Development of Approximate Cost Estimate Model for Aqueduct Bridges Restoration - Focusing on Comparison between Regression Analysis and Case-Based Reasoning -

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Received April 30 2013, Revised May 28 2013, Accepted June 11 2013

Copyright ⵑ 2013 by the Korean Society of Civil Engineers

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0)

ǣǣȀȀǤǤȀͳͲǤͳʹ͸ͷʹȀǤʹͲͳ͵Ǥ͵͵ǤͶǤͳ͸ͻ͵ ǤǤǤ

⟖Ḛጎ#ᇚ⊲⟖Ἲ#Ⳃ㬚#ᇚᵳኳ♪␂#♮ⷓ#⁦ᤶ#ᇚ⇚

0#㱊፾⍂⛛ኺ#♪᷾Ꮾ⇖㍒Ḟⴖ#␂ጎἺ#⻏⢪⳺Ḛ#0

ୢՍઽȵ୺୍૳ȵෛઽ

Jeon, Geon Yeong*, Cho, Jae Yong**, Huh, Young***

Development of Approximate Cost Estimate Model for Aqueduct Bridges Restoration

- Focusing on Comparison between Regression Analysis and Case-Based Reasoning -

ABSTRACT

To restore old aqueduct in Korea which is a irrigation bridge to supply water in paddy field area, it is needed to estimate approximate costs of restoration because the basic design for estimation of construction costs is often ruled out in current system. In this paper, estimating models of construction costs were developed on the basis of performance data for restoration of RC aqueduct bridges since 2003. The regression analysis (RA) model and case-based reasoning (CBR) model for the estimation of construction costs were developed respectively. Error rate of simple RA model was lower than that of multiple RA model. CBR model using genetic algorithm (GA) has been applied in the estimation of construction costs. In the model three factors like attribute weight, attribute deviation and rank of case similarity were optimized. Especially, error rate of estimated construction costs decreased since limit ranges of the attribute weights were applied. The results showed that error rates between RA model and CBR models were inconsiderable statistically. It is expected that the proposed estimating method of approximate costs of aqueduct restoration will be utilized to support quick decision making in phased rehabilitation project.

Key words : Aqueduct, Construction Cost Estimate, Regression Analysis, Case-Based Reasoning, Genetic Algorithms

Ⅹಾ

ǎԕ᮹ᙹಽƱ۵ᝡྙ⪵ಽᔢḶࡹ۵׮ᨦᬊᙹෝŖɪ⦹۵ƱపᮝಽᕽᙹಽƱෝ}ᅕᙹ⦹ʑ᭥⧕ᕽ۵ʑᅙᖅĥෝᝅ᜽⦹۵äᯕၵ௭Ḣ⦹ӹ⩥ᰍ

ᔾఖࡹŁᯩ۵ᝅᱶᯕအಽᯕᨱᗭ᫵ࡹ۵Ŗᔍእෝᔑᱶ⧁⦥᫵aᯩ݅. ᯕᩑǍᨱᕽ۵2003֥ᯕ⬥Ʊℕ⦽RCǍ᳑ᙹಽƱᨱݡ⦽ᝅᱢᯱഭෝʑⅩ ಽ}ఖŖᔍእᔑᱶ⫭ȡᇥᕾ(RA) ༉ߙŝᔍಡʑၹ⇵ು(CBR) ༉ߙᮥ}ၽ⦹ᩡ݅. RA ༉ߙ᮹Ğᬑ݉ᙽ⫭ȡ༉ߙᯕ݅ᵲ⫭ȡ༉ߙᅕ݅᪅₉ᮉᯕ

ԏᦹ݅. CBR ༉ߙ᮹Ğᬑᮁᱥ᦭Łญ᷹ᮥᯕᬊ⦹ᩡᮝ໑ᩢ⨆᫵ᯙ᮹aᵲ⊹, ⠙₉, ᙽ᭥᳑Õᮥ↽ᱢ⪵ݡᔢᮝಽ⦹ᩡŁ✚⯩ᩢ⨆᫵ᯙaᵲ⊹᮹ჵ

᭥ෝᱽ⦽⦹ᩍᙹಽƱ}ᅕᙹŖᔍእ᮹ᩩ⊂ᱶ⪶ࠥෝᱽŁ⦹ᩡ݅. RA ༉ߙŝCBR ༉ߙᔍᯕ᮹᪅₉ᮉᮡ☖ĥᱢ₉ᯕෝᅕᯕḡᦫᦹ݅. ᅙםྙᨱ ᕽᱽ᜽ࡽᙹಽƱ}ᅕᙹ}ఖŖᔍእᔑᱶႊჶᮡ}ᅕᙹᔍᨦ᮹᜽⧪ᨱ঑ෙᝁᗮ⦽᮹ᔍđᱶᮥ⦹۵ߑ⪽ᬊࢁᙹᯩᮥäᮝಽʑݡࡽ݅.

áᔪᨕ ᙹಽƱ, }ఖŖᔍእᔑᱶ, ⫭ȡᇥᕾ, ᔍಡʑၹ⇵ು, ᮁᱥ᦭Łญ᷹

֨ėěν

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Fig. 1. Photo of Typical Aqueduct for Irrigation

RA Model

Selection of 4 Influence Factors

Screening of RA Models

Comparison of Error Rate (SRA : MRA)

Decision of RA Model Identification

of structures to be replaced

Analysis of 11 Influence

Factors

Comparison and Application CBR

Model

Selection of 5 Influence Factors

Process of CBR Model

(5 Phases )

Minization of Error Rate

Decision of CRR Model Fig. 2. Schematic Diagram of Approximate Cost Estimate Model for Aqueduct Bridges Restoration

1. ᕽು

ᙹಽƱ۵ᯙඹྙ໦᮹ၽᱥᨱ঑ෙᩍ్Ʊప⩶᜾ᵲ⦹ӹಽᕽ

Łݡಽษ᜽ݡ᮹ᱽ1⪙ᙹಽƱᯙᦥ⑁ᦥᦥ⦝ᦥ(Aqua Appia)a

BC 312֥ᨱ⇶᳑ࡽᯕ௹⩥ᰍʭḡฯᮡᙹಽƱaᮁ౞॒ḡᨱ

ྙ⪵ᮁᔑᮝಽԉᦥᯩᮝ໑, ᔑᨦ⩢໦ᯕ⬥ᨱ۵Ŗ⦺ʑᚁ᮹ၽݍಽ

݅᧲⦽ᰍഭ᪡⩶┽᮹ᙹಽƱaÕᖅࡹᨕ᪵݅. ǎԕ᮹ᙹಽƱ۵

ᝡྙ⪵ෝᔢḶ⦹۵׮ᨦʑၹ᜽ᖅ᮹⦹ӹಽᕽ⩥ᰍ׮ᨦᬊᙹŖɪ ᬊᮝಽอᮁḡࡹŁᯩ۵ߑᯕ۵࠺ᱩʑᨱ۵ᬊᙹŖɪᯕᵲ݉ࡹᨕ

࠺đᮥ⦝⧁ᙹᯩʑভྙᯕ໑, ᔢᙹࠥšᮡ࠺đᮥ⦝⦹ʑ᭥⧕

ḡ⦹ᨱๅᖅࡹᨕḡᔢᨱי⇽ࡹ۵ᙹಽƱaᯕᬊࡹŁᯩḡᦫ݅.

ᙹಽƱ۵ ᬊᙹŖɪ ༊ᱢ᮹ ᙹಽa ࠥᵲᨱ ⦹⃽, ĥł, ࠥಽ,

׮ḡ᪡ᔑḡ॒ᮥ☖ŝ⧁ভÕթḡ༜⦹ÑӹᰆÑญᬑ⫭ෝ⦝⦹ʑ

᭥⧕ ᖅ⊹ࡹ۵ Ʊపᯕ݅(Fig. 1).

