STUDY ON THE RELATIONSHIP OF COMPRESSION INDEX FROM WATER CONTENT, ATTERBERG LIMITS AND FIELD DENSITY FOR KUTTANAD CLAY

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1 Sepeember 2016 IJIRT Volume 3 Issue 4 ISSN: STUDY ON THE RELATIONSHIP OF COMPRESSION INDEX FROM WATER CONTENT, ATTERBERG LIMITS AND FIELD DENSITY FOR KUTTANAD CLAY Kiran Jacob 1 and Professor Hari G 2 1 Pos-graduae suden, Saingis College of Engineering, Koayam, Kerala 2 Professor, Deparmen of Civil Engineering, Saingis College of Engineering, Koayam, Kerala Absrac Kuanad is a unique agriculural land in Kerala, he soil in he area where is problemaic in naure. Major porion of Kuanad lies below mean sea level and during raining season he area is submerged under waer for more han one monh in every year. Due o increase in populaion and developmen of he area has need consrucion aciviies o underaken in Kuanad region. Mos of he foundaion failures have occurred in his area is due o very low undrained shear srengh and consolidaion selemen of he clay soil. Laboraory es for obaining hese values are expensive and ime consuming process, while he soil parameer like naural moisure conen, Aerberg limis and field densi can be esimae faser and cheaper. Therefore he relaionship of compression index from waer conen, Aerberg limis and field densi are useful for reduce he esing number and coss. Linear regression is a saisical ool for making he relaion, which is he invesigaion of relaionship beween dependen variable and independen variable. Index Terms Aerberg limis, compression index, field densi, waer conen. I. INTRODUCTION Foundaion of any srucure consruced on he compressible soil layer leads o is consolidaion selemen. The rae of consolidaion selemen is relaed o he compression index or coefficien of volume change m v. For design consideraion, knowledge of he rae a which he compression of he soil akes place is essenial. The soil behavior is an imporan elemen which always affeced in he civil engineering. The soil properies such as plasici characerisics, compressibili or srengh parameer always affec he design in he consrucion. The suiabili of a soil for a paricular siuaion should be deermined based on is engineering properies and no on field observaion or proper similari of oher soils. The deerminaion of consolidaion properies from consolidaion ess is expensive, cumbersome and ime consuming process, since i akes a more han 3 weeks o complee a pical consolidaion es. A lo of knowledge, experience and skill are required on he par of he Engineer o inerpreing he resuls for field applicaion. Because of hese reason, several sudies have been made in he previous researchers o predic he compression index from index properies, which are relaively simple o deermine and ake lesser ime o obain he resul from he laboraory. Index properies of he soil such as Aerberg limis, waer conen and field densi are basic properies of he soils; herefore i is possible o predic he compression index from index properies of he soil. The aim of his sudy is o esablish relevan relaionships beween compression index, Aerberg limis and waer conen of Kuanad clay. Kuanad is a unique agriculural land in Kerala, he soil in he area where is problemaic in naure. Major porion of Kuanad lies below mean sea level and during raining season he area is submerged under waer for more han one monh in every year. Due o increase in populaion and developmen of he area has need consrucion aciviies o underaken in Kuanad region. Mos of he foundaion failures have occurred in his area is due o very low undrained shear srengh and consolidaion selemen of he clay soil. Laboraory es for obaining hese values are expensive and ime consuming process, while he soil parameer like naural moisure conen, Aerberg limis and field densi can be esimae faser and IJIRT INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY 33

