A Computational Intelligence (CI) approach is one of the main trending and potent data dealing out and processing instruments to unravel and resolve difficult and hard reliability crisis and it takes an important position in intelligent reliability analysis and management of data. Nevertheless, just few little broad reviews have recapitulated the current attempts of Computational Intelligence (CI) in reliability assessment in power systems. There are many methods in reliability assessment with the aim to prolong the life cycles of a system, to maximize profit and predict the life cycle of assets or systems within an organization especially in electric power distribution systems. Sustaining an uninterrupted electrical energy supply is a pointer of affluence and nationwide growth. The general background of reliability assessment in power system distribution using computational intelligence, some computational intelligence techniques, reliability engineering, literature reviews, theoretical or conceptual frameworks, methods of reliability assessment and conclusions was discussed. The anticipated and proposed technique has the aptitude to significantly reduce the needed period for reliability investigation in distribution networks because the distribution network needs an algorithm that can evaluate, assess, measure and update the reliability indices and system performance within a short time. It can also manage outages data on assets and on the entire system for quick and rapid decisions making as well as can prevent catastrophic failures. Those listed above would be taken care of if the proposed method is utilized. This overview or review may be deemed as valuable assistance for anybody doing research.

The study presents the finite element (FE) model update of the existing simple-spans steelconcrete composite bridge structure using a particle swarm optimisation (PSO) and genetic algorithm (GA) approaches. The Wireless Structural Testing System (STS-WiFi) of Bridge Diagnostic, Inc. from the USA, implemented various types of sensors including: LVDT displacement sensors, intelligent strain transducers, and accelerometers that the static and dynamic historical behaviors of the bridge structure have been recorded in the field testing. One part of all field data sets has been used to calibrate the cross-sectional stiffness properties of steel girders and material of steel beams and concrete deck in the structural members including 16 master and slave variables, and that the PSO and GA optimisation methods in the MATLAB software have been developed with the new innovative tools to interface with the analytical results of the FE model in the ANSYS APDL software automatically. The vibration analysis from the dynamic responses of the structure have been conducted to extract four natural frequencies from experimental data that have been compared with the numerical natural frequencies in the FE model of the bridge through the minimum objective function of percent error to be less than 10%. In order to identify the experimental mode shapes of the structure more accurately and reliably, the discrete-time state-space model using the subspace method (N4SID) and fast Fourier transform (FFT) in MATLAB software have been applied to determine the experimental natural frequencies in which were compared with the computed natural frequencies. The main goal of the innovative approach is to determine the representative FE model of the actual bridge in which it is applied to various truck load
configurations according to bridge design codes and standards. The improved methods in this document have been successfully applied to the Vietnamese steel-concrete composite bridge in which the load rating factors (RF) of the AASHTO design standards have been calculated to predict load limits, so the final updated FE model of the existing bridge is well rated with all RF values greater than 1.0. The presented approaches show great performance and the potential to implement them in industrial conditions.