ᙹಽƱ۵ᱥǎᨱᔑᰍࡹᨕᯩŁŝၹᙹᯕᔢᯕ30֥ᯕᔢĞŝ

ࡹŁᯩ݅. ᙹಽƱ۵ᯝၹᱢᮝಽ☖ᙹ᮹ʑ܆ᮥ⦹۵ᔢᇡǍ᳑᮹

ᙹಽᇡ᪡ᙹಽᇡෝၼ⊹۵⦹ᇡǍ᳑᮹ƱbŝƱݡၰʑⅩ॒ᮝಽ

Ǎᖒࡽ݅.

ᙹಽᇡ᮹݉໕⩶┽۵}ႊ⩶(Open Type)ŝ⠱ᘥ⩶(Closed Type)ᮝಽᇥඹࡹŁǎԕ᮹Ʊb⩶᜾ᮡT⩶ŝ௝ູ⩶ᮝಽࡹᨕ

ᯩ݅. Ǎ᳑ᰍഭ۵℁ɝ⎹Ⓧญ✙, PS⎹Ⓧญ✙, vᰍ᪡vš॒ᯕ

₉ḡ⦹Ł ᯩ݅.

ᱥǎ׮ᨦᬊᙹಽ۵ⅾᩑᰆᯕ᧞66,650 km(ḡǍࢹ౩᮹᧞1.7 ႑)ᨱݍ⦹໑, əᵲ⦽ǎ׮ᨕⅭŖᔍ(Korea Rural Community Corporation, KRC)ašญ⦹۵ᙹಽƱ۵᧞230 kmᨱᯕ෕۵

äᮝಽ ᦭ಅᲙ ᯩ݅(RRI, 2012)

ᙹಽƱෝ⡍⧉⦹۵ᙹญ᜽ᖅᮡי⬥⪵ࡹÑӹʑ܆}ᖁᯕ⦥᫵

⦽ Ğᬑ ׮ᨕⅭᱶእჶᨱ ঑௝ }ᅕᙹ ࡹ໑, ᦩᱥḥ݉ᮥ Ñℱ

}ᅕᙹݡᔢḡǍಽᖁᱶࡹ໕ᝅ᜽ᖅĥෝ☖⦹ᩍ}ᅕᙹ⦹íࡽ݅.

}ᅕᙹݡᔢŝᙹᵡᮥ⬉ŝᱢᮝಽ᮹ᔍđᱶ⦹ʑ᭥⧕ᕽ۵ʑᅙ ᖅĥෝ☖⧕əႊჶŝእᬊᮥ❭ᦦ⦹ᩍ᧝⦹ḡอ⩥ᰍ۵ᔾఖࡹŁ

ᯩ۵ᝅᱶᯕအಽᝅ᜽ᖅĥᯕᱥ݉ĥᨱ}ᅕᙹ᮹ʑᅙႊ⨆ᮥđᱶ

⦹ʑ ᭥⧕ᕽ۵ }ఖŖᔍእෝ ᔑᱶ⧁ ⦥᫵a ᯩ݅.

ྜྷపᯕᦩᱥḥ݉đŝᨱᕽᱽ᜽ࡹḡᦫŁᯩᮝ໑݅᧲⦽}ᅕᙹ

ݡᔢྜྷపŝŖჶᨱ঑ෙŖᔍእෝᖁᱶ⦹۵äᯕᇩa܆⦹݅. ၹ໕

Ʊℕ Ŗᔍእ۵ ᦩᱥḥ݉ đŝᨱᕽ Ʊℕ ݡᔢᯕ ᯕၙ đᱶࡹᨕ

əȽ༉᪡ྜྷపᯕᱶ⧕Კᯩᮝအಽᮁᔍ⦽Ʊℕᔍಡෝᯕᬊ⦹ᩍ

}ఖŖᔍእෝ ᔑᱶ⦹۵ äᯕ a܆⦹݅.

঑௝ᕽᯕᩑǍᨱᕽ۵ᙹಽƱෝƱℕ⧁Ğᬑᗭ᫵ࡹ۵}ఖŖᔍ እෝe݉⦽ʑᅙᱶᅕอᮥᯕᬊ⦹ᩍᔑᱶ⧁ᙹᯩ۵ႊჶᮥ}ၽ⦹

Łᯱ ⦹ᩡ݅. ᙹಽƱᨱ ݡ⦽ }ఖŖᔍእ ᔑᱶ ༉ߙᮡ ḡɩʭḡ

ᅕŁࡹŁᯩḡᦫᦥᔍᬊᯕᛞŁᔍಡaฯᮡ☖ĥᱢʑჶᯙ⫭ȡᇥ ᕾ(Regression Analysis, RA) ༉ߙᮥ}ၽ⦹ᩡŁ, ੱ⦽ࠥಽƱ॒ᨱ ᕽ ฯᯕᱢᬊࡹŁᯩ۵ᔍಡʑၹ⇵ು(Case-Based Reasoning, CBR)ᮥʑၹᮝಽ⦽༉ߙᮥ}ၽ⧉ᮝಽ៉⩥ᰆᩍÕŝᔍᬊ⠙᮹ᖒ

ᮥŁಅ⦹ᩍᱢ⧊⦽༉ߙᮥእƱ⧕ᕽᔍᬊ⧁ᙹᯩࠥಾ⦹ᩡᮝ໑

}ᅕᙹ ✚ᖒᨱ ᱢ⧊⦽ Ŗᔍእ ᔑᱶႊჶᮡ Fig. 2᪡ z݅.

}ఖŖᔍእ༉ߙᨱᕽ۵ᩢ⨆᫵ᯙ᮹❭ᦦᯕᵲ᫵⦹အಽ⫭ȡᇥ ᕾᮥ☖⧕ᔢšᖒᯕ׳ᮡᩢ⨆᫵ᯙᮥࠥ⇽⦹ᩡŁੱ⦽ᔢšᖒᮡ

ԏᮝӹᙹಽƱ᮹ℕᱢŝၡᱲ⦽šಉᯩ۵ᩢ⨆᫵ᯙॅᮥ᳑⧊⦹

ᩍᔩಽᬕᩢ⨆᫵ᯙᮝಽ⇵a⦹ᩡ݅. ੱ⦽ᩢ⨆᫵ᯙॅᮥ⢽ᵡ⪵⦹

(3)

ḡᦫᮡĞᬑ᪡݉᭥ᩢ⨆ᮥᱽÑ⦹ʑ᭥⧕⢽ᵡ⪵(Standardization)

⦽ Ğᬑෝ እƱ⦹ᩡ݅.

RA ༉ߙᨱᕽ۵⫭ȡḥ݉ᮥ☖⧕݅ᵲŖᖁᖒᯕᯩ۵ᩢ⨆᫵ᯙ

ᄡᙹෝᱽÑ⦽⬥, ⪶ᱶࡽᩢ⨆᫵ᯙᮥࠦพᄡᙹಽ⦹۵݉ᙽ⫭ȡ ᇥᕾ(Simple Regression Analysis, SRA) ༉ߙŝ݅ᵲ⫭ȡᇥᕾ (Multiple Regression Analysis, MRA) ༉ߙᮥݡᔢᮝಽᯝᱶ᳑Õ

ᮥอ᳒⦹۵ᔢšᖒᯕ׳ᮡ⬥ᅕ༉ߙᮥᖁᱶ⦹ᩡ݅. ⬥ᅕ༉ߙᨱ

ݡ⦽ᩩ⊂Ŗᔍእ᪡ᝅᱢŖᔍእᔍᯕ᮹᪅₉ᮉᮥᔑ⇽⦹Ł, ᯕ᪅₉ ᮉᮥ☖ĥᇥᕾ⦹ᩍ᪅₉ᮉŝ⢽ᵡ⠙₉a᯲ᮡ༉ߙᮥRA ༉ߙಽ

ᖁᱶ⦹ᩡ݅.