2 Sepeember 2016 IJIRT Volume 3 Issue 4 ISSN: cheaper. Therefore he relaionship of compression index from waer conen, Aerberg limis and field densi are useful for reduce he esing number and coss. Linear regression is a saisical ool for making he relaion, which is he invesigaion of relaionship beween dependen variable and independen variable. II. LITERATURE REVIEW I should be noed ha regression equaion beween soil parameer and he Aerberg limis had been developed more han 50 years ago. The capaci of soil o ake loadings is differen, as i depending on he pe of soil. Generally, soils wih smaller size (no compleely consolidaed) have a relaively smaller capaci han he coarser grained soils. Hence soils wih small size herefore have greaer selemen in comparison wih coarser grained soil. The compression index value varies for differen pe of soils. Table 1below show he value of compression index of several kinds of soils: Table I: Value of Differen Soils Kind of soil compression index Dense sand Loose sand Firm clay Siff clay Medium sof clay Organic soil In lieraure several regression equaion have been made whereby compressibili characerisic like compression index () have been evaluaed using Aerberg limis, naural waer conen(wn), iniial void raio (e o ), specific gravi and several oher properies of soil. Skempon (1944) performed consolidaion es on several number of clay soils colleced from differen locaions and gave he following regression equaion for he compression index for a remolded soil sample: = (wl- 10%) (1) Nishida (1956) develop heoreically linear regression equaion for all kind of undisurbed clay soils as showed in equaion below: Nishida model = 4 (e o - 5) (2) Terzaghi and Peck (1967) have derive equaion for ordinary clay of medium o low sensiivi, he value of compression index corresponding o in-siu condiions is roughly equal o 1.3 imes values of Skempon model,which is : Terzaghi and Peck model = (LL- 10%) (3) Similarly Azzouz (1976) regression equaion as: Azzouz model = (e o ) (4) Rendon- Herrero (1980) conducs a sudy around of 94 samples of America s clay and develops he following equaion: Rendon- Herrero model = (e o ) (5) Koppula (1981) and Wroh e al. (1978) made a regression equaion using PL for remoulding clays: Koppula Wroh model = PI (6) Serajjudin (1987) develop a linear regression equaion for 130 alluvial clay and sil in Bangladesh by using waer conen (Wn): Serajjudin model = (Wn ) (7) Similarly Sridharan and Nagaraj (2000) have given: =0.014(PI+3.6) (8) Vikas Kumar Jain and Mahabir Dixi (2015) conduced a sudy on 44 laboraory ess resuls, on he soil samples colleced from various river valley projecs = PI (9) These regression equaions were developed by sudy conduced on soil from he counry of origin of he researchers, in mos of he cases and if his equaion used in oher counries, hese may be chance of eiher over esimae or underesimae he compression index. III. METHODOLOGY For an appreciable conclusion o be esablish, 60 clay soil samples is colleced from Differen locaions in Kuanad region, Alappuzha, India. Samples were colleced from a deph of 1.5 m beneah he ground level. A. Sample Preparaion Soil samples from 60 locaions are prepared for he laboraory ess (laboraory es resuls shown in able II) according o he Indian Sandard Code for pracice, SP: 36(par 1) 1987 Laboraory esing of soil for engineering purpose. B. Soil Tesing The seleced soil samples were subjeced o he following Field and Laboraory ess. 1. Field ess a) Deerminaion of mass densi by core cuer IJIRT INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY 34

