy based index for using different numbers of natural mode shapes has been evaluated using the confusion matrix. In the second stage, to estimate the damage extent, the sensitivity of most used modal properties due to damage, such as natural frequency and flexibility matrix is compared with the mean normalized modal strain energy (MNMSE) of suspected damaged elements. Moreover, a modal property change vector is evaluated using the group method of data handling (GMDH) network as a surrogate model during damage extent estimation by optimization algorithm; in this part of methodology, the performance of the three popular optimization algorithms including particle swarm optimization (PSO), bat algorithm (BA), and colliding bodies optimization (CBO) is examined and in this regard, root mean square deviation (RMSD) based on the modal property change vector has been proposed as an objective function. Furthermore, the effect of noise in the measurement of structural responses by the sensors has also been studied. Finally, in order to achieve the most generalized neural network as a surrogate model, GMDH performance is compared with a properly trained cascade feed-forward neural network (CFNN) with log-sigmoid hidden layer transfer function. The results indicate that the accuracy of damage extent estimation is acceptable in the case of integration of PSO and MNMSE. Moreover, the GMDH model is also more efficient and mimics the behavior of the structure slightly better than CFNN model.     

A second order sliding mode strategy for fault detection and fault-tolerant-control of a MEMS optical switch

Optical switches are widely used in telecommunication industry due to their many desirable characteristics. In this paper, robust fault detection and fault-tolerant-control (FTC) system for an uncertain nonlinear MEMS optical switch are presented. The design strategy is based on the second order sliding mode approach. A robust second order nonlinear sliding mode observer capable of filtering unwanted high frequencies due to unmodeled dynamics is used to generate quantities called the the residuals. The residuals are then used for the purpose of fault detection and alarm generation. Once an alarm is registered, a fault tolerant control strategy is employed. Two different fault-tolerant control strategies for the unhealthy system are considered. The first strategy is based on conventional sliding mode, while the second is based on a second order sliding mode theory. Robustness and convergence of the proposed schemes are proved using the second method of Lyapunov and the super-twisting algorithm. A comparative study is then performed to demonstrate the superior capability of second order sliding mode control strategy in fault accommodation. Finally, the effectiveness of the proposed strategy for detection of faults, and subsequent control of the MEMS optical switch is illustrated through simulation studies.     

Position control of an electro-pneumatic system based on PWM technique and FLC

In this paper, modeling and PWM based control of an electro-pneumatic system, including the four 2–2 valves and a double acting cylinder are studied. Dynamic nonlinear behavior of the system, containing fast switching solenoid valves and a pneumatic cylinder, as well as electrical, magnetic, mechanical, and fluid subsystems are modeled. A DC–DC power converter is employed to improve solenoid valve performance and suppress system delay. Among different position control methods, a proportional integrator derivative (PID) controller and fuzzy logic controller (FLC) are evaluated. An experimental setup, using an AVR microcontroller is implemented. Simulation and experimental results verify the effectiveness of the proposed control strategies.     

Sonochemical Synthesis and Characterization of the New Plate-Shaped Lead(II)–Iodo Coordination Polymer: A Precursor to Produce a Pure Phase Nano-Sized Lead(II) Oxide

Preparation of plate-shaped nanostructures of a new 1D polymeric lead(II) complex containing the Pb₂-(μ-I)₂ motif, [Pb(neo)I₂] _n ,(1) where neo is the abbreviation of neocuproine, using a sonochemical method is described. The new coordination polymer is characterized by scanning electron microscopy, X-ray diffraction (XRD), elemental analysis and infrared spectroscopy. The single crystalline material is obtained using a heat gradient applied to a solution of the reagents. Single-crystal XRD analysis indicates that a coordination number of six for Pb^II ions, (i.e., PbN₂I₄) with an asymmetrical coordination sphere and “stereo-chemically active” electron lone pairs. They also show that the chains interact with each other through π–π stacking interactions creating a 3D framework. PbO nanoparticles are obtained by thermolysis of 1 at 180?°C with oleic acid as a surfactant. Scanning electron microscopy confirms formation of PbO particles with a size of ~25?nm.     