CBR ʑჶᮡ⧕đ⦹Łᯱ⦹۵ᔍಡෝᮁᔍ⦽ʑ᳕ᔍಡಽᇡ░

⇵⇽⦹ᩍ↽ᖁ⧕ෝ᨜۵ႊჶᯕ݅. ⦽⠙ᖁ⧪᮹ฯᮡ}ఖŖᔍእ

ᔑᱶ༉ߙᩑǍᨱᕽ۵ᩩ⊂Ŗᔍእ᪅₉ෝ↽ᗭ⪵⦹ʑ᭥⦹ᩍŖᔍእ

ᩢ⨆᫵ᯙᨱݡ⦽aᵲ⊹ෝ↽ᱢ⪵⦹۵ႊჶᵲ⦹ӹಽᮁᱥ᦭Ł ญ᷹(Genetic Algorithm, GA)ᮥᯕᬊ⦹Łᯩ݅. GA௡⧕Ḳ݉

(Solution Population)ᮥḥ⪵᜽⍽ษḡสʭḡᔾ᳕⦽⧕Ḳ݉ᨱᕽ

aᰆᳬᮡ⧕ෝ₟ᦥԕ۵ႊჶᮝಽᯕᩑǍᨱᕽࠥᯕෝᱢᬊ⦹ᩡ݅.

ʑ᳕᮹ࠥಽƱ}ఖŖᔍእᔑᱶႊჶᩑǍᨱᕽ۵᪅₉ᮉᮥԏ⇵

ʑ ᭥⦹ᩍ ᮁᔍࠥ ⠙₉ʑᵡŝ ᙽ᭥ʑᵡᮥ ᯝᱶ⦽ sᮝಽ ࢱŁ

ᩢ⨆᫵ᯙ᮹aᵲ⊹ෝ↽ᱢ⪵ᄡᙹಽ⦹۵äᯕᯝၹᱢᯕӹᯕᩑǍ ᨱᕽ۵ᩢ⨆᫵ᯙ᮹aᵲ⊹᫙ᨱ⠙₉ʑᵡŝᙽ᭥ʑᵡࠥ↽ᱢ⪵ᄡ ᙹᨱ⡍⧉⦹ᩡᮝ໑✚⯩ ᩢ⨆᫵ᯙ᮹aᵲ⊹ෝᱽ⦽⦹۵ႊჶᮥ

᯦ࠥ⦹ᩍ┱ᔪ⬉ᮉŝᩩ⊂᪅₉ෝ}ᖁ⦹ᩡ݅. CBR ༉ߙᮡ⠙₉ʑ ᵡ᮹ჵ᭥ෝݍญ⦹ᩍࠥ⇽ࡹ۵᪅₉ᮉᯕ↽ᱡᯙĞᬑಽ⦹ᩡ݅.

ᅙםྙᮡᙹಽƱෝƱℕ}ᅕᙹ⦹۵ߑᗭ᫵ࡹ۵}ఖᔍᨦእෝ

e݉⦽ʑᅙᱶᅕอᮥᯕᬊ⦹ᩍᔑᱶ⧁ᙹᯩࠥಾ⧉ᮝಽ៉}ᅕᙹ

ᔍᨦ᜽⧪ᨱ ঑ෙ ᝁᗮ⦽ ᮹ᔍđᱶᮥ ⦹۵ߑ ࠥᬡᮥ ᵥ äᮝಽ

ʑݡࡽ݅.

2. ᩩእᱢŁₑ

ᙹಽƱƱℕ}ᅕᙹෝ᭥⦽}ఖŖᔍእᔑᱶ༉ߙᮥ}ၽ⦹ʑ

᭥⦹ᩍRA ༉ߙŝCBR ༉ߙᨱݡ⦽↽ɝ᮹ᩑǍ࠺⨆ŝRA, CBR, GAᨱ ݡ⦽ ᯕುᱢ ႑Ğᮡ ݅ᮭŝ z݅.

RAෝᯕᬊ⦽Ʊప᮹}ఖŖᔍእᔑᱶᩑǍᨱ۵RC ௝ູƱᨱ

ݡ⦹ᩍݡ⢽Ŗ᳦ྜྷప, ⫭ȡႊᱶ᜾, ⧊ᖒ݉a, Ŗᔍእḡᙹ॒ᮥ᳑⧊

⦽༉ߙ(Kim, B. S. and Kwon, S. H., 2009)ᯕၽ⢽ࡹᨕᯩŁ, PSC BoxƱᨱݡ⦹ᩍ₉ಽᙹ, ⅾᩑᰆ, ⡎ᬱ, ⩶Ł, Ğeእ, Ğeᙹ, Ğeᰆᮥ ࠦพᄡᙹಽ ⦽ ݅ᵲ⫭ȡᇥᕾ ༉ߙ(Cho, J. H., 2009)

॒ᯕ ᯩ݅.

CBR ᯕᬊ⦽ᩑǍᨱ۵Õ⇶ྜྷ᮹ⅩʑŖᔍእෝ⇵ᱶ⦹ʑ᭥⦹

ᩍᜅ⥥౩ऽ᜽✙ʑၹ᮹GA-augmented CBR ༉ߙᯕᱽᦩࡹᨩŁ

(Doğan, S. Z. et. al., 2006), ࠥಽƱᨱ ݡ⧕ᕽ۵ ǎԕ᮹ PSC BeamƱෝݡᔢᮝಽᔍᨦ᮹ʑ⫮݉ĥᨱᕽᩑᰆ, ᔢᇡ໕ᱢ, ⡎ᬱ,

₉ಽᙹෝᩢ⨆᫵ᯙᮝಽ⦹۵༉ߙ(Kim, S. G., 2010)ᯕ}ၽࡹᨩ ᮝ໑, vၶᜅƱᨱݡ⦽ᩑᰆ, ⡎ᬱ, ᔢᇡ໕ᱢ, ₉ಽᙹෝᩢ⨆᫵ᯙᮝ ಽ⦹۵༉ߙ(CBR I)ŝᩍʑᨱaᖅ᭥⊹, ḡᩎ, ↽ݡĞeᰆ, Ğeᙹ, aᖅ⩶┽, ʑⅩ⩶᜾ᮥ⇵a⦽༉ߙ(CBR II)ᯕ}ၽࡹᨕᯩ݅(Kang, S. H., 2010).

RA᪡CBRᮥእƱ⦽ᩑǍಽ۵Ŗ࠺ᵝ┾ᨱݡ⦽ⅩʑḢᱲŖᔍ እෝᩩ⊂⦹໕ᕽCBR ᜽ᜅ▽᮹ᨱ్ᮉᯕMRA ༉ߙ᮹ᨱ్ᮉᅕ

݅ԏíӹ┡ԍ݅Łၽ⢽⦽םྙ॒ᯕᯩ݅(Kim, G. H. and Kang, K. I., 2004; Koo, C. W., 2007). Ğపᱥ℁ᱶÑᰆᨱݡ⦽}ఖŖᔍ እᔑᱶ༉ߙᩑǍᨱᕽࠥCBR ༉ߙ᮹᪅₉ᮉᯕԏᮡäᮝಽᇥᕾ⦹

ᩡ݅(Cho, N. H. et al., 2011). ⦽⠙CBR ༉ߙ᮹ᩩ⊂ᱶ⪶ࠥෝ

⨆ᔢ᜽┅ʑ᭥⧕đŝsᨱݡ⦽ᅕᱶ᜾ᮥ}ၽ⦽CBR-Revision Model(Ji, C. Y., 2008)ŝ CBR᮹ ᙹᱶ(Revise)݉ĥᨱᕽ RA

༉ߙᮥ☖⧕ᅕᱶᮥ⦹۵CBR ʑၹMRA ᅕᱶ༉ߙᯕᯩ݅(Kim, Y. S., 2010).

