Risk Assesment for Large-scale Slopes Using Multiple Regression Analysis

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ISSN 1229-2427 (Print) ISSN 2288-646X (Online) http://dx.doi.org/10.7843/kgs.2013.29.11.99 ळćܤЛèलॸǷϬܫ ۔29ĕ 11ॣ 2013Ǭ 11٫ pp. 99 G 106

JOURNAL OF THE KOREAN GEOTECHNICAL SOCIETY Vol.29, No.11, November 2013 pp. 99 G 106

ᘯⱜ㧗ᄋ⃏⑨⪏# ⪿⧴㢧# ᙋᄧᷳ# ↏㐓ᶿ⪣# ⩏㤣ᜏ# 㜔ཋ

Risk Assesment for Large-scale Slopes Using Multiple Regression Analysis

㇫㋼ ᛫¹ Lee, Jong-Gun ㈜ ✋ ⲏ² Chang, Buhm-Soo ᢷ ㄠ ⲏ³ Kim, Yong-Soo ⮔ ㈣ㄨ⁴ Suk, Jae-Wook

▯ ㍷ ⵔ⁵Moon, Joon-Shik

Abstract

In this study, the correlation of evaluation items and safety rating for 104 of large-scale slopes along the general national road was analyzed. And, we proposed the regression model to predict the safety rating using the multiple regressions analysis. As the result, it is shown that the evaluation items of slope angle, rainfall and groundwater have a low correlation with safety rating. Also, the regression model suggested by multiple regression analysis shows high predictive value, and it would be possible to apply if the evaluation items of excavation condition and groundwater (rainfall) are not clear.

⧟# # # ⴋ

ٍ҆ĵقԴəێъĶʪԜقܕۦॠə2ܛҼ࢐ϸ104Òՙقʂ३ԜࢗथÀ२ЀęԜࢗथÀˣśۆٍěՁں

қԵॠČ, थÀ२ЀںČͲॢɰܼধŊқԵںࣀ३؋ۻˣśںٚࠑॣսەəধŊϿ঍ں܃֨ॠٕɰ. қԵĀę, ԐϸąԐٮÌڍфݓॠսۆथÀ२ЀڹԜࢗथÀˣśęۆٍěՁۋǰڹìڷͿқԵʼؽɰ. ̚ॢ, ɰܼধŊқԵ ںࣀ३܃֨ʽধŊϿ঍ڹۼࠄԜࢗ, Ìڍфݓॠսۆ२ЀںࣺɳॠşرͲڏܓæقԴটڌۋÀɠॢìڷͿ

ࣺɳʽɰ.

Keywords : Large-scale slope, Risk assessment, Chi-square test, Multiple regression analysis

1. ⑧# ᮫

ş঳ѺজۆٖॳڷͿݚܼÌڍфʂ঍ࢗॄǴ֥ۆ

ҾʪÀݒÀॠČەڷ϶, ÌڍÌʪфݓ՚Ìڍ͟ˣۆ

Ìڍ࣢Ձق˰͆Ҽ࢐ϸۦ३ə۾޲ҾʪÀݒÀॠČ

őϿÀʂ঍জʼČەɰ. ̚ॢ, ĶǴۆ߯Ŗ10țÂٍ

Ìս͟ۋथț҃ɰأ10mm ݒÀॠٕڷ϶, 2100țūݓ

أ200mm ۋԜݒÀॣìڷͿٚԜʼر(şԜߔ, 2012)

߯ŖۆҼ࢐ϸۦ३࣢ՁڹؘڷͿʌڎ̤͸३ݗìڷ ͿٚԜʽɰ.

ĶǴقԴəҼ࢐ϸۆڦॹʪथÀεڦ३Áşěѻ ͿɰتॢथÀşܵں܃֨ॠيটڌॠČەڷ϶(KICT 2002; KISTEC 2004; KEC 2004; NIDP 2009) Óěۺۍ

थÀݓशфşܵں܃؋ॠşڦ३ɰتॢٍĵÀսॱ

1ⲯ㬦⭪, 㧶ሇ❶▾⧢Ⲟᆏគ#❶▾⧢Ⲟ⪊ሆ☦#►Ⱎ⪊ሆ⭪#(Member, Senior Researcher of Institute of Infrastructure Safety, Korea Infrastructure Safety Corporation, Tel:

+82-31-910-4292, Fax: +82-31-910-4181, [email protected], Corresponding author, ᇪ❺ⲚⰪ)

2ⲯ㬦⭪, 㧶ሇ❶▾⧢Ⲟᆏគ# ❶▾⧢Ⲟ⪊ሆ☦# ⪊ሆ☦ⰿ# (Member, Director of Institute of Infrastructure Safety, Korea Infrastructure Safety Corporation)

