**6. Conclusion**

A bridge is one of the most symbolic, important, as well as expressive infrastructures worldwide for social and economic activities of mankind where it serves as the crucial link in the transport network. Therefore, condition assessment and damage detection of this asset is frequently required to guarantee the safe functioning of the infrastructure. To do so, SHM systems have been applied to make satisfactory decisions on structural maintenance, repair, and rehabilitation. However, conventional SHM cannot be used for structural continuous monitoring, real-time and online assessment to solve real-world problems. Therefore, integration of SHM with soft computing techniques has been successfully applied for optimized monitoring of bridges in recent years. This is due to the fact that soft computing is an umbrella of computational techniques that tolerates uncertainty imprecision, partial truth, and ambiguity. Hence, this chapter introduced 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 were required to maintain the health condition of infrastructures and for smooth functioning of cities.

## **Acknowledgements**

The authors wish to acknowledge the University of Malaya and K.N.TOOSI University of Technology for providing the resources and supporting this research. The authors would also like to express their sincere thanks to the Structural Health Monitoring Research Group (StrucHMRSGroup), which was led by Professor Emeritus Hashim Abdul Razak. (Program Number: IIRG007A-2019).

*Introduction to Monitoring of Bridge Infrastructure Using Soft Computing Techniques DOI: http://dx.doi.org/10.5772/intechopen.104905*