2.1 ฎֱंজ

⫭ȡᇥᕾᮡᄡᙹॅᔍᯕ᮹⧉ᙹšĥෝȽ໦⦹۵☖ĥᱢႊჶᮝ ಽ, ࠦพᄡᙹ(Independent Variable)a1}ᯙ݉ᙽ⫭ȡᇥᕾŝ2}

ᯕᔢᯙ݅ᵲ⫭ȡᇥᕾᮝಽǍᇥࡽ݅. ⫭ȡᇥᕾᨱᕽ۵⫭ȡ༉⩶᮹

ᮁ᮹ᖒᮥáᱶ⦹Łbࠦพᄡᙹᨱݡ⦽⫭ȡĥᙹ᮹⇵ᱶၰᮁ᮹ᖒ

áᱶᮥᝅ᜽⦹໑, ༉⩶᮹ᱢᱶᖒᮥ⪶ᅕ⦹ʑ᭥⧕ᯕᔢ⊹(outlier) ᨱݡ⦽❱݉ᮥ⦹Ł݅ᵲ⫭ȡᇥᕾ᮹Ğᬑᨱ۵݅ᵲŖᖁᖒ(Multi- collinearity)᮹ ᳕ᰍᩍᇡෝ á☁⦽݅.

ᯕᔢ⊹᮹❱ᄥႊჶᨱ۵Hat Diagonal(Leverage), Cook’s Dis- tance, Ŗᇥᔑእᮉ(Covariance Ratio), DFFITS (Difference of Fits) ॒ᯕᯩᮝ໑, Cook’s Distanceaᯝၹᱢᮝಽᯕᬊࡹ໑1ᮥ

Ⅹŝ⦹໕ ᯕᔢ⊹ಽ ❱ᱶ⦽݅.

݅ᵲŖᖁᖒᯕ௡݅ᵲ⫭ȡᇥᕾᨱᕽࠦพᄡᙹᔍᯕᨱᕽಽᔢš ᯕ׳ᮡ äᯕ ⡍⧉ࡹᨕᯩᮥ ভ۵ ᳦ᗮᄡᙹᨱၙ⊹۵ ࠦพᄡᙹ

bb᮹ᩢ⨆ᮥǍᇥ⦹ʑᨕಅᬭ⫭ȡĥᙹ᮹⇵ᱶᱶၡࠥaⓍí

ᱡ⦹ࡹ۵⩥ᔢᮥั⦽݅. ᯝၹᱢᮝಽđᱶĥᙹa80 ᯕᔢᯕࡹ໕

݅ᵲŖᖒᖒᯕ ᯩᮭᮥ ᮹ᝍ⧕ ᅕᦥ᧝ ⦽݅Ł ᦭ಅᲙ ᯩᮝ໑ ᯕ

❱ᄥႊჶᨱ۵ᇥᔑ➞₞ᯙᯱ(Variation Index Factor, VIF), Łᮁ

⊹(Eigenvalue), ᔢ┽ḡᙹ(Condition Index), ᇥᔑእᮉ(Proportion of Variation) ॒ᯕᯩ݅. VIF᮹Ğᬑ݅ᵲŖᖁᖒᯕᨧᮝ໕1ᨱ

ɝᱲ⦹Ł10ᮥⅩŝ⦹໕݅ᵲŖᖁᖒᯕᯩ݅Ł⦹໑, ᔢ┽ḡᙹ۵

10ᮥⅩŝ⦹໕᧞⦽ᱶࠥ, 100ᮥⅩŝ⦹໕v⦽݅ᵲŖᖁᖒᯕᯩ݅

Ł ❱ᱶ⦽݅(Kwon, S. H., 2008).

MRA᮹ᖁ┾ჶᨱ۵ᄡᙹ⇵aჶ(ᱥḥჶ, Forward Selection),

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Table 1. Samples of Aqueduct Bridges by Provinces

Total Gyeonggi Gangwon Chungbuk Chungnam Jeonbuk Gyeongbuk Gyeongnam

61 7 4 5 27 5 10 3

ᄡᙹᱽÑჶ(⬥ḥჶ, Backward Elimination), ᄡᙹ᷾qჶ(݉ĥᖁ

┾ჶ, Stepwise Selection), ༉ुa܆⦽⫭ȡ(All Possible Regres- sion) ॒ᯕ ᯩ݅.

2.2 ॷߢ׆ࢱ౟ߨ

ᔍಡʑၹ⇵ುᯕ௡‘ŝÑྙᱽ⧕đᨱݡ⦽Ğ⨹ᮥᔩಽᬕྙᱽᨱ

ᱢᬊ⦹ᩍə⧕đ₦ᮥᱽ᜽⦹۵᜽ᜅ▽’ᮝಽᱶ᮹ࡽ݅(Riesbeck and Schank, 1989). CBRᯕ݅ෙᯙŖḡ܆ʑჶŝǍᄥࡹ۵ᱱᮡ

ŝÑᔍಡಽᇡ░Ǎℕᱢḡ᜾ᮥᯕᬊ⦹໑, ⧕đࡽᔩಽᬕྙᱽ۵

ŝÑᔍಡಽᱡᰆࡹᨕၙ௹᮹ᩩᔢࡹ۵ྙᱽෝ⧕đ⦹۵ߑᯕᬊ

⧁ᙹᯩíࡽ݅۵ᱱᯕ݅. CBR᮹݉ĥ۵⇵⇽(Retrieve), ᰍᔍᬊ (Reuse), ᙹᱶ(Revise), ᮁḡ(Retain)᮹ ᝙ᯕⓕಽ ᯕ൉ᨕḥ݅

(Aamodt and Plaza, 1994). ᔩಽᬕྙᱽaၽᔾ⦹໕ᬑᖁᔍಡʑ ၹ(Case-base)ᮝಽᇡ░ᮁᔍᔍಡෝ⇵⇽(Retrieve)⦹Ł⇵⇽ࡽᔍ ಡ۵ྙᱽ᮹⧕ݖ(Proposed Solution)ᮝಽᱽᦩࡽ݅. ᯕভ⇵⇽ࡽ

ᔍಡ۵əݡಽᱢᬊ(Reuse)ࡹÑӹᙹᱶࢁᙹᯩ݅. ᯕ౨í⪶ᱶࡽ

⧕ݖ(Confirmed Solution)ᯕ↽᳦⧕ݖᯕࡹ໑ᯕ⧕ݖᨱݡ⦽

ᅕ᳕a⊹aᯩ݅Ł❱݉ࡹᨕᙹᱶ(Review)ࡹ໕ᱡᰆ(Retain)ࡹ

Ł ᔩಽᬕ ྙᱽ⧕đᨱ ⪽ᬊࡽ݅.

CBR ŝᱶᨱᕽᔍಡ᮹⇵⇽ᮡəᖒŝᨱⓑᩢ⨆ᮥӝ⊽݅

(Brown, C. E. and Gupta, U. G., 1994). ঑௝ᕽ⇵⇽(ੱ۵᳑⫭)ᨱ ᕽ ᱽᯝ ᵲ᫵⦽ ᫵ᗭa ᔍಡ e᮹ ᮁᔍࠥෝ ⊂ᱶ⦹۵ äᯕ݅.

