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.

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.

Currently, visual inspections for damage identification of structures are broadly used. However, they have two main drawbacks; time limitation and qualified manpower accessibility. Therefore, more precise and quicker technique is required to monitor the condition of structures. To aid the aim, a data mining based damage identification approach can be utilized to solve these drawbacks. In this study, to predict the damage severity of single-point damage scenarios of I-beam structures a data mining based damage identification framework and a hybrid algorithm combining Artificial Neural Network (ANN) and Imperial Competitive Algorithm (ICA), called ICA-ANN method, is proposed. ICA is employed to determine the initial weights of ANN. The efficiency coefficient and mean square error (MSE) are used to evaluate the performance of the ICA-ANN model. Moreover, the proposed model is compared with a pre-developed ANN approach in order to verify the efficiency of the proposed methodology. Based on the obtained results, it is concluded that the ICA-ANN indicates a better performance in detection of damage severity over the ANN method used only.