Preliminary Assessment of Liquefiable Area in Ermita ...

8th IEEE International Conference Humanoid, Nanotechnology, Information Technology Communication and Control, Environment and Management (HNICEM) The Institute of Electrical and Electronics Engineers Inc. (IEEE) – Philippine Section 9-12 December 2015 Water Front Hotel, Cebu, Philippines

Preliminary Assessment of Liquefiable Area in Ermita, Manila Using Genetic Algorithm Erica Elice S. UY Gokongwei College of Engineering De La Salle University Manila Taft Ave. Manila City, Philippines [email protected]

Elmer P. DADIOS MEM Department Head Gokongwei College of Engineering De La Salle University Manila Taft Ave. Manila City, Philippines [email protected] Jonathan R. DUNGCA Dean Gokongwei College of Engineering De La Salle University Manila Taft Ave. Manila City, Philippines [email protected]

Abstract— Liquefaction is a phenomenon that happens when there an excessive ground movement. The original soil structure is destroyed thus weakening its strength. When water is present within the ground, the development of porewater pressure is accelerated due to ground movements also causing the strength of the soil to decrease. Assessment of liquefaction is carried out to Ermita, Manila because of its proximity to Manila Bay. The presence of water makes the area vulnerable to liquefaction. Genetic algorithm is incorporated in the computation of the factor of safety against liquefaction. Parameters considered in genetic algorithm are depth of soil with respect to the ground surface, N60, fines content, unit weight and stress reduction coefficient. The factor of safety computed from genetic algorithm yield an error of 0.6%. Keywords— liquefactio;, genetic algorithm.

I. INTRODUCTION Liquefaction is a phenomenon where the soil loses its strength due to earthquake. The soil tends to flow resembling a liquid because of cyclic loading [1]. The presence of water beneath the ground surface can intensify liquefaction. Since, the porewater pressure can develop at a rapid pace due to ground shaking. Furthermore, effective shear stress within the soil reaches zero when shaking happens. Soil liquefaction is a type of soil failure that can cause damage to infrastructures such as buildings and roads. In the Philippines, an intensity 8 earthquake with 7.3 magnitude happened on August 2, 1968 at Casiguran, Aurora. At that time, it was considered as the most destructive earthquake the country had experienced for the last 20 years.

978-1-5090-0360-0/15/$31.00 ©2015 IEEE

One of the famous damages the earthquake left was the instant collapse of Ruby Tower. The Philippine Bar Association Building, Aloha Theater and Tuason Building are some of the infrastructures that experienced severe structural damage and are close to collapse[5]. Another earthquake that happened in the Philippines was on July 16, 1990 at Cabanatuan City. A 7.8 magnitude was recorded and the epicenter was located at the town of Rizal, Nueva Ecija. The earthquake was due to the strike-slip movements along the Digdig Fault and Philippine Fault. It made a 125 km long ground rupture starting from Dingalan, Aurora to Cuyapo, Nueva Ecija. Besides infrastructure damages, liquefaction was also experienced because of this earthquake. It happened at Dagupan City, Pangasinan and caused the buildings to sink by 1 m. [6]. It is evident that Philippines, specifically Metro Manila, is constantly faced with natural disasters especially earthquakes. The importance of knowing areas vulnerable to liquefaction is important. Since, this can help in the mitigation of possible damages a structure can experience due to liquefaction. One way to assess liquefaction vulnerability is to gather in situ test data from Standard Penetration Test (SPT) [9]. Seed and Idriss (1979) first proposed to use the results from that test and it was further improved by Seed et al. (1985) [8] and Youd et al (2001) [3]. The technique employs empirical equations that assess the index properties of the soil. Another way to assess liquefaction is by using data from a Cone Penetration Test (CPT). An interaction diagram for liquefaction assessment was developed by Robertson and Wride (1998) [3]. It was based on cyclic resistance ratio (CRR) and corrected CPT tip resistance. The diagram developed is suitable for a moment magnitude (Mw) of 7.5, sands with FC < 5% and median grain size, D50, of 0.25-2.0 mm. Although there are already existing methods, they only utilize limited number of parameters. In order to improve the assessment of liquefaction, a method that does not employs empirical relations or chart is needed. One method that can be


used is genetic algorithm. It is a search algorithm that is based on the mechanics of natural selection and natural genetics. The algorithm was developed by John Holland [2]. Genetic algorithm is a tool that has the capability to understand complicated relations among parameters. This lessens the errors from the uncertainties of the soil parameters. With this, it is the goal of this research to have a preliminary assessment of liquefiable area in Ermita, Manila with the use of genetic algorithm. Preliminary assessment is carried out to check the potential of the algorithm to perform assessment. The paper first discusses the area of study, Ermita, Manila.it is followed by the steps in computing the factor of safety used to assess liquefaction prone areas. The basic concept of genetic algorithm is also discussed together with the flowchart implemented. It is followed by the discussion and analysis of the data is presented.

= depth correction factor = (1atm/σ’vo)0.5

σ’vo = effective overburden pressure = hammer energy correction factor = borehole diameter correction factor = rod length correction factor = correction factor for samplers with or without liner It is a customary practice that the blow count is corrected. This is because of the errors brought about the testing equipment and the stresses the soil carries.