ᮁᔍᔍಡෝ⇵⇽⦹۵ႊჶᨱ۵ᩍ్ႊჶᯕᯩᮝӹ↽ɝᯕᬤ⇵⇽

ႊჶ(Nearest Neighbor Retrieval)ᯕձญᔍᬊࡹŁᯩᮝ໑, ᮁᔍ ᖒ᮹ᱶࠥෝӹ┡ԕ۵⃺ࠥಽ۵ᔢݡᱢÑญ(Distance)ෝᔍᬊ⦹۵ ߑ Ñญaaʭᬙᙹಾᮁᔍࠥa⍅ḡ۵እᮁᔍᖒ⃺ࠥ(Dissimilarity Measure)ෝ ᔍᬊ⦽݅.

Ñญ᮹}ֱᮡ݅᧲⦹໑ᮁⓕญॵᦩÑญ(Euclidean Distance) a aᰆ ݡ⢽ᱢᯕŁ ᜾ (1)ಽ ⢽ʑࡽ݅.

ƂÞìß á  à áö^ćÞ à ß^{Þ àß áö^ć^ā^ƈ ^Þſ^ƈ^{à ƀ}^ƈ^ß^Ï₍₁₎

ᩍʑᕽ,۵2-norm ᯕŁſ_ƈ᪡ƀ_ƈ۵ᄂ░᪡᮹ƈჩṙ

ᬱᗭ

ᔍಡ۵ᩍ్ᗮᖒ(Attribute)ᮥaḡŁᯩᮝ໑ᗮᖒ᮹ᵲ᫵ࠥ

ᨱ ঑௝ ⇵⇽᮹ ᱶ⪶ࠥa ݍ௝ḡí ࡽ݅(Doğan et al, 2006).

ᗮᖒ᮹ᔢݡᱢᵲ᫵ࠥෝӹ┡ԕʑ᭥⦹ᩍaᵲ⊹ෝᔍᬊ⦹Łᯩᮝ ໑, CBRᨱᕽᔍᬊࡹŁᯩ۵ᗮᖒaᵲ⊹(Attribute Weight) ᔑᱶ

ႊჶᨱ۵࠺ᯝaᵲ⊹(Feature Counting), ⫭ȡᇥᕾჶ, ĥ⊖ᇥᕾŝ ᱶ(Analytic Hierarchy Process, AHP), ᮁᱥ᦭Łญ᷹॒ᯕᯩ݅.

CBRᮡMRAෝእ೐⦽GA, ANN(Artificial Neural Network), MCS(Monte Carlo Simulation) ॒݅᧲⦽ႊჶŝđ⧊ࡹᨕHybrid

༉ߙᯕ }ၽࡹ۵ ॒ ၽᱥࡹŁ ᯩ݅.

2.3 କୢੵճࠤஶ

ᮁᱥ᦭Łญ᷹ᮡCBR᮹ᗮᖒaᵲ⊹ෝ↽ᱢ⪵⦹ʑ᭥⦹ᩍᔍᬊ

ࡽ݅. ᮁᱥ᦭Łญ᷹ᮡḥ⪵᪡ᱢᯱᔾ᳕᮹ᬱญෝ༉ႊ⦽↽ᱢ⪵

ႊჶੱ۵᮹ᔍđᱶᮥ᭥⦽⧕ჶᮝಽᯕᬊࡹ໑1975֥ၙǎၙ᜽e ݡ⦺⍕⥉░ᇥ᧝John Holland Ʊᙹ᮹ᱡᕽAdaption in Natural and Artificial Systemsෝ☖⧕ၽ⢽ࡹᨩ݅. GA۵đᱶುᱢႊჶ ᮝಽ↽ᱢ⧕ෝǍ⦹ḡ༜⦹۵Ğᬑᨱ↽ᖁ᮹⧕ෝǍ⧁ᙹᯩ۵

ၽčᱢʑჶᯕ݅(Moon, B. R., 2008). ᷪ⧕᮹Ḳ⧊ᯙ༉Ḳ݉ᮥ

ᩍ్ᖙݡᨱÙℱḥ⪵᜽┅໕ᕽ༊ᱢ⧉ᙹಽ ⢽⩥ࡹ۵ᱢ⧊ࠥᨱ

ၹ᮲⦹۵ ⧕ෝ ₟۵ ႊჶᯕ݅.

ᰆᱱᮝಽ۵༊ᱢ⧉ᙹaእᖁ⩶ᯕÑӹᇩᩑᗮᯕÑӹྙᱽaࡹ

ḡᦫᮝ໑ᇡᱢ⧊⦽⧕aḡᩎ↽ᱢ(Local Optima)ᨱ዁ḡ۵äᮥ

࠭ᩑᄡᯕෝ ☖⧕ᕽ ႊḡ⧁ ᙹ ᯩ݅.

݉ᱱᮝಽ۵ᱽ᧞᳑Õᯕฯᮡ༉⩶ᨱᕽ۵⧕₟ʑaᛞḡᦫᮝ໑

ࠥ⇽ࡽ⧕a↽ᱢᯥᮥ᷾໦⧁ᙹᨧ݅۵ᱱŝੱ⦽}ℕᙹ(Popu- lation), ᄡᯕᮉ(Mutation Rate) ॒ᨱ঑௝⧕᮹₉ᯕaⓍḡอ

ᯕෝđᱶ⦹۵Ƚ⊺ᯕᨧ݅۵ᱱࠥᯩ݅(Park, C. K. and Seo, J. Y., 2009).

3. ᙹಽƱ⩥⫊ၰᇥᕾ

3.1 ୪଀ࢫਓୡվॷण

᳑ᔍᇥᕾݡᔢᮡKRCa2003֥ ~ 2012֥ʭḡƱℕ⦽ᙹಽƱ

ᵲᱥǎ61}ᗭ᮹ᔍಡᯕ໑ḡᩎᄥ⢽ᅙᙹ, ᱽᬱၰŖᔍእ⩥⫊ᮡ

bbTable 1 ~ 2᪡z݅. ᙹಽƱȽ༉ෝᇥᕾ⦽đŝ, ⠪Ɂ(↽ᗭ

~↽ݡ)ᮝಽ☖ᙹ݉໕⡎ᮡ1.21 m(0.3 ~ 3.0 m), ☖ᙹ݉໕׳ᯕ۵

0.94 m(0.5 ~ 1.7 m), Ʊb׳ᯕ۵4.02 m(1.4 ~ 9.5 m)ᯕŁ, }ᅕ ᙹŖᔍ ✚ᖒᔢ Ʊbᯕӹ Ʊݡෝ Ʊℕ⦹ḡ ᦫ۵ Ğᬑa ฯᮝ໑

Ŗᔍ᳦ඹ۵౩ၙ⎹, ℁ɝ, Ñ⣙Ḳ, vš࠺ၵญ, እĥ॒20ᩍ᳦ᯕ

ᔍᬊࡹᨩ݅.