3ⲯ㬦⭪, 㧶ሇ❶▾⧢Ⲟᆏគ# ❶▾⧢Ⲟ⪊ሆ☦# ⚲▷⪊ሆ⭪# (Member, Senior Researcher of Institute of Infrastructure Safety, Korea Infrastructure Safety Corporation) 4ⲯ㬦⭪, 㧶ሇ❶▾⧢Ⲟᆏគ# ❶▾⧢Ⲟ⪊ሆ☦# ⪊ሆ⭪# (Member, Researcher of Institute of Infrastructure Safety, Korea Infrastructure Safety Corporation)

5ⲯ㬦⭪, ᅗ∛រ㧳ᇪ#ᄎ▾㫲ᅗ⩪ᖢ⹚ᆏ㧳√#ᇪ⚲#(Member, Professor of School of Architectural, Civil, Environmental and Energy Engrg., Kyungpook National Univ.)

*→# ᘖῒ⩪# រ㧶# 㘺⯲Ḗ# ⭪㧲ᜮ# 㬦⭪⯚# 2014ᗞ# 5⭮# 31Ⱆዦ⹚# ኒ# ᕎ⭃⯞# 㧳㬦ᳶ# ↎ᕎⶖ❶ዊ# ₮ᰧᝢ᝾. ⲚⰪ⯲# ᄚ㘺# ᕎ⭃ᆖ# 㨂፲# ᘖῒ⹫⩪# ᄦⱆ㧲⪆# ᥶ṗᝢ᝾.

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ʼؽɰ. ࣢০, Kangę Um(2007)ڹॢĶ֨Ժ؋ۻėɳ قԴ܃֨ʽथÀѪ(2004)قʂ३ʫςՁê܁ں֬֨

ॠيथÀۍۙٮ؋ۻˣśۆٍěՁںқԵॠČɰܼ

ধŊқԵں ࣀ३ ٚࠑ ধŊϿ঍ں ܃֨ॠٕɰ. ̚ॢ, Chae ˣ(2004)ڹͿݓࣰ֟ধŊқԵںࣀ३ԓԐࢗь Ԧقʂॢঝέ΁ۺٚࠑϿʝں܃֨ॠٕڷ϶, LEEٮ

KIM(2004)ڹ Ϳݓࣰ֟ধŊқԵں ࣀ३ ؒъҼ࢐ϸۆ

؋܁ՁथÀεٚࠑॣսەəঝέ΁ۺϿʝں܃؋ॠ

ٕɰ.

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ॠČەɰ. ࣢০, Ҽ࢐ϸۆąڍ, ֨࣢Ѫق˰͆‘ݓϸ ڷͿҙࢢٍݔȭۋ50йࢢۋԜںप॥ॢۼࢹҙͿԴ

ɳێսथٍۤ200йࢢۋԜۍۼࢹԐϸ’ڹ2ܛ֨ԺН ق३ɾʼдͿܳşۺ(13ț)ڷͿ܁н۾êں֬֨ॠ يآॢɰ. ܁н۾êڹԜࢗथÀٮ؋܁ՁथÀͿĵқ ʼ϶ԜࢗथÀĀęٮ؋܁ՁथÀĀęܼ߯۹ˣśۋ

ܛ०थÀĀę(؋ۻˣś)ͿĀ܁ʽɰ(Ķࢹ३تҙ, 2010).

2ܛҼ࢐ϸڹ֩(1)ęÏۋࢹ֮ࠗʪڱ(soil depth ratio:

SR)ق˰͆ࢹԐҼ࢐ϸфؒъҼ࢐ϸڷͿĵқʼ϶, ؒ ъҼ࢐ϸڹݓъÌʪ࣢Ձф֩(2)ۆҸ΀ࡾşҼ(block size ratio: BR)ق˰͆ۼνؒъҼ࢐ϸ, ࣷթؒъҼ࢐ϸ,

ٍأؒъҼ࢐ϸڷͿՃқʽɰ(Ķࢹ३تҙ, 2010). Ҹ΀ࡾ

şҼ(BR)قԴҸ΀ࡾşݓս(block size index: Ib)əÁ

ۼνķقʂॢथŒÂü(Si)ۆԓցथŒڷͿ܁ۆʽɰ.

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ì ¢_{ƀ á ć} Ð

ā

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(2)

ۋܼۼνؒъҼ࢐ϸۆԜࢗथÀəۍۤŒَ(), ݓ

ъѺ঍(), ĵܓНѺ঍(), ьԦőϿ()ۆ२Ѐںप

॥ॠə՜ԜԜࢗथÀٮۼνܳॳ(), ۼνąԐ(), ۼ νԜࢗ(), Ҽ࢐ϸąԐ(), Ìڍфݓॠս(), ۼࠄ Ԝࢗ(), ѕսܓæ(), ҃঒/҃ÌԜࢗ()ۆ२Ѐںप

॥ॠəࣷĨڅۍथÀͿۋΘرݕɰ(Ķࢹ३تҙ, 2010).