3 Sepeember 2016 IJIRT Volume 3 Issue 4 ISSN: mehod. 2. Laboraory ess b) We sieve analysis c) Hydromeer analysis d) Specific gravi es e) Consolidaion es f) Aerberg limi es 3. Daa Collecion Daa are colleced from he field and laboraory soil esing which conduced as saed above. A oal number of 60 soil daa from he ess are used for his sudy. In which hir samples are from lower kuanad area and he oher hir samples are from upper kuanad area. For collecing he sample, he ground surface is cleared from debris and he clay is colleced from a deph of 1.5 meer from he soil surface. The samples colleced were undisurbed. The diameer and lengh of he sampler are 3.8 and 20 cm respecively. Soil from boh end of he sampler is removed and sealed wih paraffin wax. Sampler is wrapped using high quali polyhene cover. Adequae daa is imporan for carrying ou he required analysis in order o achieve he objecives of he presen sudy. 4. Daa Analysis And Formulaion Of Proposed Regression Equaion In order o develop he relaion of compression index from waer conen, field densi and aerberg limis from he soil samples colleced, i is necessary o conduc deailed calculaion and analysis of he soil daa colleced from he es resuls. The daa were analyzed by means of linear regression analysis o form he proposed correlaion. Table II: Summary of Laboraory Tes Resuls Sam ple No. Wae r Cone n Liqu id Plas ic Field Densi (kn/ m 3 ) Speci fic Gravi Soil Classifica ion 1 CH CH CH CH-MH 1 CH-MH CH Sam ple No. Wae r Cone n Liqu id Plas ic Field Densi (kn/ m 3 ) Speci fic Gravi CH 5 CH Soil Classifica ion 5 CH 2 CH-MH IJIRT INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY 35

4 Sepeember 2016 IJIRT Volume 3 Issue 4 ISSN: Sam ple No. Wae r Cone n Liqu id Plas ic Field Densi (kn/ m 3 ) Speci fic Gravi MH -MH -MH 2 CH -MH 2 CH-MH -MH 1 CH -MH 4 CH Soil Classifica ion 2 CH -MH 2 CH-MH -MH IV. RESULTS AND DISCUSSIONS A. compression index From Simple Linear Regression 1. Relaion beween Compression Index () and waer conen (w) Linear regression is done beween compression index and waer conen and he resul is represened by equaion (1) and graphical represenaion is given in Figure 1. = 0.007w (1) R 2 =0.85. = 0.007w R² = W Fig. 1. Compression Index Vs Waer Conen Graph 2. Relaion Beween Compression Index () And Liquid (LL) The linear rend line in Figure 2 represens he resul of regression. The relaion formulaed is given by he equaion (2). = 0.005LL (2) R 2 = Regression Plo Regression Plo = 0.005LL R² = LL Fig. 2. Compression Index Vs Liquid Graph 3. Relaion Beween Compression Index () And Plasic (PL) IJIRT INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY 36

5 Sepeember 2016 IJIRT Volume 3 Issue 4 ISSN: A relaion beween compression index and plasic limi is esablished and is given equaion (3) and represened graphically in Figure 3. = 0.013PL (3) R 2 =0.87. = 0.013PL R² = PL Fig. 3. Compression Index Vs Plasic Graph 4. Relaion Beween Compression Index () And Field Densi (ρ) The resul of linear regression beween compression index and field densi is represened by equaion (4) and he graphical represenaion is given in Figure 4. = -73ρ (4) R 2 =0.86. Regression Plo Regression Plo = -73ρ R² = ρ (kn/m 3 ) Fig. 4. Compression Index Vs Field Densi Graph B. Compression Index From Muliple Variable Regression Wih Two Independen Variable 1. Shear Srengh ( ) From Waer Conen (w) And Liquid (LL) Muliple regression equaion obained from compression index, waer conen and liquid limi and he resul is represened by equaion (5). = w+0.002LL (5) R 2 = Compression Index () From Liquid (LL) And Plasic (PL) A resul of muliple regression of compression index from liquid limi and plasic limi is represened by equaion (6). = 0.003LL+0.006PL (6) R 2 =0. 3. Compression Index () From Waer Conen(w) And Field Densi(ρ) An equaion connecing compression index, waer conen and field densi was obained from he muliple value regression analysis. And i is given as equaion (7). = w-0.124ρ (7) R 2 =1. 