Order 3728490_Application of Artificial Neural Network (ANN) in Civil Engineering.edited (Essay Sample)

Student Name Prof Course/Unit Submission Date Application of Artificial Neural Network (ANN) in Civil Engineering Artificial Neural Networks (ANN) is a system that functions like a fully developed human brain since it stores and retrieves data and uses it in solving complex information and gaining knowledge with experience. ANN entails a symbolic method of intelligence calculation and data processing, and both are achieved through soft computing (Jahirul et al. 632). The network is characterized by its ability to solve difficult tasks, adapt to changing environments, modifying the internal structure to achieve the desired outcome, generating own rules based on learned examples, and being fast and inexpensive while at it. It finds a broad application in civil engineering and has attracted attention from researchers due to its applicability to real-life situations, as discussed herein. ANN in Road Safety. The creation of networks with the ability to identify the end of the row of cars during traffic congestion and displaying a message to the roadway could help in reducing the number of vehicles that follow that route. Consequently, this could save time for the users and the drivers (Singh et al. 32). Once the networks are fully operational, they will identify road accidents and hence improve the transport system. They will also analyze the whole infrastructure and road network based on nondestructive measurements taken using a deflectometer. Additionally, it will help predict the crack propagation on the various concrete pavements, hence predicting the service life of the paths (Ghasedi, Sarfjoo, and Bargegol 11). Current vehicles have been inbuilt with auto-pilot mode that has an ANN system. This will help reduce road accidents and improve the road transport system's effectiveness and efficiency. Construction Technology and Management. Some of the common challenges in the construction sector include a lack of a model for determining labor productivity, a method for forecasting revenues of disputes during construction, and a lack of effective and reliable techniques for cost optimization (Albahri et al. 21). However, these issues are being solved by connecting ANN with the methods used for scheduling, such as PERT and CPM. The approach has also helped in striking a balance between cost, time, and resources. Structural Engineering. There is strong interest in the application of ANN in structural engineering. Most of the studies have focused on the behavior of concrete using ANN. The studies have been used to predict failures, propagate cracks, and develop shear strength depending on the dimension variations of the beam, among other functions (Pan et al. 24). The collaboration of ANN has enabled composite structures to be monitored using damage assessment techniques. Other forms of ANN application include identifying and localization of possible imperfections in curtails wall systems and monitoring structural health by assessing the changes resulting from damage. Additionally, ANN has been effectively applied in modeling the nonlinear features of steel structures, estimating how different structures respond to seismic, and studying various methods of vibration suppression (Wu and Wang 89). The network system enables the knowledge gained to be applied in the design and maintenance of systems. Soil Studies. Challenges that are experienced in geotechnical engineering include uncertainty in the design values of indices. In some design cases, great experience and knowledge are required to solve such types of challenges. Nowadays, solving the issues has become easier due to the integration of ANN. ANN can learn from experience and hence has modeling superiority compared to conventional methods (Bangert 69). This will eliminate the need to make assumptions on the fundamental rules that govern the issue being considered, as ANN will provide accurate analysis and solutions. ANN can now be applied to investigate various complex engineering issues. Such problems include loading conditions and the potential failures, soil liquefaction, foundation studies, as well as soil-pile interactions (Lan et al. 31). The findings can enhance decision-making in such scenarios as to when developing the foundation of a house; it is important to understand the loading conditions and possible failures, soil-pile interactions, and the liquefaction of soil and how this could affect the house. Water Resource Engineering. Neural networks are applied in hydraulics in various forms. Such include the mathematical formulation and modeling for the analysis of water distribution systems, identification of water loss through cracks in an underground piping system, and retrofitting and rehabilitating water networks (Feng and Niu 80). There are also ongoing studies on how ANN can be applied in expanding sewage network systems, enhancing water quality, and increasing the effectiveness of sewage treatments. Therefore, this shows that the studies conducted on Artificial Neural Networks over the last two decades show how it is applied in civil engineering. The reports also show an increa...