՜ԜԜࢗथÀٮࣷĨڅۍथÀεࣀ३՜ԜԜࢗݓս (f1) фࣷĨڅۍݓս(f2)ÀĀ܁ʼ϶(֩(3), (4)), ֩(5) ٮÏۋۋ˞ۆܛ०ۺۍथÀͿԜࢗथÀĀ॥ݓս(F) ÀĀ܁ʽɰ(Ķࢹ३تҙ, 2010)

ƄÎ á ć

ā

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ԜࢗˣśۋĀ܁ʽɰ(Ķࢹ३تҙ, 2010).

थÀ२Ѐ˞ۆʂҙқڹگ؋۾êфÂɳॢࠑ܁ں

ࣀ३थÀÀÀɠॠǣێҙथÀ२ЀڹۙΒҙܔфই

ۤيæˣڷͿۍ३ࣷ؊ॠş৪˜ąڍÀψɰ. थÀ२ Ѐڹʂҙқ۾êۙۆܳěۺۍࣺɳق˰͆Ā܁ʼر

Ҽ࢐ϸۆĀ॥ԜࢗÀÓěۺڷͿъٖʼşقə܃أۋ

˰δɰ.

ٍ҆ĵقԴəێъĶʪԜقқपॠə2ܛҼ࢐ϸق

ʂॢ܁н۾êĀęεц࢖ڷͿÁÁۆԜࢗथÀ२Ѐ ęԜࢗथÀˣśۆٍěՁڮИεқԵॠٕɰ. ̚ॢ, ɰܼধŊқԵںࣀ३ԜࢗथÀˣśقй࠘əÁԜࢗ

थÀ२ЀۆٖॳʪεқԵॠČथÀ२Ѐۆܓ०ق˰

͆Ҽ࢐ϸۆԜࢗथÀˣśںٚࠑॣսەəধŊϿ঍

ں܃֨ॠٕɰ.

(3)

Ilj1# 41# Vwdwxv# ri# odujh0vfdoh# vorshv

2. ⭠Ὃ⭛࿋

ĶࢹİࣀҙقԴə1997țҙࢢێъĶʪԜقқपॠ əҼ࢐ϸۆߕćۺۍڮݓěνεڦ३‘ʪͿҼ࢐ϸڮ ݓěν֨֟ࢰ’ںÒьॠيڏڌܼۋ϶, 2012țںşܵ

ڷͿێъĶʪԜقқपॠəҼ࢐ϸڹߪ29,757Òՙ ۋɰ(Ķࢹ३تҙ, 2012). ۋܼ2ܛҼ࢐ϸڹ148Òՙۋ

϶, ٍ҆ĵقԴə148ÒՙܼۼνؒъҼ࢐ϸق३ɾ ʼə104Òՙۆ܁н۾êۙΒεটڌॠيқԵں֬֨

ॠٕɰ.

Fig. 1ڹ 104Òՙۆ ۼνؒъҼ࢐ϸق ʂॢ ইডں

҃يܳČەɰ. Ҽ࢐ϸڹąԜݓًقÀۤψۋқप (40.4%)ॠČەڷ϶, ąşݓًقəɳ2Òՙ(1.9%)χڦ

࠘ॠČەɰ. ۋ͠ॢݓًѻқपٮɵνҼ࢐ϸۆĵՁ

ؒܛڹজՁؒ(33.7%), ࣅۺؒ(34.6%), ѺՁؒ(31.7%) ۋäۆŒˣॠóқपॠəìڷͿǣࢍǮɰ. Ҽ࢐ϸۆ

थŒٍۤڹ341mͿٍۤۋ200300mۍҼ࢐ϸۋۻ ߕۆ52%ۋ϶500m ۋԜۆҼ࢐ϸڹ7.7%قҝęॠٕ

ɰ. Ҽ࢐ϸۆथŒȭۋə63mͿۻߕۆ 51%À50

60mۋ϶, ȭۋ70m ۋԜۆҼ࢐ϸڹ13.5%χқपॠČ

ەəìڷͿǣࢍǮɰ.

