Khaled Ghaedi

By Khaled Ghaedi and Zainah Ibrahim

Among the countless natural disasters, earthquakes are capable to inflict vast devastation to a large number of buildings and constructions at the blink of an eye. Lack of knowledge and awareness on earthquake as well as its comeback is conspicuous and results in disaster; leading to bitter memories. Therefore, earthquake forecast has been a polemical study theme that has defied even the most intelligent of minds. In this chapter, an attempt was made to do an extensive overview in the area of the earthquake prediction as well as classifying them into the main strategies comprising short‐, immediate‐, and long‐term prediction. An example of each strategy was carried out by mentioning their corresponding approaches/algorithms, such as ΔCFS, CN, MSc, M8, ANN, FFBPANN, KNN, GRNN, RBF, and LMBP; depending on the importance of each strategy. Based on these, it was concluded that, after the Tohoku‐Oki earthquake with M9.0, the current orientation of the Headquarters for earthquake Research Promotion of MEXT in Japan declare that, their mission would be long‐term statistical forecast of seismicity. Even, it is claimed that they do not emphasize on short‐term forecasting. Besides, intermediate‐term estimations are not capable to be used for prevention of all damages and protect all human life, but they may be utilized to undertake certain affordable activities to decrease damage, losses, and modify postdisaster relief. And, despite the long‐term prediction is more concerned by researchers, there is no certain satisfactory level to content them. De facto, the made covenant of 1970 that investigators will be capable to forecast/predict ground excitations within a decade, still remains unmet.

Part of the book: Earthquakes

By Khaled Ghaedi, Farzad Hejazi, Meisam Gordan, Ahad Javanmardi, Hamed Khatibi and Ali Joharchi

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.

Part of the book: Computational Overview of Fluid Structure Interaction

By Meisam Gordan, Saeed-Reza Sabbagh-Yazdi, Khaled Ghaedi, David P. Thambiratnam and Zubaidah Ismail

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.

Part of the book: Applied Methods in Design and Construction of Bridges, Highways and Roads

By Khaled Ghaedi, Meisam Gordan, Ahad Javanmardi, Hamed Khatibi and Ramin Vaghei

Part of the book: Applied Methods in Design and Construction of Bridges, Highways and Roads