Data Mining Technology for Structural Control Systems: Concept, Development, and Comparison

Structural control systems are classified into four categories, that is, passive, active, semi-active, and hybrid systems. These systems must be designed in the best way to control harmonic motions imposed to structures. Therefore, a precise powerful computer-based technology is required to increase the damping characteristics of structures. In this direction, data mining has provided numerous solutions to structural damped system problems as an all-inclusive technology due to its computational ability. This chapter provides a broad, yet in-depth, overview in data mining including knowledge view (i.e., concept, functions, and techniques) as well as application view in damped systems, shock absorbers, and harmonic oscillators. To aid the aim, various data mining techniques are classified in three groups, that is, classification-, prediction-, and optimization-based data mining methods, in order to present the development of this technology. According to this categorization, the applications of statistical, machine learning, and artificial intelligence techniques with respect to vibration control system research area are compared. Then, some related examples are detailed in order to indicate the efficiency of data mining algorithms. Last but not least, capabilities and limitations of the most applicable data mining-based methods in structural control systems are presented. To the best of our knowledge, the current research is the first attempt to illustrate the data mining applications in this domain

Optimization-Based Evolutionary Data Mining Techniques for Structural Health Monitoring

In recent years, data mining technology has been employed to solve various Structural Health Monitoring (SHM) problems as a comprehensive strategy because of its computational capability. Optimization is one the most important functions in Data mining. In an engineering optimization problem, it is not easy to find an exact solution. In this regard, evolutionary techniques have been applied as a part of procedure of achieving the exact solution. Therefore, various metaheuristic algorithms have been developed to solve a variety of engineering optimization problems in SHM. This study presents the most applicable as well as effective evolutionary techniques used in structural damage identification. To this end, a brief overview of metaheuristic techniques is discussed in this paper. Then the most applicable optimization-based algorithms in structural damage identification are presented, i.e. Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA) and Ant Colony Optimization (ACO). Some related examples are also detailed in order to indicate the efficiency of these algorithms

Seismic response of a base isolated cable-stayed bridge under near-fault ground motion excitations / Ahad Javanmardi … [et al.].

Nowadays, development of cable-stayed bridges is increasing around the world. The mitigation of seismic forces to these bridges are obligatory to prevent damages or failure of its structural members. Herein, this paper aimed to determine the near-fault ground motion effect on an existing cablestayed bridge equipped with lead-rubber bearing. In this context, Shipshaw cable-stayed bridge is selected as the case study. The selected bridge has a span of 183.2 m composite deck and 43 m height of steel tower. 2D finite element models of the non-isolated and base isolated bridges are modelled by using SAP2000. Three different near-fault ground motions which are Tabas 1978, Cape Mendocino 1992 and Kobe 1995 were subjected to the 2D FEM models in order to determine the seismic behaviour of the bridge. The near-fault ground motions were applied to the bridge in the longitudinal direction. Nonlinear dynamic analysis was performed to determine the dynamic responses of the bridge. Comparison of dynamic response of nonisolated and base isolated bridge under three different near-fault ground motions were conducted. The results obtained from numerical analyses of the bridge showed that the isolation system lengthened the period of bridge and minimised deck isplacement, base shear and base moment of the bridge. It is concluded that the isolation system significantly reduced the destructive effects of near-fault ground motions on the bridge

Seismic Response Characteristics of RCC Dams Considering Fluid-Structure Interaction of Dam-Reservoir System

In analysis of different types of dams, i.e. arch, gravity, rockfill and Roller Compacted Concrete (RCC) dams, the effect of hydrodynamic water pressure as an effective factor must seriously be taken into consideration. In present study, the hydrodynamic effect is precisely deliberated in RCC dams and compared to hydrostatic pressure effect. For this purpose, Kinta RCC dam in Malaysia is selected and 2D finite element (FE) model of the dam is performed. The Lagrangian approach is used to solve the dam-reservoir interaction, fluid–structure interaction (FSI), and in order to evaluate the crack pattern, Concrete Damaged Plasticity (CDP) model is implemented. Comparisons show that hydrodynamic pressure significantly changes the dam behaviour under seismic excitations. Moreover, the hydrodynamic effect modifies the deformation shape of the dam during the ground motions, however, it increases the magnitudes of the developed stresses causing more extensive tension crack damages mostly in the heel and upstream zones of the dam