II. ERMITA, MANILA The desired area of investigation is Ermita, Manila. This district is chosen since the area is close to Manila Bay which is can intensify liquefaction. The area is also where most of the government offices in Manila City are situated such as Manila City Hall and Supreme Court of the Philippines [10]. SPT data were collected from 24 sites within the district as shown in Figure1. Each site has at least 3 borehole data set collected. The depth of the soil considered is 20 meters. At every meter, the blow count is recorded and the soil properties are classified. This means that each site would have approximately 60 data. A total of 1740 set of data is considered for the assessment. The location of the groundwater table is determined for each site. The deepest location of groundwater table, approximately 9.8 m, is at S3, Adriatico St., Ermita. The other locations’ depth of the ground water table ranges from 0 to 5 m with respect to the ground surface. From the borehole data, the type of soil in the area is mostly silty sand and silty fine sand. III.

LIQUEFACTION ASSESSMENT

The factor of safety against liquefaction is the basis for assessment. In order to estimate the liquefaction potential of an area the following steps are employed [3]:

Figure 1. Map of Ermita, Manila Step 3: Cyclic Shear Stress Ratio (CSR) is computed. (2) Where: σo and σ’o = total and effective vertical overburden stress, respectively.

Step 1: Location of the ground water table. The location of ground water table is first determined since this can affect the computation for the vertical effective stress. Step 2: Correct SPT blow count using the equation: (1) Where: = corrected normalized SPT blow count. = measured SPT blow count.

amax = peak horizontal acceleration (PGA) in g. (0.4g is the value used for this research) rd = stress reduction coefficient Cyclic shear stress ratio is the ratio of the equivalent cyclic shear stress developed during earthquake loading (τav) to the eﬀective normal stress during consolidation on any plane [4]. Step 4: Fines content (FC) correction for the corrected


normalized SPT blow count. (3) Where: α and β = coefficients for fines content. α = 0 for FC < 5 % α = exp[1.76 – (190/FC2)] for 5 % < FC < 35 % α = 5.0 for FC > 35 % β = 1.0 for FC < 5 % β = [0.99 + (FC1.5/1000)] for 5 % < FC < 35 % β = 1.2 for FC > 35 % Step 5: Cyclic Resistance Ratio (CRR)7.5

(4)

Cyclic resistance ration is considered as the maximum CSR value that a soil layer can resist before liquefaction [4].

offspring. The point of crossover is randomly selected. Mutation is the next step. It is performed based on a chosen rate and it is dependent on the variables of the population. Once new offspring are created, they are evaluated based on the fitness function. The offspring that satisfies the fitness function is included in the final population [4]. A program is developed in Labview that performs the basic steps in genetic algorithm. The flowchart of the algorithm is shown in Figure2. The fitness function for the algorithm is Equation 6. It is maximized so that the data for soil layers that are vulnerable to liquefaction is obtained. From the borehole data gathered, the factor of safety against liquefaction was first computed before the population is fed to the program. The set of data that satisfied the fitness function is transferred to the crossover stage. Crossover is performed at a random position. The final position for crossover is shown in Figure 2. The parameters considered for crossover are the depth of soil with respect to the ground surface, N60, fines content, unit weight stress reduction coefficient. It is then followed by mutation. The mutation rate is based on the probability of the parameters to exceed its maximum values. After mutation, a new population can be generated. Factor of safety against liquefaction is computed using the new population. Cross over can be repeated once the value of factor of safety is not met. P1

D

N60

FC

rd

Unit Weight

Crossover Point P2

D

N60

FC

rd

Unit Weight

Step 7: Magnitude Scaling Factor (MSF) (5)

Figure 2. Crossover of Parent 1 (P1) to Parent 2 (P2). V. RESULTS OF THE STUDY

Where: Mw = earthquake moment magnitude Step 8: Factor of Safety against Liquefaction (6) A factor of safety greater that 1 is considered to be as a liquefiable soil. (Youd) IV. GENETIC ALGORITHM Genetic algorithm is a mathematical technique that is patterned with respect to how natural selection happens similar to biology. The algorithm starts with the population. It is based on the principles of natural selection and survival of the fittest. The individual who fits best in the environment will have a higher probability of survival. On the other hand, the individuals with poor performance are not considered for the next generation. The population must be generated randomly to have promising results. To characterize the best individual, a fitness function or objective function is needed. Once the population is evaluated, crossover is performed to create new