थŒ՜ԜԜࢗݓսфࣷĨڅۍݓսə0.19, 0.36ۋ

϶थŒĀ॥ݓսə0.31ͿқԵʼؽɰ. Fig. 2ٮÏۋ

՜ԜԜࢗݓսфࣷĨڅۍݓսəĵՁؒܛق˰͆ই ۹ॢ޲ۋε҃ۋݓə؍ڷǣ, ࣅۺؒڷͿĵՁʽҼ࢐

ϸۆ՜ԜԜࢗݓսÀজՁؒфѺՁؒڷͿĵՁʽҼ

࢐ϸۆ՜ԜԜࢗݓս҃ɰȭڹÉںǣࢍǻɰ. Ā॥ݓ սق˰δ؋ۻˣśڹBˣśۋ54Òՙ, Cˣśۋ49Ò ՙ, Dˣśۋ1ÒՙͿԓ߻ʼرʂҙқۋ֨ԺНۆ؋ۻ قəݓۤۋػČێҙąйॢĀ॥ۋьԦॠٕäǣÂ ɳॢ҃սÀज़څॢԜࢗۍìڷͿǣࢍǮɰ(Table 1).

ۋəʂőϿҼ࢐ϸۆ࣢ՁԜ, ңĨ֨क़३ʪÀȭɰə

۾ںÇ؋ॢ҃սۺۍԺćф֪՚ॢ܁Ҽق˰δìڷ Ϳࣺɳʽɰ. BˣśۆĀ॥ݓսə0.1710.290ۆѩڦ εÀݓ϶थŒĀ॥ݓսə0.25ͿǣࢍǮɰ. Cˣśۆ

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қԵʼؽɰ.

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3. ᜐᴈ⑼# ࿋⭠

˃Òۆѩܳ঍ۙΒقʂॢʫςѺսٮܛ՚Ѻսۆ

ٍěՁيҙəʫςՁê܁ںࣀ३қԵۋÀɠॠɰ. ѩ

ܳ঍ۙΒə˃Òۆѩܳ঍Ѻս(X,Y)εÁÁI, J սܵ

قԴXεॱڷͿYεَͿݓ܁३I×JÒۆĀ०ܓæں

शইॠəқॣशͿĵՁॣսەɰ(܁ġϿٮ߯ڌԵ, 2002). ̚ॢ, XۆÁսܵقԴYۆܓæҙқपÀʴێ

ॣ˺˃ѺսəࣀćۺڷͿʫςۋ͆Čॠ϶, ۋəʫ ςѺսٮܛ՚ѺսÂقٍěՁۋۻঅػɰəìںۆ йॢɰ(KangęUm, 2007). ʫςՁê܁قԐڌʼəê

܁ࣀć͟ڹࠢۋ܃ĕࣀć͟(X²)ڷͿ֩(6)ęÏۋࠑ܁

ʼ϶, ֬܃ě޶ॢҾʪս(ě޶Ҿʪ, Ou)ٮÁѺսÀԴ Ϳʫςۋ͆ČÀ܁ॣ˺ঝέۺڷͿćԓʼəۋ΁ۺ ۍҾʪս(şʂҾʪ, Eu)ۆ޲ۋεҼİ३ԓ߻ʽɰ.

±^Ïá

ā

_Ɠ ^ć^Þ^¨^Ɠ^à_Ɠ^Ɠ^ß

Ï

(6)

ٍ҆ĵقԴəʫςѺսεÁथÀ२Ѐۆ۾սͿ, ܛ

՚ѺսεԜࢗथÀˣśڷͿԺ܁ॠٕڷ϶Table 2ٮ

Ïۋ˃ѺսقʂॢĀ०ܓæںқॣशͿǣࢍǴؽɰ.

̚ॢ, SPSS ࣀćःࢅݓεԐڌॠيࠢۋ܃ĕ(X²) ࣀć

͟ęࣀć͟قʂॢڮۆঝέ(p-value)ںԓ߻ॠيʫς Ձê܁ں֬֨ॠٕɰ.

थÀ२ЀęԜࢗथÀˣśقʂॢʫςՁê܁Āę εTable 3قǣࢍǴؽɰ. ÁथÀ२Ѐقʂ३ԓ܁ʽ

ࠢۋ܃ĕࣀć͟(X²)ۆ ڮۆঝέ(p-value)ۋ 0.05ۋԜۋ ϸ, ʫςѺսٮܛ՚ѺսÀࣀćۺڷͿʫςۋ͆əŊ

ИÀԺںÌॠó޽࢘ॠóʼдͿ˃ѺսۆٍěՁڹ

ǰڹìڷͿ҇սەɰ. ъϸ, ڮۆঝέ(p-value)ۋ0.05 йχێąڍ, ʫςѺսٮܛ՚ѺսəʫςՁۋĀيʼ رٍěՁۋȭɰəìںۆйॢɰ. ˰͆Դ, ࠢۋ܃ĕࣀ ć͟(X²)ۆڮۆঝέ(p-value)ق˰͆‘ԐϸąԐ’ٮ‘Ì ڍфݓॠս’ε܃ٽॢ10ÒۆथÀ२ЀڹҼ࢐ϸۆ

