Simply put, artificial intelligence (AI) involves using computers to classify, analyze, and draw predictions from data sets, using a set of rules called algorithms. Stanford AI Lab. introduced an evolved hybrid genetic algorithm and neural network (GNN) model [92]. This paper summarizes recently developed methods and theories in the developing direction for applications of artificial intelligence in civil engineering, including evolutionary computation, neural networks, fuzzy systems, expert system, reasoning, classification, and learning, as well as others like chaos theory, cuckoo search, firefly algorithm, knowledge-based engineering, and simulated annealing. The algorithm mimics the behavior of electrons flowing, through electric circuit branches with the least electric resistance. White Paper on Artificial Intelligence A European approach to excellence and trust Artificial Intelligence is developing fast. Dekkers and Aarts [116] presented a stochastic approach which was based on the simulated annealing algorithm. Flood, “Towards the next generation of artificial neural networks for civil engineering,”, S. Sharma and A. Das, “Backcalculation of pavement layer moduli from falling weight deflectometer data using an artificial neural network,”, R. Bendaña, A. Del Caño, and M. P. De La Cruz, “Contractor selection: Fuzzy-control approach,”, A. Bilgil and H. Altun, “Investigation of flow resistance in smooth open channels using artificial neural networks,”, Z. Q. Gu and S. O. Gu, “Diagonal recurrent neural networks for MDOF structural vibration control,”, S. Laflamme and J. J. Connor, “Application of self-tuning Gaussian networks for control of civil structures equipped with magnetorheological dampers,” in, F. Xiao and S. N. Amirkhanian, “Effects of binders on resilient modulus of rubberized mixtures containing RAP using artificial neural network approach,”, B. This work presents in detail how to apply PSO method in finding the optimal PID gains of gantry crane system in the fashion of min-max optimization. In the commercialization of artificial intelligence technology, there are many successful examples abroad, for enterprise and socially brought considerable benefit. Mahjoobi et al. Prerequisites A. M. Madkour, K. P. Dahal, and H. Yu, “Comparative performance of intelligent algorithms for system identification and control,”, M. Y. Cheng, H. S. Peng, Y. W. Wu, and T. L. Chen, “Estimate at completion for construction projects using evolutionary support vector machine inference model,”, J. Sobhani and A. As for neural network structure and algorithm, its improvement research has been in progress. [53] summarizes the structural optimization applications in civil engineering design and development of the situation based on the characteristics of the bridge structure design process was proposed for the bridge project to the genetic algorithm, neural network, expert system technology as the basis for combining automated design and optimization of structural design of the system. [18] presented a new empirical model to estimate the base shear of plane steel structures subjected to earthquake load using a hybrid method integrating genetic programming (GP) and simulated annealing (SA), called GP/SA. 2012, Article ID 145974, 22 pages, 2012. https://doi.org/10.1155/2012/145974, 1Faculty of Civil Engineering & Architecture, Zhejiang University of Technology, Hangzhou 310023, China, 2College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou 310023, China. presented an investigation into the comparative performance of intelligent system identification and control algorithms within the framework of an active vibration control (AVC) system [61]. Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think. [23] presented a new approach, based on evolutionary polynomial regression (EPR), for analysis of stability of soil and rock slopes. Bianchini and Bandini [71] propose a neuro-fuzzy model to predict the performance of flexible pavements using the parameters routinely collected by agencies to characterize the condition of an existing pavement. (2)To deepen the understanding of the problems of uncertainty and to seek appropriate reasoning mechanism is the primary task. The neural network will be very broad used in the civil engineering field application prospect. The book offers a global spread of authors, from USA, Canada, UK, Japan, France, Russia, China and Singapore, who are all world recognized experts in their separate areas. Senouci and Al-Derham [14] presented a genetic-algorithm-based multiobjective optimization model for the scheduling of linear construction projects. To overcome disadvantage, prove mathematical model accurate, and identify parameters full, Wang et al. [10] described two artificial intelligence techniques for prediction of maximum dry density (MDD) and unconfined compressive strength (UCS) of cement stabilized soil. Gupta et al. Results indicated that error statistics of soft computing models were similar, while ANFIS models were marginally more accurate than FIS and ANNs models. The attractive control strategy derived there-from was applied to seismically excited bridges using LRB isolation. [41] presents a fuzzy-logic-based system for selecting contractors. Eliseo et al. This paper focus on the History of A.I. Artificial Intelligence History. In this technique, a combination of the genetic algorithm and the least-squares method was used to find feasible structures and the appropriate constants for those structures.
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