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Table 2. Basic information of Aqueduct Bridges for Estimating Construction Cost

Aqueducts Flume Span Pier Abut Construction Cost(10⁶ KRW)

Width Height Length Number Height Number Height Number Flume Pier Abut Total OD1 1.5 1.20 107.2 11 7.0 10 1.5 2 118.0 17.5 2.5 137.92

OD7 1.4 1.10 106 11 4.8 9 1.5 2 95.2 9.7 1.3 106.22

OD11 1.2 0.90 50 5 3.1 3 1.5 2 32.9 2.2 1.3 36.32

DG1 3.0 1.70 157 19 4.1 18 ¿ ¿ 262.7 25.8 ¿ 288.52

YA1 0.9 0.85 48 8 2.0 7 ¿ ¿ 20.7 6.4 ¿ 27.02

: : : : : : : : : : : : :

MC11 1.8 1.40 72 6 ¿ ¿ ¿ ¿ 46.9 ¿ ¿ 46.92

SH2 0.7 0.65 27 3 ¿ ¿ ¿ ¿ 10.6 ¿ ¿ 10.6

MJ51 1.0 0.70 90 9 9.5 8 1.5 ¿ 63.9 20.8 0.6 85.3

*Unit of Width, Height, Length : m, KRW : Korean Rate Won

Table 3. Results of Regression Analysis of IF

Influence Factor Intercept Regression

Coefficient R square W. of Flume () -35,995,460 61,690,663 0.277 H. of Flume (¡) -22,171,895 62,868,707 0.118 L. of Restoration (¥) -10,370,482 892,404 0.657 No. of Span (§_Ƒ) 0.1178058 8,919,190 0.711 H. of Peir (¡_Ǝ) 8,150,120 10,424,191 0.101 No. of Pier (_§_Ǝ) -9,970,415 11,262,477 0.683 H. of Abut (_¡_ſ) 74,645,239 -23,869,347 0.053 No. of Abut (§_ſ) -9,865,920 23,032,588 0.024

Fig. 3. Length(¥) vs. Total Cost

3.2 ઽේ૬଴ଭট୨

Ŗᔍእᩢ⨆᫵ᯙ(Influence Factor, IF)ᮡᰍഭప, ݉a॒ŝ

zᮡᱶపᱢ᫵ᯙŝᖅ⊹ḡᩎ॒ŝzᮡᱶᖒᱢ᫵ᯙᮝಽǍᇥࡹӹ

ᙹಽƱ᮹᯦ḡᩍÕᯕÑ᮹zᦥᱶపᱢ᫵ᯙอᮥእᮉ⃺ࠥ(Ratio Scale)ಽ ᇥᕾ⦹ᩡ݅.

ᙹಽƱ۵ᙹಽᇡ(Flume), Ʊb(Pier), Ʊݡ(Abut)᮹Ǎ᳑ಽǍ ᖒࡹအಽŖᔍእࠥᯕॅǍ᳑ᄥಽǍᇥ⦹ᩡ݅. Ŗᔍእᩢ⨆᫵ᯙᮡ

Table 2᪡zᯕᯕॅǍ᳑᮹ᰍഭపŝእಡšĥaᯩ۵ᙹಽᇡ᮹

☖ᙹ݉໕⡎()ŝ׳ᯕ(¡), Ʊℕᩑᰆ(¥), Ğeᙹ(§_Ƒ), Ʊb᮹⠪

Ɂ׳ᯕ(¡_Ǝ)᪡Ʊbᙹ(§_Ǝ), Ʊݡ᮹⠪Ɂ׳ᯕ(¡_ſ)᪡Ʊݡᙹ(§_ſ)ෝ

ᖁᱶ⦹ᩡ݅.

ʑ┡, ᰍഭ݉a۵ÕᖅʑᚁᩑǍᬱ᮹ᩑࠥᄥÕᖅŖᔍእḡᙹ (Construction Cost Indices)ෝᱢᬊ⦹ᩡŁ, Ŗᔍእ۵ᙽŖᔍእෝ, ʑᅙ݉᭥۵mෝᔍᬊ⦹ᩡᮝ໑b᳦☖ĥᇥᕾᮡᝁ഑ᙹᵡ95%ෝ

ᱢᬊ⦹ᩡ݅.

3.3 ઽේ૬଴ଭฎֱंজ

⫭ȡᇥᕾᨱᦿᕽⅾ61}ᔍಡᵲᯕᔢ⊹ಽ❱݉ࡹ۵5}ᔍಡෝ

႑ᱽ⦹ᩡᮝ໑, ༉ߙá᷾ᬊ5}ᔍಡෝᱽ᫙⦽51}ᔍಡෝᇥᕾݡ ᔢᮝಽ⦹ᩡ݅. ༉ߙ᮹á᷾ᮡᔍᬊࡽᱥℕᯱഭ᮹10%ෝᔍᬊ⦹۵

äᯕ ᯝၹᱢᯕ݅(Doğan et al, 2006).

ᧂ᮹እᮉᮡᙹಽᇡ89.2%, Ʊb9.7%, Ʊݡ1.1%ಽᇥᕾࡹᨩ݅.

ᩢ⨆᫵ᯙŝŖᔍእ᮹ᔢšᖒᮥ❱݉⦹ʑ᭥⧕ᩢ⨆᫵ᯙᮥࠦพ ᄡᙹ, ⅾŖᔍእෝ᳦ᗮᄡᙹಽ⦹ᩍᝁ഑ࠥ95%᮹⫭ȡᇥᕾᮥ⦹ᩡ

݅. đᱶĥᙹ(R²)aๅᬑԏᮡᩢ⨆᫵ᯙᮥᱽ᫙⦹Ł, R²a0.65ᯕᔢ

ࡹ۵Ʊℕᩑᰆ(¥), Ğeᙹ(§Ƒ), Ʊbᙹ(§Ǝ)ෝᖁᱶ⦹ᩡ݅. ᯕđŝ ۵ Table 3 ၰ Fig. 3~Fig. 5᪡ z݅.

۵ᩢ⨆᫵ᯙ᮹ᄡᙹ᳑⧊ᮥᔩಽᬕŖᔍእᩢ⨆᫵ᯙᮝಽǍᖒ⦹ᩍ

ᱽᦩ⦹ᩡ݅. ᳑⧊ᄡᙹ(Combination Variable, CV)ಽᕽᙹಽᇡ۵

Þ âÏ¡ß¥, Ʊbᮡ¡_Ǝ읆§_Ǝ, Ʊݡ۵¡_ſ읆§_ſෝᔍᬊ⦹ᩡᮝ໑, ᳑⧊

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Fig. 4. No. of Span(_§_Ƒ) vs. Total Cost Fig. 5. No. of Pier(_§_Ǝ) vs. Total Cost

Table 4. Results of Regression Analysis of CV

Classification Variables Intercept Regression

Coefficient R square Cost of

Super/Sub structure

ÞâÏ¡ß¥ -10,758,039 246,005 0.896

¡_Ǝ읆§_Ǝ 42,747,739 49,585 0.286

¡_ſ읆§_ſ 1,216,092 53,941 0.005

Total Cost

ÞâÏ¡ß¥ -10,650,130 268,393 0.898

¡_Ǝ읆§_Ǝ 39,278,510 190,075 0.070

¡_ſ읆§_ſ 36,232,196 -296,225 0.0002

Fig. 6.ÞâÏ¡ß¥ vs. Flume Cost

Table 5. IF and Construction Cost of Cases for Verification Aqueducts §_Ƒ §_Ǝ ÞâÏ¡ß¥ ¡_Ǝ읆§_Ǝ Total Cost

YA2 4 3 98.80 6 18,625,870 BD7-2 9 8 324.00 15 61,686,042 GD47 28 0¿ 467.50 0 105,234,192 CG4 3 0¿ 64.80 0 12,726,126 MJ134 3 2 72.00 10 11,390,220 ᄡᙹ᮹Ŗᔍእᨱݡ⦽ᔢššĥෝᇥᕾ⦽đŝ۵Table 4 ၰFig.

6ŝ z݅.