ԜࢗथÀˣśęٍěՁۋȭڹìڷͿқԵʼؽɰ. Ԑ ϸąԐə104Òՙܼ, 85Òՙ(81.7%)À4564°ۆѩڦ قқपॠČەڷ϶64°ۋԜۍҼ࢐ϸڹ14Òՙ(13.5%) قҝęॠɰ. ̚ॢ, Table 2ٮÏۋԐϸąԐۆѺজق

˰͆ԜࢗथÀˣśۆѺজÀ̤͸ॠݓ؍؉ࣀćۺڷ ͿٍěՁۋǰڹìڷͿࣺɳʽɰ. Ìڍфݓॠսۆ

थÀ२Ѐ̚ॢ1ێ߯ʂÌڍ͟ۋ0100mmۍѩڦق

91Òՙ(87.5%)ÀқपॠČەڷ϶, Ìڍ͟ۆѺজق˰

δԜࢗथÀˣśۆѺজÀ̤͸ॠݓ؍ɰ. Ìڍфݓ ॠսəÁҼ࢐ϸۆݓॠսڦ̚ə1ێÌڍ͟ڷͿथ Àʼǣ, ԺćۙΒεঝۍॠşرͲڏąڍÀψڷдͿ

ʂҙқ1ێÌڍ͟ںşܵڷͿथÀεॠóʽɰ. ॠݓ χ, 1ێÌڍ͟ԓ߻֨ɾ३ٍʪ߯ʂێÌڍ̚͟əथ ț߯ʂێÌڍ͟ˣęÏڹۺڌşܵۋϼঝॠݓ؍؉

थÀ२ЀقʂॢܳěۺࣺɳۆۆܕʪÀȭş˺Лق

ԜࢗथÀˣśقй࠘əٖॳۋǰڹìڷͿԐΒʽɰ.

4. ᘯⱜ㧗ᄋ⃏⑨

ɰܼধŊқԵڹ˃ÒۋԜۆʫςѺսٮܛ՚Ѻս ÂۆԸ঍ۺۍěćεԺϼॠəࣀćۺşѪڷͿ, ԓ߻

ʽĀ܁ćս(R²)ͿধŊϿ঍ۆۺ०܁ʪεࠑ܁ॣսە ڷ϶t-ࣀć͟фFÉںࢹʂͿÒѻধŊćսٮধŊϿ

(6)

Wdeoh# 71# Dssolhg# yduldeohv# lq# hdfk# fdvh Yduldeohv

Fdvh Y4 Y5 Y6 Y7 Y8 Y9 Y: Y43 Y44 Y45

FDVH# 4          

FDVH# 5        0  

FDVH# 6   0      0 0

Wdeoh# 81# Uhvxow# ri# pxowlsoh# uhjuhvvlrq# dqdo|vlv

Yduldeohv FDVH# 4 FDVH# 5 FDVH# 6

Frhiilflhqwv w0ydoxh# +ws, Frhiilflhqwv w0ydoxh# +ws, Frhiilflhqwv w0ydoxh# +ws,

Frqvwdqw 31376 813;:# +?313334, 31384 71<88# +?313334, 313:< 9133;# +?313334,

Y4 31344 8197:# +?313334, 31345 813;:# +?313334, 3133; 51535# +3136,

Y5 31349 ;17::# +?313334, 3134: :15:7# +?313334, 3134: 813<;# +?313334,

Y6 3134: ;198;# +?313334, 3134< :1<88# +?313334, 0 0

Y7 31346 81<79# +?313334, 31348 81:7<# +?313334, 31345 61637# +31334,

Y8 31345 481487# +?313334, 31345 451:85# +?313334, 31345 ;17:7# +?313334,

Y9 31345 4:15;3# +?313334, 31344 4614:7# +?313334, 31346 44153;# +?313334,

Y: 31344 81993# +?313334, 31347 81:84# +?313334, 31344 61636# +31334,

Y43 31347 91:49# +?313334, 0 0 3135: ;1933# +?313334,

Y44 31348 71;<3# +?313334, 31356 91964# +?313334, 0 0

Y45 31344 81:;8# +?313334, 31346 81<34# +?313334, 0 0

U⁵ 31<7< 31<57 31;77

I0ydoxh 4:41;:7 45918:9 :61<89

Is0ydoxh ?313334 ?313334 ?313334

Wdeoh# 91# Uhjuhvvlrq# prgho# irupxod# iru# hdfk# fdvh

Fdvh Uhjuhvvlrq# prgho# irupxod

FDVH# 4 SGL# @# 31376.31344Y4.31349Y5.3134:Y6.31346Y7.31345Y8.31345Y9.31344Y:.31347Y43.31348Y44.31344Y45 FDVH# 5 SGL# @# 31384.31345Y4.3134:Y5.3134<Y6.31348Y7.31345Y8.31344Y9.31347Y:.31356Y44.31346Y45

FDVH# 6 SGL# @# 313:<.3133;Y4.3134:Y5.31345Y7.31345Y8.31346Y9.31344Y:.3135:Y43

঍قʂॢڮۆՁںÁÁê܁ॣսەɰ(Ì֧ࢗ, 2006).