A. Genetic Algorithm From the borehole data collected, a total of 1740 parents were tested for the fitness function. Two hundred forty one parents had a factor of safety greater than 1. Crossover and mutation is performed. To check the performance of the new population, it is once again tested with the fitness function with 100 generations. Each generation has 241 parents and 241 factor of safety. The mean of the factor of safety is the basis for comparison. Comparing the factor of safety of each parent to the original data is not conservative. The original parent is different from the parent that is created from genetic algorithm. The mean of the original parents is 1.64. It is compared to the results of genetic algorithm and an error of 0.6% is observed. The number of occurrence is observed from the 100 generations as seen in Figure 4. Sixty three out of 100 generation yielded 1.64 as the mean of the factor of safety. This shows the promising potential of genetic algorithm in assessing liquefaction. B. Liquefaction For the preliminary assessment an earthquake moment magnitude of 5.0 is used since it is the smallest magnitude


that the country had experience [11]. Peak ground acceleration of 0.4 is used to simulate a severe perceived ground shaking and moderate to heavy potential damage. From the computed factor of safety, 241 out of 1740 or 13.85% has a value larger than 1 and is at risk to liquefaction. To check the vulnerable area, the factor of safety is mapped and checked per site. As seen in Table1, S20 has the largest, 85, amount of soil layers that are liquefiable. It is followed by S8, 37, S2, 15, S22, 14, and S12 and S16, 11.

Figure 4. Number of Occurrences for the Factor of Safety. TABLE I.

LIST OF LIQUEFIABLE SOIL LAYERS FOR EACH SITE Site

Figure 3. Flowchart of Genetic Algorithm.

Liquefiable Soil Layers

S1

6

S2

15

S3

5

S4

2

S5

0

S6

0

S7

6

S8

37

S9

1

S10

7

S11

5

S12

11

S13

4

S14

0

S15

2

S16

11

S17

0

S18

8

S19

9

S20

85

S21

7

S22

14

S23

2

S24

1

S25

1

S26

2

S3 and S20 are close with each other as seen in Figure 1. The soils in S20 are mostly clayey sand and high plasticity clay while for S3 it is fine sand and organic clayey silt. The location of groundwater table for S22 is 0.54-2 m while S8 is 1.0-9.8 m. For the fines content, S22 has values greater than 35% while for S3 it is all less than 5%. This is the reason why even though

8th IEEE International Conference Humanoid, Nanotechnology, Information Technology Communication and Control, Environment and Management (HNICEM) The Institute of Electrical and Electronics Engineers Inc. (IEEE) – Philippine Section 9-12 December 2015 Water Front Hotel, Cebu, Philippines potential is REFERENCES

both sites are close to each other, their liquefaction different. S3 is the location of the deepest groundwater table. Its potential for liquefaction is small. VI.

CONCLUSION

Preliminary liquefaction assessment is carried out for Ermita, Manila using genetic algorithm. An error of 0.6% is observed when compared to the original data. Through genetic algorithm new population can be generated which can avoid the uncertainties brought about the soil’s properties. With this preliminary study, it is found that S22 or at Adriatico Street Corner Pedro Gil Street. Further studies can be conducted on the basis of this preliminary study. Especially on the area of mitigating liquefaction. ACKNOWLEDGMENT I would like to thank Dr. Jonathan R. Dungca and Renz Anderson Chua for sharing the knowledge and data about liquefaction. I am also grateful to Dr. Elmer P. Dadios in introducing and teaching me genetic algorithm.

[1]

B. M. Das, Principles of Geotechnical Engineering, Cengage Learning, 2010. [2] D. E. Goldberg, Genetic Algorithms in Seach,Optimization, and Machine Learning. Alabama, University of Alabama, 1989. [3] T.L. Youd and I.M. Idriss, “A Treatise on Electricity and Ma Liquefaction Resistance Of Soils:Summary Report from the 1996 NCEER And 1998 NCEER/NSF Workshops on Evaluation of Liquefaction Resistance of Soils,” Journal Of Geotechnical And Geoenvironmental Engineering, 2001, pp.291-313. [4] M. Afzalirad, I. Shooshpasha and M. Bagheripour, “A genetic algorithm approach for assessing soil liquefaction potential based on reliability method in Magnetism,” vol. 121, J. Earth Syst. Sci., 2012, pp. 45-62. [5] Philippine Institute of Volcanology and Seismology-1(PHIVOLCS) Retrieved from http://www.phivolcs.dost.gov.ph/index.php?option=com_content&view =article&id=2262:casiguran-earthquake-02-august-1968&catid=43R. Nicole, “Title of paper with only first word capitalized,” J. Name Stand. Abbrev., in press. [6] Philippine Institute of Volcanology and Seismology-2(PHIVOLCS2) Retrieved from http://earthquake.phivolcs.dost.gov.ph/1990LuzonEQ_Monograph/pp04 3/pp043.html [7] D. E. Goldberg, Genetic Algorithms in Seach,Optimization, and Machine Learning. Alabama, University of Alabama, 1989. [8] H.B. Seed, “Soil liquefaction and cyclic mobility evaluation for level ground during earthquakes.” Vol. 105, Journal of Geotechnical Engineering, 1979, pp. 201-255. [9] H. B. Seed, K. Tokimatsu, L. F. Harder and R. M. Chung, “The influence of SPT procedures in soil liquefaction resistance evaluations.” Vol. 111, Journal of Geotechnical Engineering, 1985 ,pp.1425–1445. [10] Wikipedia Retrieved from https://en.wikipedia.org/wiki/Ermita [11] J. R. Dungca, Liquefaction Potential Map of Manila, De La Salle University, 1997.