Introduction to Monitoring of Bridge Infrastructure Using Soft Computing Techniques

More than a billion structures exist on our planet comprising a million bridges. A number of these infrastructures are near to or have already exceeded their design life and maintaining their health condition is an engineering optimization problem. Besides, these assets are damage-prone during their service life. This is due to the fact that different external loads induced by the environmental effects, overloading, blast loads, wind excitations, floods, earthquakes, and other natural disasters can disturb the serviceability and integrity of these structures. To overcome such bottlenecks, structural health monitoring (SHM) systems have been used to guarantee the safe functioning of structures to make satisfactory decisions on structural maintenance, repair, and rehabilitation. However, conventional SHM approaches such as virtual inspections cannot be used for structural continuous monitoring, real-time and online assessment. Therefore, soft computing techniques can be significantly used to mitigate the aforesaid concerns by handling the qualitative analysis of the complex real world behavior. This chapter aims to introduce the optimized SHM-based soft computing techniques of bridge structures through artificial intelligence and machine learning algorithms in order to illustrate the performance of advanced bridge monitoring approaches, which are required to maintain the health condition of infrastructures as well as to protect human lives

Experimental investigation of passive tuned mass damper and fluid viscous damper on a slender two dimensional steel frame

Vibration is a serious concern for tall buildings added to a natural disaster such as earthquake, wind storms, sea waves and hurricanes. The risk of occurrence of structural damage can be decreased by using a controlled vibration system to increase the damping characteristics of a structure. Damping is defined as the ability of the structure to dissipate a portion of the energy released during a dynamic loading event. The aims of this study are (1) to investigate a 4-storey 2D steel frame retrofit with tuned mass damper to reduce its vibration as well as compare the results with response displacement of the structure using viscous damper. In this project, the focus is limited to present an experimental model with semi-rigid connections and to show its validity by comparing the experimental results (achieved from shaking table test) with the analytical results obtained from theoretical model (SAP2000 software), (2) to demonstrate the performance of such a damper when fitted to a structure by analysis and tests the models and (3) comparison the dynamic responses of the structure in three verify of: a) using passive tuned mass damper, b) using viscous damper and c) using the combination of these two damping devices. Therefore, a series of shaking table tests of the 4-storey 2D steel frame with and without passive tuned mass damper (PTMD) and viscous damper (VD) was carried out to evaluate the performance of the buildings. The results of the experimental tests illustrate that damping devices decrease the structural responses of slender frame on shaking table. In addition, effectiveness of passive tuned mass damper is greater than viscous damper

A Damage Detection Approach in the Era of Industry 4.0 Using the Relationship between Circular Economy, Data Mining, and Artificial Intelligence

Over the last decades, the emergence of new technologies has inspired a paradigm shift for the fourth industrial revolution. For example, circular economy, data mining, and artificial intelligence (AI), which are multidisciplinary topics, have recently attracted industrial and academic interests. Sustainable structural health monitoring (SHM) also concerns the continuous structural assessment of civil, mechanical, aerospace, and industrial structures to upgrade conventional SHM systems. A damage detection approach inspired by the principles of data mining with the adoption of circular-economic thinking is proposed in this study. In addition, vibration characteristics of a composite bridge deck structure are employed as inputs of AI algorithms. Likewise, an artificial neural network (ANN) integrated with a genetic algorithm (GA) was also developed for detecting the damage. GA was applied to define the initial weights of the neural network. To aid the aim, a range of damage scenarios was generated and the achieved outcomes confirm the feasibility of the developed method in the fault diagnosis procedure. Several data mining techniques were also employed to compare the performance of the developed model. It is concluded that the ANN integrated with GA presents a relatively fitting capacity in the detection of damage severity

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, damage 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

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 characteristics and Artificial Neural Network (ANN). ANN is used in conjunction with modal strain energy-based damage index for locating and quantifying damage in the steel beams which are the main load bearing elements of the bridge, while the relative modal flexibility change is used to locate and quantify damage in the bridge deck. Research is carried out using dynamic computer simulations supported by experimental testing. The design and construction of the experimental composite bridge model is based on a 1:10 ratio of a typical multiple girder composite bridge, which is commonly used as a highway bridge. The procedure is applied across a range of damage scenarios and the results confirm its feasibility to detect and quantify damage in composite concrete slab on steel girder bridges