A review of statistical methods for the evaluation of aquatic habitat suitability for instream flow assessment

Habitat models serve three main purposes: First, to predict species occurrences on the basis of abiotic and biotic variables, second to improve the understanding of species‐habitat relationships and third, to quantify habitat requirements. The use of statistical models to predict the likely occurrence or distribution of species based on relevant variables is becoming an increasingly important tool in conservation planning and wildlife management. This article aims to provide an overview of the current status of development and application of statistical methodologies for analysing the species‐environment association, with a clear emphasis on aquatic habitat. It describes the main types of univariate and multivariate techniques available for analysis of species‐environment association, and specifically focuses on the assessment of the strengths and weaknesses of the available statistical methods to estimate habitat suitability. A second objective of this article is to propose new approaches using existing statistical methods. A wide array of habitat statistical models has been developed to analyse habitat‐species relationship. Generally, physical habitat is dependent on more than one variable (e.g. depth, velocity, substrate, cover) and several suitability indices must be combined to define a composite index. Multivariate approaches are more appropriate for the analysis of aquatic habitat as they inherently consider the interrelation and correlation structure of the environmental variables. Ordinary multiple linear regression and logistic regression are popular methods often used for modelling of species and their relationships with environment. Ridge regression and Principal component regression are particularly useful when the independent variables are highly correlated. More recent regression modelling paradigms like generalized linear models (GLMs) present advantages in dealing with non‐normal environmental variables. Generalized additive models (GAMs) and artificial neural networks are better suited for analysis of non‐linear relationships between species distribution and environmental variables. The fuzzy logic approach presents advantages in dealing with uncertainties that often exist in habitat modelling. Appropriate methods for analysis of multi‐species data are also presented. Finally, the few existing comparative studies for predictive modelling are reviewed, and advantages and disadvantages of different methods are discussed. Copyright © 2006 John Wiley & Sons, Ltd.     

Pile-soil-structure interaction in pushover analysis of jacket offshore platforms using fiber elements

In this paper, pushover analysis of fixed jacket offshore platforms with the application of “Fiber Elements” which are capable of modeling post-buckling behavior of braces has been conducted and case study on two functional jacket offshore platforms in the Persian Gulf region has been performed. Two-dimensional models of the mentioned platforms are simulated using “DRAIN-3DX” software and the wave force is considered as the lateral load pattern on the structures.In this paper, pushover analysis of jacket offshore platforms is performed by developing numerical models that incorporate different foundation conditions for considering pile-soil-structure interaction. Increasing the displacement of the platforms and recording the relative load factors by incorporating the displacement control method is the basis for performing the analysis. In order to consider the importance of pile-soil interaction, six individual cases are examined which include pile-soil interaction analysis with actual soil in-situ characteristics. Pile head fixed below mud line elevation, pile head hinged below mud line elevation, application of linear pile stubs, application of non-linear pile-stubs & finally a combination of linear and non-linear pile-soil characteristics.In the analyses performed herein, the condition in which pile-soil interaction is considered is set to be the base case. The results from incorporating the non-linear pile stub have the most compatibility with the base case. Application of Linear pile stubs and the combination of linear and non-linear pile has much deviation with the base case. Ultimate strength of the platform in the non-linear pile stub case was very close to the base case.     

Incremental dynamic analysis of steel frames equipped with NiTi shape memory alloy braces

This paper determines the seismic performance of four‐storey concentrically braced frames equipped with either steel buckling‐restrained braces or buckling‐restrained superelastic shape memory alloy (SMA) braces through incremental dynamic analysis. The incremental dynamic analysis technique is used to examine the behaviour of four‐storey braced frames with four different bracing configurations (including diagonal, split‐X, chevron‐V and inverted‐V) under 20 different ground motion records. The study reveals a satisfactory performance at the design intensity level for both types of braced frames. The results show that the SMA braces lead to a uniform distribution of inelastic response over the height of the buildings, as well as mitigating seismic response in terms of maximum inter‐storey drift and residual roof displacement. By comparing the responses of SMA and buckling‐restrained braced frames under higher intensities of earthquake loading, it is found that the SMA braces can be more beneficial especially under severe ground motion excitations. Copyright © 2014 John Wiley & Sons, Ltd.     