ٍ҆ĵقԴəʫςՁê܁قԴٍěՁۋǰڹìڷ ͿқԵʽ‘ԐϸąԐ’ٮ‘Ìڍфݓॠս’ε܃ٽॢ10 ÒۆथÀ२ЀںʫςѺսͿԺ܁ॠČ, ԜࢗथÀق˰

δĀ॥ݓսεܛ՚ѺսͿԺ܁ॠيɰܼধŊқԵں֬

֨ॠٕɰ. ̚ॢ, Դ΁قԴسśॢцٮÏۋथÀ२Ѐ

ܼۙΒҙܔфইۤيæˣڷͿÓěۺۍࣺɳۋرͲ ڏ२Ѐں܃ٽॢąڍقʂ३ԴʪқԵں֬֨ॠي (Table 4) ܁н۾êںࣀॢĀ॥ݓսٮٚࠑʽĀ॥ݓս εҼİқԵॠٕɰ. Case 2əcase 1ۆʫςѺսقԴ

‘ۼࠄԜࢗ’ε܃ٽॢąڍۋ϶, case 3əcase1ۆʫςѺ սقԴۍėĵܓНęěʹʽथÀ२Ѐۍ‘ĵܓНĀ॥’,

‘ѕսܓæ’, ‘҃঒/҃ÌԜࢗ’ε܃ٽॢąڍۋɰ.

қԵĀę, Table 5ٮÏۋcaseѻĀ܁ćս(R²)əÁ Á0.949, 0.924, 0.844Ϳԓ߻ʼرϿ˜caseقԴধŊ

Ͽ঍ڹȭڹԺϼͳں҃ۋČەɰ. ̚ॢ, caseѻʫςѺ սقʂॢt-ࣀć͟ۆڮۆঝέ(tp) фۻߕধŊϿ঍ق

ʂॢFÉۆڮۆঝέ(Fp-value)ۋϿ˃0.05҃ɰۚڷд ͿÁÁۆʫςѺսфۻߕধŊϿ঍ڹڮۆॠɰČࣺ

ɳॣսەɰ. қԵĀęق˰͆, Ҽ࢐ϸۆԜࢗˣśٚ

ࠑںڦॢٚࠑĀ॥ݓս(Predicted defect index: PDI) ধ ŊϿ঍֩ںTable 6قǣࢍǴؽɰ.

Fig. 3ęÏۋ, case 1ق˰͆ԓ߻ʽٚࠑĀ॥ݓսə

Bˣśۆąڍ0.1640.298, Cˣśۆąڍ0.3010.545 ۆѩڦεÀݓ϶104Òՙܼ98Òՙ(94.2%)قʂॢԜ

ࢗथÀˣśںٚࠑॠٕɰ. Case 2ق˰͆ԓ߻ʽٚࠑ Ā॥ݓսəAˣśۆąڍ1ÒՙͿ0.148ۋ϶Bˣśۆ

ąڍ0.1890.299, Cˣśۆąڍ0.3040.536ۆѩڦ εÀݓČ96Òՙ(92.3%)قʂॢԜࢗथÀˣśںٚࠑ ॠٕɰ.

(7)

Ilj1# 61# Glvwulexwlrq# ri# suhglfwhg# ghihfw# lqgh{# lq# hdfk# fdvh

Ilj1# 71# Glvwulexwlrq# ri# huuru# udqjh# lq# hdfk# fdvh

Case 2əcase 1ۆʫςѺսقԴ‘ۼࠄԜࢗ’ε܃ٽॢ

ąڍۋɰ. Case 2ۆٚࠑέۋ92.3%Ϳȭڹìڹ‘ۼࠄ Ԝࢗ’ۆथÀ२ЀۋԜʂۺڷͿѺѻͳۋǰڼںۆй

ॢɰ. ۋə‘ۼࠄԜࢗ’ÀԸŒَьࣷ, ܃رьࣷ, şćĹ

޳, ێъьࣷ, ҝٰۻьࣷˣۆĹ޳ѓѪق˰͆थÀʼ ə२ЀڷͿԺćۙΒÀ҃ܕʼرەݓ؍äǣşܕۙ

Βεࣀ३ঝۍۋҝÀɠॢąڍÀψ؉ÓěۺۍथÀ Àرͷş˺ЛۍìڷͿࣺɳʽɰ.