Table 4᪡zᯕᙹಽᇡ᮹ᄡᙹ᳑⧊ᮡᔢšᖒᯕ׳ᮝӹƱbŝ

Ʊݡ᮹ᄡᙹ᳑⧊ᮡԏᮡäᮝಽӹ┡ԍ݅. ঑௝ᕽᙹಽᇡ, Ʊb, Ʊݡbb᮹Ŗᔍእෝ⧊⦹ᩍⅾŖᔍእෝᩩ⊂⦹۵ႊჶᮡᇡᱢ⧊

⦽äᮝಽ❱݉ࡹᨩ݅. ݅อƱbŖᔍእaⅾŖᔍእ᮹᧞10%ෝ

₉ḡ⦹Ł ᯩᮝအಽ¡_Ǝ읆§_Ǝෝ Ŗᔍእ ᩢ⨆᫵ᯙᨱ ⡍⧉⦹ᩡ݅.

঑௝ᕽᙹಽƱ}ఖŖᔍእᔑᱶᮥ᭥⦽ᩢ⨆᫵ᯙᮝಽƱℕᩑ ᰆ(¥), Ğeᙹ(§_Ƒ), Ʊbᙹ(§_Ǝ), _{Þ âÏ¡ß¥}, _¡_Ǝ_읆§_Ǝ᮹5}ෝᖁ ᱶ⦹ᩡᮝ໑ ᯕᨱ ݡ⦽ ⫭ȡḥ݉ᮥ ᝅ᜽⦹ᩡ݅.

4. }ఖŖᔍእᔑᱶ༉ߙ

4.1 ฎֱंজࡦ܄

4.1.1 ฎֱ஼ۚ

⫭ȡḥ݉ᮡ ݅ᵲ⫭ȡᇥᕾ᮹ᄡᙹᱽÑჶᮥ ᯕᬊ⦹ᩍᯕᔢ⊹᪡

݅ᵲŖᖁᖒᮥᇥᕾ⦹ᩡ݅. ᯕᇥᕾᮡ EXCEL ʑၹ᮹KESS(Korean Educational Statistics Software, stat.snu.ac.kr/time)ෝᯕᬊ

⦹ᩡᮝ໑ə đŝaჵᬊ⥥ಽəఉᯙSPSS᪡࠺ᯝ⧉ᮥ⪶ᯙ⦹

ᩡ݅.

⫭ȡḥ݉đŝ, Table 2᮹DG1ᔍಡaᯕᔢ⊹(Cook’s Distance

= 9.2884ⴏ 1, Leverage = 0.8259 ⴏ 2(p + 1)/n = 2(5+10)/51

= 0.235, p = ࠦพᄡᙹ᮹ ᙹ, n = ⢽ᅙᙹ)ಽ ❱݉ࡹᨕ ᯕෝ

ᱽ᫙⧉ᮝಽ៉50}ᔍಡ(5}á᷾ᔍಡᄥࠥ)ෝ⫭ȡᇥᕾݡᔢᮝ ಽ⦹ᩡ݅. 5}ࠦพᄡᙹᔍᯕ᮹݅ᵲŖᖁᖒᮡƱℕᩑᰆ(¥)᮹

VIFa81.1745ಽ10ᮥⓍíⅩŝ⦹ᩍ݅ᵲŖᖁᖒᯕᯩ۵äᮝಽ

❱݉ࡹᨕࠦพᄡᙹᨱᕽᱽ᫙⦹ᩡᮝ໑ᯕᨱ঑௝⫭ȡᇥᕾ᮹ࠦพ ᄡᙹෝĞeᙹ(§_Ƒ), Ʊbᙹ(§_Ǝ), _{Þ âÏ¡ß¥}, _¡_Ǝ_읆§_Ǝ᮹4}ಽ

↽᳦ đᱶ⦹ᩡ݅.

4.1.2 ฎֱंজࡦ܄ଭૈఙଘ

⫭ȡᇥᕾ༉ߙ(RA)᮹ᱢ⧊ᖒᮥᇥᕾ⦹ʑ᭥⧕ᕽRA ༉ߙ᮹

ᩩ⊂Ŗᔍእෝ Table 5᮹ á᷾ᔍಡ ᝅᱢŖᔍእ᪡ እƱ⦹ᩡ݅.

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Table 6. Standardized IF and Cost of Cases for Verification Aqueduct §_Ƒ §_Ǝ ÞâÏ¡ß¥ ¡_Ǝ읆§_Ǝ Total Cost

YA2 -0.374 -0.494 -0.564 -0.714 -0.477 BD7-2 0.766 0.998 1.113 -0.201 0.733 GD47 5.096 -1.389 2.181 -1.056 1.956 CG4 -0.602 -1.389 -0.817 -1.056 -0.642 MJ134 -0.602 -0.792 -0.764 -0.486 -0.680

Table 7. Error Rate of Linear Regressin Models

Model Variable Regression Coefficient Aqueduct Estimated Cost (KRW) Actual Cost (KRW) Error Rate (%)

