Over the past few years, swarm intelligence based optimization techniques such as ant colony optimization and particle swarm optimization have received considerable attention from engineering researchers and practitioners. These algorithms have been used in the solution of various engineering problems. Recently, a relatively new swarm based optimization algorithm called the Artificial Bee Colony (ABC) algorithm has begun to attract interest from researchers to solve optimization problems. The aim of this study is to present an optimization algorithm based on the ABC algorithm for the discrete optimum design of truss structures. The ABC algorithm is a meta-heuristic optimization technique that mimics the process of food foraging of honeybees. Originally the ABC algorithm was developed for continuous function optimization problems. This paper describes the modifications made to the ABC algorithm in order to solve discrete optimization problems and to improve the algorithm's performance. In order to demonstrate the effectiveness of the modified algorithm, four structural problems with up to 582 truss members and 29 design variables were solved and the results were compared with those obtained using other well-known meta-heuristic search techniques. The results demonstrate that the ABC algorithm is very effective and robust for the discrete optimization designs of truss structural problems.

Discrete optimum design of truss structures using artificial bee colony algorithm

This paper reviews the research of monitoring technologies for bolted structural connections. The acoustoelastic effect based method, the piezoelectric active sensing method, and the piezoelectric impedance method are the three commonly used to monitor bolted connections. The basic principle and the applications of these three methods are discussed in detail in this paper. In addition, this paper presents a comparison of these methods and discusses their suitability for in situ or real-time bolt connection monitoring.

Review of Bolted Connection Monitoring

Foreword Chapter 1 Introduction to SHM (Daniel L Balageas) Chapter 2 Vibration-based techniques for SHM (Claus-Peter Fritzen) Chapter 3 Fiber-optics sensors (Alfredo Guemes and Jose M Menendez) Chapter 4 SHM with piezoelectric sensors (Philippe Guy and Thomas Monnier) Chapter 5 SHM using electrical resistance (Michelle Salvia and Jean-Christophe Abry) Chapter 6 Low frequency electromagnetic techniques (Michel B Lemistre) Chapter 7 Capacitive methods for SHM in civil engineering (Xavier Derobert and Jean Iaquinta) Short Bibliographies of the Contributors Index

/pdf/structural-health-monitoring-37wv1pwxci.pdf

Structural health monitoring

Over past few decades, families of algorithms based on the intelligent group behaviors of social creatures like ants, birds, fishes, and bacteria have been extensively studied and applied for computer-aided optimization. Recently there has been a surge of interest in developing algorithms for search, optimization, and communication by simulating different aspects of the social life of a very well-known creature: the honey bee. Several articles reporting the success of the heuristics based on swarming, mating, and foraging behaviors of the honey bees are being published on a regular basis. In this paper we provide a brief but comprehensive survey of the entire horizon of research so far undertaken on the algorithms inspired by the honey bees. Starting with the biological perspectives and motivations, we outline the major bees-inspired algorithms, their prospects in the respective problem domains and their similarities and dissimilarities with the other swarm intelligence algorithms. We also provide an account of the engineering applications of these algorithms. Finally we identify some open research issues and promising application areas for the bees-inspired computing techniques.

https://dr.ntu.edu.sg/bitstream/10356/83447/1/Computing%20with%20the%20collective%20intelligence%20of%20honey%20bees%20%e2%80%93%20A%20survey.pdf

Computing with the collective intelligence of honey bees – A survey

Monitoring of multi-bolt connection looseness using entropy-based active sensing and genetic algorithm-based least square support vector machine

On the application of bees algorithm to the problem of crack detection of beam-type structures

Early detection of bolt loosening is a major concern in the oil and gas industry. In this study, a vibration-based health monitoring strategy has been developed for detecting the loosening of bolts in a pipeline’s bolted flange joint. Both numerical and experimental studies are conducted to verify the integrity of our implementation as well as of an enhancement developed along with it. Several damage scenarios are simulated by the loosening of the bolts through varying the applied torque on each bolt. An electric impact hammer is used to vibrate (excite) the system in a consistent manner. The induced vibration signals are collected via piezoceramic sensors bonded onto the pipe and flange. These signals are transferred remotely by a wireless data acquisition module and then processed with a code developed in-house in the MATLAB environment. After normalization and filtering of the signals, the empirical mode decomposition is applied to establish an effective energy-based damage index. The assessment of the...

http://journals.sagepub.com/doi/pdf/10.1177/1475921713479641

Improvement of a vibration-based damage detection approach for health monitoring of bolted flange joints in pipelines:

In this paper the application of bees algorithm (BA) in the finite element (FE) model updating of structures is investigated. BA is an optimization algorithm inspired by the natural foraging behavior of honeybees to find food sources. The weighted sum of the squared error between the measured and computed modal parameters is used as the objective function. To demonstrate the effectiveness of the proposed method, BA is applied on a piping system to update several physical parameters of its FE model. To this end, the modal parameters of the numerical model are compared with the experimental ones obtained through modal testing. Moreover, to verify the performance of BA, it is compared with the genetic algorithm, the particle swarm optimization and the inverse eigensensitivity method. Comparison of the results indicates that BA is a simple and robust approach that could be effectively applied to the FE model updating problems.

Finite element model updating using bees algorithm

This paper presents the application of a novel vibration-based technique for detecting fatigue cracks in structures. The method utilizes the empirical mode decomposition method (EMD) to establish an effective energy-based damage index. To investigate the feasibility of the method, fatigue cracks of different sizes were introduced in an aluminum beam subjected to a cyclic load under a three-point bending configuration. The vibration signals corresponding to the healthy and the damaged states of the beam were acquired via piezoceramic sensors. The signals were then processed by the proposed methodology to obtain the damage indices. In addition, for the sake of comparison, the frequency and damping analysis were performed on the test specimen. The results of this study concluded with two major observations. Firstly, the method was highly successful in not only predicting the presence of the fatigue crack, but also in quantifying its progression. Secondly, the proposed energy-based damage index was proved to be superior to the frequency-based methods in terms of sensitivity to the damage detection and quantification. As a result, this technique could be regarded as an efficient non-destructive tool, since it is simple, cost-effective and does not rely on analytical modeling of structures. In addition, the capability of the finite element method (FEM) in mimicking the experiments, and hence for consideration as an effective tool for conducting future parametric studies, was also investigated.

https://iopscience.iop.org/article/10.1088/0964-1726/20/11/115017/pdf

Application of a robust vibration-based non-destructive method for detection of fatigue cracks in structures

This study presents numerical simulations and experimental verification of a vibration-based damage detection technique. Health monitoring of a submerged pipe's girth-weld against an advancing notch is attempted. Piezoelectric transducers are bonded on the pipe for sensing or actuation purposes. Vibration of the pipe is excited by two means: (i) an impulsive force; (ii) using one of the piezoelectric transducers as an actuator to propagate chirp waves into the pipe. The methodology adopts the empirical mode decomposition (EMD), which processes vibration data to establish energy-based damage indices. The results obtained from both the numerical and experimental studies confirm the integrity of the approach in identifying the existence, and progression of the advancing notch. The study also discusses and compares the performance of the two vibration excitation means in damage detection.

https://www.mdpi.com/1424-8220/14/9/17174/pdf

Pejman Razi

Papers

On the application of bees algorithm to the problem of crack detection of beam-type structures

Improvement of a vibration-based damage detection approach for health monitoring of bolted flange joints in pipelines:

Finite element model updating using bees algorithm

Application of a robust vibration-based non-destructive method for detection of fatigue cracks in structures

A vibration-based strategy for health monitoring of offshore pipelines' girth-welds.