4. Compression Index () From Liquid (LL) And Field Densi(ρ) The resul of muliple regression equaion obained from compression index, waer conen and liquid limi and he resul is represened by equaion (8). = LL-40ρ (8) R 2 =0. C. Compression Index From Muliple Variable Regression Wih Three Independen Variable 1. Shear Srengh( ) From Waer Conen(w), Liquid (LL) And Plasic (PL) The resul of muliple variable regression equaion of compression index from waer conen, liquid limi and plasic limi is represened by equaion (9). = 0.004w+0.001LL+0.004PL (9) R 2 =3 2. Compression Index () From Waer Conen(w), Liquid (LL) And Field Densi(ρ) An equaion connecing compression index, waer conen, liquid limi and field densi was obained from he muliple value regression analysis. And i is given as equaion (10). = w+0.001LL-0.102ρ (10) R 2 =2. 3. Compression Index () From Liquid (LL), Plasic (PL) And Field Densi(ρ) A relaion of compression index from liquid limi, plasic limi and field densi is esablished and is given equaion (11). = LL+0.005PL-0.093ρ (11) R 2 =3. D. Compression Index From Muliple Variable Regression Wih Four Independen Variable 1. Compression Index () From Waer Conen(w), Liquid (LL), Liquid (PL) And Field IJIRT INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY 37

6 Sepeember 2016 IJIRT Volume 3 Issue 4 ISSN: Densi(ρ) An equaion connecing compression index, waer conen, liquid limi, plasic limi and field densi was obained from he muliple value regression analysis. And i is given as equaion (12). = w LL PL ρ (12) R 2 =4. V. CONCLUSION In his sudy he relaionship of compression index from waer conen, field densi and Aerberg limis from he 60 soil samples colleced from Kuanadu has been invesigaed. For his purpose, various linear and muliple regression models were developed and a parameric sudy was conduced in order o obain he mos suiable and pracically applicable relaionships. Based on he analysis carried ou, he conclusions of he sudy can be summarized as follows: The regression equaion demonsraes ha a direc linear relaionship exis beween compression index value and he soil parameers such as waer conen, liquid limi, plasic limi and field densi. A good agreemen was observed beween he acual and prediced values of compression index, which is he main characerisic of a good fiing model. In he regression equaions, he value of R 2 increases wih increase in independen variable, which indicae ha he value of compression index influence he value of soil parameers such as waer conen, liquid limi, plasic limi and field densi. The relaions will be useful no only for single person bu also for he governmen agencies, who are involved in consrucion of building work and oher srucures in he sudy area. The ime and cos required for shear srengh es will be reduced. ACKNOWLEDGMENT I would like o sincerely hank Ass Prof. C. K Cherian, Head of he Civil Engineering Deparmen, Saingis College of Engineering, for his kind suppor hroughou he compleion of his venure and also my P.G coordinaor Ass Prof. Joe G Philip, Deparmen of Civil Engineering, Saingis College of Engineering, for he suppor and encouragemen during he course of his work. REFERENCES [1] Reza Jemshidi, Parichehr Tizpa, Mohammed Razool, sandrolemos, MehranKarimpour,- The use of index parameers o predic soil geoechnical properies, IOSR Journal of Mechanical and Civil Engineering, vol. 11, PP , June [2] Akayuli,C.,Ofosu,B.,Nyako,S.O.andOpuni,K.O., (2013),-The Influence of Observed Clay Conen on Shear Srengh And Compressibili of residual sandy Soils, Inernaional Journal of EngineeringResearchandApplicaions,vol.3,pp , July 2013 [3] Vikas Kumar Jain, Mahabir Dixi and Dr. R. Chira (2015),- Correlaion of Plasici Index and Compression Index of Soil, Inernaional Journal of Innovaions in Engineering and Technology (IJIET),Vol 5, Issue 3,June [4] Slame Widodo and Abdelazim Ibrahim,- Esimaion of Primary Compression Index () Using Physical Properies of Ponianak Sof Clay, Inernaional Journal of Engineering Research and Applicaions, Vol. 2, Issue 5, pp , Sepember- Ocober [5] Terzaghi, and peck, - Soil Mechanics in Engineering pracice, 1s ed., John Wiley and Sons, New York, IJIRT INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY 38