Case 3ق˰͆ԓ߻ʽٚࠑĀ॥ݓսəBˣśۆąڍ

0.1710.293, Cˣśۆąڍ0.3030.505ۆѩڦεÀ ݓ϶case 2ٮυ޴ÀݓͿ96Òՙ(92.3%)قʂॢԜࢗ

थÀˣśںٚࠑॠٕɰ. Case 3əcase 1ۆʫςѺսق Դ‘ĵܓНѺ঍’, ‘ѕսܓæ’, ‘҃঒/҃ÌԜࢗ’ε܃ٽॢ

ąڍۋɰ. Case 2ٮυ޴ÀݓͿcase 3ۆٚࠑέʪ92.3%

ͿǣࢍǮɰ. ۋə‘ĵܓНѺ঍’, ‘ѕսܓæ’, ‘҃঒/҃

ÌԜࢗ’ ̚ॢԜࢗथÀĀ॥ݓսԓ߻قй࠘əٖॳʪ Àǰڼںۆйॢɰ. Case 3قԴ܃ٽʽѺս˞ڹҼ࢐

ϸقԺ࠘ʽۍėĵܓНقʂॢथÀ२ЀڷͿ׆, ۋ˞

ۆѺѻͳۋǰڹìڹ΀҇࣡фء࠶ˣۆ҃Ìėۋ

ݓܼقԺ࠘ʼرەäǣǤԵѓݓڐࢍνфʮҤےė

ˣۆ҃঒ėۋ֩ԦڷͿʙيەر۾êۙۆܳěۺۍ

þ३قۆܕʪÀȭş˺ЛۍìڷͿࣺɳʽɰ.

ԜşۆқԵĀęقԴ, case 1, case 2, case 3ق˰͆

ٚࠑʽԜࢗथÀˣśڹÁÁ98, 96, 96ÒՙͿࣀćۺ ڷͿڮۆйॢ޲ۋ͆Č҃şقəرͲړۋەɰ. ॠݓ χ, ۋəcase 2ٮcase 3قԴʴێॢʫςѺսε܃ٽॢ

ìۋ؉ɦ͆caseق˰͆ۺڌʽʫςѺսۆٖॳʪε

थÀॠşڦ३ÁşɰδʫςѺսε܃ٽॠيқԵں

֬֨ॢìۋɰ. ˰͆Դ2ܛۼνؒъҼ࢐ϸقʂॢ۾

ê֨, ԺćۙΒÀ҃ܕʼرەݓ؍؉Ĺ޳ѓѪںࣺɳ ॠşرͲڏąڍقəٍ҆ĵۆcase 2ق˰δধŊϿ

঍֩ںটڌॣսەںìۋɰ. ̚ॢ, ইۤقԴ҃঒ė

ф҃ÌėˣۍėĵܓНۆԜࢗεࣷ؊ॠş৪˜ąڍ قəٍ҆ĵۆcase 3ق˰͆ʪ߻ʽধŊϿ঍ںটڌ ॠيԜࢗथÀˣśںٚࠑॣսەںìۋɰ.

ɳ, Fig. 3ęÏۋcaseق˰͆١޲ۆ߯ʂÉڹÇՙ

ॣսەڷǣ(case 2) ɰܼধŊқԵۆ࣢ՁԜʫςѺս Àܶر˞ս΀ٚࠑʽĀ॥ݓսۆथŒ١޲ڱۋݒÀ ॠдͿ, ۋεČͲॠيটڌॠəìۋࢍɾॣìڷͿԐ Βʽɰ.

5. ࿻# ᮫

ٍ҆ĵقԴəێъĶʪԜقқपॠə104Òՙۆ2 ܛҼ࢐ϸ۾êۙΒεц࢖ڷͿ, थÀ२ЀęԜࢗथÀ

ˣśÂۆٍěՁںқԵॠČɰܼধŊқԵںࣀ३Ԝ

ࢗथÀˣśںٚࠑॣսەəࣀćۺধŊϿ঍ں܃؋

ॠٕɰ. қԵĀęəɰڼęÏɰ.

(1) ێъĶʪԜقқपॠə2ܛҼ࢐ϸۆथŒ՜ԜԜ

ࢗݓսфࣷĨڅۍݓսəÁÁ0.19, 0.36ۋ϶थŒ

Ā॥ݓսə0.31ͿқԵʼؽɰ. Ā॥ݓսق˰δ؋

ۻˣśڹBˣśۋ54Òՙ, Cˣśۋ49Òՙ, Dˣś ۋ1Òՙۋ϶ĵՁؒܛق˰δ՜ԜԜࢗݓսфࣷ

Ĩڅۍݓսۆ޲ۋə̤͸ॠݓ؍ڹìڷͿܓԐʼ ؽɰ.