Behavior of moment‐resisting tall steel structures exposed to a vertically traveling post‐earthquake fire

An investigation is performed on a 10‐story moment‐resisting steel structure designed to the Life Safety level of performance of the Federal Emergency Management Agency (FEMA) 356 code by exposing it to post‐earthquake fire (PEF). The fire curve is accounted for using the natural fire method, and the fire is subjected to the floors vertically in three different scenarios: (a) fire initiated from the first floor, (b) fire initiated from the fourth floor and (c) fire initiated from the seventh floor. A delay of 5 minutes and 25 minutes are considered for spreading the fire between the floors. To make a comparison between the results, a concurrent fire is also considered for the fire analysis. The results indicate that the PEF resistance of the frame exposed to the concurrent fire and the 5 minutes delay is much lower than that with a delay of 25 minutes. The results also show that subjecting the frame to a delayed fire of 25 minutes leads to the collapse of the frame during cooling phase, whereas in the other scenarios, the frame collapses during heating phase. As a result, more considerations need to be implemented in the codes on top of that for the PEF itself and that is the appropriate rate of spread of fire between floors. Copyright © 2013 John Wiley & Sons, Ltd.     

DDoS attack detection in IEEE 802.16 based networks

Achieving high data rate transmission, WiMAX has acquired noticeable attention by communication industry. One of the vulnerabilities of the WiMAX network which leads to DDoS attack is sending a high volume of ranging request messages to base station (BS) in the initial network entry process. In the initial network entry process, BS and subscriber station (SS) exchange management messages. Since some of these messages are not authenticated, malicious SSs can attack the network by exploiting this vulnerability which may increase the traffic load of the BS and prevent it from serving the SSs. So, detecting such attacks is one of the most important issues in such networks. In this research, an artificial neural network (ANN) based approach is proposed in order to detect DDoS attacks in IEEE 802.16 networks. Although lots of studies have been devoted to the detection of DDoS attack, some of them focus just on some statistical features of the traffic and some other focus on packets’ headers. The proposed approach exploits both qualitative and quantitative methods. It detects the attack by feeding some features of the network traffic under attack to an appropriate ANN structure. To evaluate the method, first a typical attacked network is implemented in OPNet simulator, and then by using the proposed system, the efficiency of the method is evaluated. The results show that by choosing suitable time series we can classify 93 % of normal traffic and 91 % of attack traffic.     

AQM controller design for TCP networks based on a new control strategy

When the network suffers from congestion, the core or edge routers signal the incidence of congestion through the active queue management (AQM) to the sources. The time-varying nature of the network dynamics and the complex process of retuning the current AQM algorithms for different operating points necessitate the development of a new AQM algorithm. Since the non-minimum phase characteristics of the network dynamics restrict direct application of the proportional-integral-derivative (PID) controller, we propose a compensated PID controller based on a new control strategy addressing the phase-lag and restrictions caused by the delay. Based on the unstable internal dynamics caused by the non-minimum phase characteristics, a dynamic compensator is designed and a PID controller is then allowed to meet the desired performance objectives by specifying appropriate dynamics for the tracking error. Since the controller gains are obtained directly from the dynamic model, the designed controller does not require to be tuned over the system operating envelop. Moreover, simulation results using ns2 show improvements over previous works especially when the range of variation of delay and model parameters are drastic. Simplicity, low computational cost, self-tuning structure and yet considerable improvement in performance are exclusive features of the proposed AQM for the edge or core routers.