Meisam Gordan

Bio: Meisam Gordan is an academic researcher from University of Malaya. The author has contributed to research in topics: Structural health monitoring & Artificial neural network. The author has an hindex of 7, co-authored 24 publications receiving 167 citations. Previous affiliations of Meisam Gordan include K.N.Toosi University of Technology.

Recent developments in damage identification of structures using data mining

Abstract: CIVIL STRUCTURES ARE USUALLY PRONE TO DAMAGE DURING THEIR SERVICE LIFE AND IT LEADS THEM TO LOSS THEIR SERVICEABILITY AND SAFETY. THUS, DAM-AGE ASSESSMENT CAN GUARANTEE THE INTEGRITY OF STRUCTURES. AS A RESULT, A STRUCTURAL DAMAGE DETECTION APPROACH INCLUDING TWO MAIN COMPONENTS, A SET OF ACCELEROMETERS TO RECORD THE RESPONSE DATA AND A DATA MINING (DM) PROCEDURE, IS WIDELY USED TO EXTRACT THE INFORMATION ON THE STRUCTURAL HEALTH CONDITION. IN THE LAST DECADES, DM HAS PROVIDED NUMEROUS SOLUTIONS TO STRUCTURAL HEALTH MONITORING (SHM) PROBLEMS AS AN ALL-INCLUSIVE TECHNIQUE DUE TO ITS POWERFUL COMPUTATIONAL ABILITY. THIS PAPER PRESENTS THE FIRST ATTEMPT TO ILLUSTRATE THE DATA MINING TECHNIQUES (DMTS) APPLICATIONS IN SHM THROUGH AN INTENSIVE REVIEW OF THOSE ARTICLES DEALING WITH THE USE OF DMTS AIMED FOR CLASSIFICATION-, PREDICTION- AND OPTIMIZATION-BASED DATA MINING METHODS. ACCORDING TO THIS CATEGORIZATION, APPLICATIONS OF DMTS WITH RESPECT TO SHM RESEARCH AREA ARE CLASSIFIED AND IT IS CONCLUDED THAT, APPLICATIONS OF DMTS IN THE SHM DOMAIN HAVE INCREASINGLY BEEN IMPLEMENTED, IN THE LAST DECADE AND THE MOST POPULAR TECHNIQUES IN THE AREA WERE ARTIFICIAL NEURAL NETWORK (ANN), PRINCIPAL COMPONENT ANALYSIS (PCA) AND GENETIC ALGORITHM (GA), RESPECTIVELY.

Damage detection in steel-concrete composite bridge using vibration characteristics and artificial neural network

Abstract: This paper develops and applies a procedure for detecting damage in a composite slab-on-girder bridge structure comprising of a reinforced concrete slab and three steel I beams, using vibration cha...

IoT and Big Data Applications in Smart Cities: Recent Advances, Challenges, and Critical Issues

Abstract: The notion of smart cities has remained under evolution as its global implementations are challenged by numerous technological, economic, and governmental obstacles. Moreover, the synergy of the Internet of Things (IoT) and big data technologies could result in promising horizons in terms of smart city development which has not been explored yet. Thus, the current research aims to address the essence of smart cities. To this end, first, the concept of smart cities is briefly overviewed; then, their properties and specifications as well as generic architecture, compositions, and real-world implementations are addressed. Furthermore, possible challenges and opportunities in the field of smart cities are described. Numerous issues and challenges such as analytics and using big data in smart cities introduced in this study offers an enhancement in developing applications of the above-mentioned technologies. Hence, this study paves the way for future research on the issues and challenges of big data applications in smart cities.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

A New Structural Health Monitoring Strategy Based on PZT Sensors and Convolutional Neural Network.

Abstract: Preliminaries convolutional neural network (CNN) applications have recently emerged in structural health monitoring (SHM) systems focusing mostly on vibration analysis However, the SHM literature shows clearly that there is a lack of application regarding the combination of PZT-(lead zirconate titanate) based method and CNN Likewise, applications using CNN along with the electromechanical impedance (EMI) technique applied to SHM systems are rare To encourage this combination, an innovative SHM solution through the combination of the EMI-PZT and CNN is presented here To accomplish this, the EMI signature is split into several parts followed by computing the Euclidean distances among them to form a RGB (red, green and blue) frame As a result, we introduce a dataset formed from the EMI-PZT signals of 720 frames, encompassing a total of four types of structural conditions for each PZT In a case study, the CNN-based method was experimentally evaluated using three PZTs glued onto an aluminum plate The results reveal an effective pattern classification; yielding a 100% hit rate which outperforms other SHM approaches Furthermore, the method needs only a small dataset for training the CNN, providing several advantages for industrial applications