(2) 12ÒۆथÀ२Ѐę؋ۻˣśۆٍěՁںқԵॢĀ ę, ‘ԐϸąԐ’ ф‘Ìڍфݓॠս’ε܃ٽॢ10Ò

२ЀڹԜࢗथÀˣśęٍěՁۋȭڹìڷͿǣ

(8)

ࢍǮɰ. ‘ԐϸąԐ’ ф‘Ìڍфݓॠս’२Ѐڹ2ܛ

Ҽ࢐ϸۆ࣢ՁԜѺѻͳۋػäǣथÀşܵۋϿ঒

ॢìڷͿࣺɳʼдͿÓěۺۍथÀεڦ३ÒԸ ۋज़څॢìڷͿԐΒʽɰ.

(3) ɰܼধŊқԵںࣀ३܃֨ʽধŊϿ঍ڹcase 1ق Դ98Òՙ(94.2%), case 2قԴ96Òՙ(92.3%), case 3قԴ96Òՙ(92.3%)ۆԜࢗˣśںٚࠑॠيٚࠑ έۋȭڹìڷͿқԵʼؽɰ. Case 1 ф2ۆٚࠑ έۋȭڹìڹ‘ۼࠄԜࢗ’, ‘ĵܓНѺ঍’, ‘ѕսܓ æ’, ‘҃঒/҃ÌԜࢗ’ۆ२ЀۋÓěۺथÀÀرͲ ڗʂҙқܼÂѩڦۆÉڷͿथÀʼдͿԜࢗथ Àˣśقй࠘əٖॳۋǰş˺ЛۍìڷͿࣺɳ ʽɰ.

(4) ٍ҆ĵəێъĶʪԜقқपॠəҼ࢐ϸںʂԜ ڷͿॠٕڷдͿۺڌѩڦε2ܛҼ࢐ϸۻߕͿঝ ʂॠşڦ३ԴəɰتॢЀۺڷͿæԺʽ2ܛҼ࢐

ϸقʂॢ߸ÀٍĵÀज़څॠ϶, ٍěՁۋǰڹì ڷͿқԵʽथÀ२Ѐقʂ३ԴʪÓěۺۍथÀÀ

ۋΘرݗսەʪ΀߸ÀêࢹÀۋΘر܋آॣì ڷͿࣺɳʽɰ.

ཛ⏷⪣# ᅋ

ٍ҆ĵəĶࢹİࣀҙقԴڏٖܼۍ‘ʪͿҼ࢐ϸڮ ݓěν֨֟ࢰ’ۆݓڙںы؉սॱʼؽڷ϶, ۋقŪڹ

ÇԐε˚ςɦɰ.

⾃ါẃ㤗# (References)

1.Ķࢹ३تҙ(2010), ؋ۻ۾êф܁н؋ۻݕɳՃҙݓࠞ, pp.12-1-52.

2.܁ġϿ, ߯ڌԵ(2002), ѩܳ঍ۙΒқԵÒ΁-SASۆڿڌф३ Ե, ۙڮ؉ࠢʚй, pp.238.

3.şԜߔ(2012), ॢъʪş঳Ѻজۻϐ҃ČԴ, pp.70-81.

4. Chae, B. G., Kim, W. Y., Cho, Y. C., Kim, K. S., Lee, C. O., and Choi, Y. S. (2004), “Development of a Logistic Regression Model for Probabilistic Prediction of Debris Flow”, Journal of Korean Society of Engineering Geology (KSEG), Vol.14, No.2, pp.211-222.

5. Kang, T. S. and Um, J. G. (2007), “Risk Assessment of the Road Cut Slopes in Gyeoungnam based on Multiple Regression Analysis”, Journal of Korean Society of Engineering Geology (KSEG), Vol.17, No.3, pp.393-404.

6. Lee, Y. H. and Kim, J. Y. (2004), “A Proposal of the Evaluation Method for Rock Slope Stability Using Logistic Regression Analysis”, Journal of Korean Society of Rock Mechanics, Vol.12, No.2, pp.

133-141.

7. Korea Infrastructure Safety & Technology Corporation (KISTEC) (2004), Cut Slope Maintenance Manual.

8. Korea Institute of Construction Technology (KICT) (2002), Development and Operation of Cut Slope Management System.

9. Minstry of Land, Transport & Maritime Affairs (2012), Operation of Road Cut Slope Management System in 2012, pp.13-18.

10. Korea Expressway Corporation (KEC) (2004), Development of Cut Slope Maintenance System on the Highway.

11. National Institute for Disaster Prevention (NIDP) (2009), A Study on the Field Survey System Development for Steep Slope.

12. Kang, T. S. (2006), Statistical Analysis on Slope Instability Parameters in Risk Assessment, M.D Thesis, Pukyong National University, pp.53-75.

Received : August 13^th, 2013 Revised : October 7^th, 2013 Accepted : November 14^th, 2013