**Author details**

settlement. Additionally, the results of training and testing were shown in **Figures 3** and **4**, showing the projected settlements are plotted with the actual data. One can see that settlements were predicted more accurately by the RF model than by the REP Tree model. While the REP Tree model few settlements cases are

This paper explores the potential of RF and REP Tree models for predicting liquefaction-induced settlement using field data. The models were trained and tested based on the Pohang city liquefaction-induced settlement database. Both models assess liquefaction-induced settlement with substantial contributing factors such as depth, unit weight, corrected SPT blow count and cyclic stress ratio. The performance of the models presented is measured using statistical parameters such as the correlation coefficient (*r*), MAE, and RMSE. The RF model indicates a better performance with respect to the training and testing datasets. From this analysis it can be inferred that the RF model works well in predicting liquefaction-induced settlement as opposed to the REP Tree model. Since, artificial intelligence-based approaches are data-dependent and their output can vary depending on the dataset, the quality and number of training datasets and the size of the experiments. Finally, it is obvious that the proposed models are open to develop and accumulation of more data will provide much better evaluation of liquefaction-induced settlements.

The work presented in this paper was part of the research sponsored by the Key

Program of National Natural Science Foundation of China under Grant No. 51639002 and National Key Research and Development Plan of China under Grant

relatively under predicted as compared to the RF model.

*Natural Hazards - Impacts, Adjustments and Resilience*

**6. Conclusions**

**Acknowledgements**

No. 2018YFC1505300-5.3.

The authors declare no conflict of interest.

**Conflict of interest**

**268**

Mahmood Ahmad1,2, Xiaowei Tang1 \* and Feezan Ahmad<sup>1</sup>

1 State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, China

2 Department of Civil Engineering, University of Engineering and Technology Peshawar (Bannu Campus), Bannu, Pakistan

\*Address all correspondence to: tangxw@dlut.edu.cn

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
