**2. Data acquisition**

Tokimatsu and Seed [1] developed a technique for predicting ground post liquefaction settlements based on volumetric strain, SPT N-value and cyclic stress ratio (CSR) relationships in the case of completely liquefied saturated sands transformed from an experimental relationship between relative sand density, volumetric strain, and maximum shear strain. Ishihara and Yoshimine [2] used an alternative approach to estimate ground settlements based on the safety factor, by means of the maximum shear strain which is an essential factor affecting the postliquefaction volumetric strain. The liquefaction-induced settlement during the earthquake can be identified if the safety factor and relative density are established. Furthermore, the simplified method was constructed only by a relation between relative density, the factor of safety against liquefaction (FS) and volumetric strain (*εv*) to quantify the settlement of a site where the safety factor of safety against liquefaction was obtained By combining earthquake intensity and SPT N-value with empirical equations to cause measurement error and lead to significant prediction

*Natural Hazards - Impacts, Adjustments and Resilience*

Analytical method used to assess liquefaction-induced settlements is based on the effective stress analysis of dynamic response which accounts for the generation and dissipation of excess pore water pressures. When used to evaluate post-

liquidation settlements in saturated sand deposits, the volume compressibility coefficient of the sand is required which is very difficult to determine for the liquefied sand layer [4]. Shamoto et al., [4] suggested a simplified approach for estimating liquefaction-induced settlements of saturated sand deposits, based on the experimental evidence that there is an almost linear relationship between the function of the void ratio and the logarithm of the maximum shear strain induced during cyclic

In numerical analysis, earthquake-induced liquefaction in the free-field may be interpreted as a 1D phenomenon occurring along a vertical soil column in which seismic-induced cyclic shear and compressive forces increase the pore pressure and hence cause a reduction in the transient soil strength and stiffness. Reconsolidation arises in the soil after liquefaction due to the dissipation of the excess pore pressure (Δu) by means of water flow, resulting in the vertical settlement of the ground

Park et al. [6] established a simple and sustainable method for predicting liquefaction-induced settlement using ANN. Tang et al. [3] found that the ANN and Bayesian Belief Networks (BBN) predictive outcomes are better than the Ishihara

REP tree techniques are used to develop two new models for evaluation of

liquefaction-induced settlement. Although these techniques have been successfully applied in many domains, the application in geotechnical earthquake engineering is

The remainder of this chapter is organized as follows: Section 2 briefly provides the description of data acquisition for liquefaction-induced settlement calculation.

Pohang earthquake (*Mw* = 5.4) that hit the Heunghae Basin around Pohang city had a liquefaction-induced damages—settlement and lateral displacement. In this study liquefaction-induced settlement is considered as a case of illustration. Several efforts have been made since the event to evaluate the post-earthquake damages [7–11]. Nevertheless, the liquefaction-induced settlement has received little attention. Settlement caused by liquefaction is commonly calculated by taking into account various factors and following several sophisticated analytical and numerical procedures. Nevertheless, in most cases it may not be possible to acquire such parameters in the field, as some of the required data may not be obtainable. The main purpose of this study is to evaluate liquefaction-induced settlement based on the database of field observations. To achieve this purpose, the random forest and

error [3].

loading.

surface [5].

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and Yoshimine simplified approach.

limited based on the literature surveys.

In this study, Park et al. [6] collected database from the Integrated DB Centre of National Geotechnical Information, Korea [12] and the UBCSAND constitutive effective stress model [13] was used to develop predictive models. SPT data were obtained for five different borehole sites near the epicenter of the earthquake at Pohang. The input parameters for the RF and REP Tree models are depth (m), unit weight (kN/m<sup>3</sup> ), corrected SPT blow count (*N*1(60)) and cyclic stress ratio (CSR) and the output is the observed settlement (mm). For details about the database, readers can refer to Park et al. [6]. The summary of the data base comprised 100 data points (20 data for each borehole) along with the corresponding settlement values is shown in **Table 1**.



**Borehole Depth (m) Unit Weight (kN/m<sup>3</sup>**

*DOI: http://dx.doi.org/10.5772/intechopen.94274*

**)** *N***1(60)** *CSR* **Settlement (mm)**

*Evaluation of Liquefaction-Induced Settlement Using Random Forest and REP Tree Models:…*

BH-A-5 1 20 11 0.32 0.5

*Note: Borehole (BH-A-5) data comprised of 20 data points is used as testing dataset in this study.*

*Summary of liquefaction-induced settlement database.*

**Table 1.**