1

«^Ï: 0.920

Constant -6.74E+06 YA2 13,290,789 18,625,870 28.6

BD7-2 46,237,021 61,686,042 25.0

§_Ƒ 3.74E+06

GD47 152,560,465 105,234,192 45.0

§_Ǝ -4.66E+06

CG4 12,039,083 12,726,126 5.4

ÞâÏ¡ß¥ 1.16E+05

MJ134 16,092,201 11,390,220 41.3

¡_Ǝ읆§_Ǝ 1.25E+06 Average 29.1

2

«^Ï: 0.886

Constant -5.22E+06 YA2 11,905,604 18,625,870 36.1

BD7-2 53,456,466 61,686,042 13.3

§_Ǝ -4.00E+06 GD47 103,256,066 105,234,192 1.9

CG4 9,817,201 12,726,126 22.9

ÞâÏ¡ß¥ 2.32E+05

MJ134 13,829,289 11,390,220 21.4

¡_Ǝ읆§_Ǝ 1.03E+06 Average 19.1

3

«^Ï: 0.866

Constant -7.93E+06 YA2 19,018,575 18,625,870 2.1

BD7-2 66,334,941 61,686,042 7.5

§_Ƒ 2.91E+06 GD47 133,128,788 105,234,192 26.5

CG4 9,053,471 12,726,126 28.9

ÞâÏ¡ß¥ 1.27E+05

MJ134 14,510,873 11,390,220 27.4

¡_Ǝ읆§_Ǝ 4.54E+05 Average 18.5

4

«^Ï: 0.845

Constant -6.58E+06 YA2 17,261,117 18,625,870 7.3

BD7-2 69,866,445 61,686,042 13.3

ÞâÏ¡ß¥ 2.19E+05 GD47 95,734,836 105,234,192 9.0

CG4 7,606,065 12,726,126 40.2

¡_Ǝ읆§_Ǝ 3.69E+05 MJ134 12,872,299 11,390,220 13.0

Average 16.6

5

«^Ï: 0.823

Constant -6.40E+06

YA2 17,366,799 18,625,870 6.8

BD7-2 71,535,475 61,686,042 16.0 GD47 106,052,371 105,234,192 0.8

ÞâÏ¡ß¥ 2.41E+05

CG4 9,188,580 12,726,126 27.8 MJ134 10,920,438 11,390,220 4.1

Average 11.1

RA ༉ߙ᮹Ğᬑᙹ۵ᩢ⨆᫵ᯙᷪࠦพᄡᙹ᮹}ᙹa4}ᯕအಽ

ᯕॅ᮹ ᳑⧊ᮡ 15}a a܆⦹ӹ ᯕ ᵲ ⴗ ࠦพᄡᙹ᮹ }ᙹa

1, 2, 3, 4ᯙ᳑⧊ᨱᕽ1}ᦊᮡ⡍⧉⦹Łⴘᙹಽᇡෝӹ┡ԕ۵ Þ âÏ¡ß¥ᮥ ⡍⧉⦹໑ ⴙ ᔢʑ ࢱ ᳑Õᮥ ༉ࢱ อ᳒⦹۵ ä

ᵲđᱶĥᙹaⓑ5}ෝᖁᱶ⦹ᩡ݅. ⫭ȡᇥᕾᮡᝁ഑ᙹᵡ95%ಽ

⫭ȡ᜾ᨱݡ⦽Fá᷾ŝ⫭ȡĥᙹ(ᱩ⠙ŝʑᬙʑ)ᨱݡ⦽tá᷾ᮥ

⦹ᩍᮁ᮹ᖒᮥ⪶ᯙ⦹ᩡ݅. RA ༉ߙ᮹ᱢ⧊ᖒᮡᱩݡsᮝಽ⦽

᪅₉ᮉ᮹ Ⓧʑ᪡ ᇥ⡍ෝ እƱ⦹ᩍ ᇥᕾ⦹ᩡ݅.

ੱ⦽ᩢ⨆᫵ᯙ᮹ᯱഭsᮥᬱsəݡಽᔍᬊ⦽Ğᬑ᪡݉᭥

ᩢ⨆ᮥᱽÑ⦹ʑ᭥⧕Table 6ŝzᯕᯱഭsᮥ(ᯱഭs-⠪Ɂ)/⢽ᵡ

⠙₉ಽ⦹۵⢽ᵡ⪵⦽ĞᬑෝእƱ⦹ᩡᮝ໑əđŝ۵ bbTable 7, Table 8ŝ z݅.

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Table 8. Error Rate of Standardized Linear Regressin Models

Model Variable Regression Coefficient Aqueduct Estimated Cost (KRW) Actual Cost (KRW) Error Rate (%)

1S

«^Ï: 0.919

Constant 0.0 YA2 12,671,083 18,625,870 32.0

BD7-2 45,777,476 61,686,042 25.8

§_Ƒ 0.45117

GD47 151,861,366 105,234,192 44.3

§_Ǝ -0.445540

CG4 11,619,966 12,726,126 8.7

ÞâÏ¡ß¥ 0.45660

MJ134 15,475,732 11,390,220 35.9

¡_Ǝ읆§_Ǝ 0.61278 Average 29.6

2S

«^Ï: 0.887

Constant 0.0 YA2 11,199,022 18,625,870 39.9

BD7-2 53,992,720 61,686,042 12.5

§Ǝ -0.33490 GD47 99,418,402 105,234,192 5.5

CG4 7,882,128 12,726,126 38.1

ÞâÏ¡ß¥ 0.85748

MJ134 12,836,665 11,390,220 12.7

¡_Ǝ읆§_Ǝ 0.51364 Average 21.7

3S

«^Ï: 0.879

Constant 0.0 YA2 13,180,429 18,625,870 29.2

BD7-2 61,897,073 61,686,042 0.3

§_Ƒ 0.31169 GD47 119,534,713 105,234,192 13.6

CG4 2,208,254 12,726,126 82.6

ÞâÏ¡ß¥ 0.50692

MJ134 9,633,818 11,390,220 15.4

¡_Ǝ읆§_Ǝ 0.31799 Average 28.2

4S

«^Ï: 0.862

Constant 0.0 YA2 11,984,415 18,625,870 35.7

BD7-2 65,051,709 61,686,042 5.5

ÞâÏ¡ß¥ 0.79753 GD47 86,295,552 105,234,192 18.0

CG4 1,158,589 12,726,126 90.9

¡_Ǝ읆§_Ǝ 0.29853 MJ134 8,743,620 11,390,220 23.2

Average 34.6

5S

«^Ï: 0.823

Constant 0.0

YA2 17,366,799 18,625,870 6.8

BD7-2 71,535,475 61,686,042 16.0

GD47 106,052,371 105,234,192 0.8

ÞâÏ¡ß¥ 0.90738

CG4 9,188,580 12,726,126 27.8

MJ134 10,920,438 11,390,220 4.1

Average 11.1

4.1.3 ฎֱंজࡦ܄ंজ

Table 7᮹5aḡRA ༉ߙᮥእƱ⧕ᅕ໕, ࠦพᄡᙹa1}ᯙ

Ğᬑ(SRA)᮹đᱶĥᙹ(R²)۵0.823ᮝಽᩍ్}ᯙĞᬑ(MRA)᮹

đᱶĥᙹ0.845~0.920ᨱእ⧕᯲ᦥࠦพᄡᙹa᳦ᗮᄡᙹᨱၙ⊹۵

ᱥℕᩢ⨆ಆᯕ᯲ᮝӹ, ᪅₉ᮉᮡ⠪Ɂ20.8%ᨱᕽ11.1%ಽqᗭࡹ

ᨩŁ ⢽ᵡ⠙₉ࠥ 12.6 ~ 15.7ᨱᕽ 10.9ಽ qᗭࡹᨩ݅.

SRA᪡MRA ə൚ᮝಽӹ٥ᨕbᔍಡ(⢽ᅙ5}᪡⢽ᅙ20}) ᨱݡ⦽ᇥᔑᇥᕾᮥ⦽đŝ₉ᯕෝᯙᱶ⧁ᙹᨧ۵äᮝಽӹ┡ԍ݅.

ə్ӹ Table 8᮹ ⢽ᵡ⪵⦽ Ğᬑෝ ⡍⧉⦹ᩍ SRA ə൚(⢽ᅙ

10})ŝ MRA ə൚(⢽ᅙ 40})ᨱ ݡ⦽ ᇥᔑᇥᕾᮥ ⦽đŝ,

⠪Ɂᮡ11.1ŝ26.7, ⢽ᵡ⠙₉۵10.29᪡ 19.45ಽӹ┡ԉᮝಽ

៉ࢱə൚ ᔍᯕᨱ₉ᯕ(ᮁ᮹⪶ශp = 0.039)aᯩ۵äᮝಽ

ӹ┡ԍ݅.

ᯕ్⦽đŝ۵ᙹಽᇡ᮹Ŗᔍእa⠪Ɂ90% ᱶࠥෝ₉ḡ⦹Ł

ᯩᨕŖᔍእእᵲᯕԏᮡƱbᯕ⡍⧉ࡽĞᬑ᪅₉ᮉᯕ⍅ḡʑ

ভྙᯙ äᮝಽ ❱݉ࡽ݅.

঑௝ᕽ}ఖŖᔍእᔑᱶᮥ᭥⦽RA ༉ߙᮡࠦพᄡᙹෝÞ â Ï¡ß¥᮹1}ಽ⦹۵SRA ༉ߙᯕᯕᱢ⧊⦽äᮝಽ❱݉ࡹᨩᮝ

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Fig. 7. Process of CBR for Approximate Cost Estimate ໑ə᜾ᮡ² áà ÓíÑ×lÓâÏíÑÎlÒZ Þ âÏ¡ß¥, (݉᭥ : m, ᬱ) ᯕ݅. ᯕ ༉ߙᮡ e݉⦽ ᔑ⇽᜾ᯕḡอ ƱbŖᔍእෝ ၹᩢ⧁ ᙹ

ᨧ۵ ݉ᱱᯕ ᯩ݅.

⦽⠙⢽ᵡ⪵⦽Ğᬑ᪡ᬱsᮥəݡಽᔍᬊ⦹۵Ğᬑ(እ⢽ᵡ⪵

Ğᬑ) ᔍᯕ᮹᪅₉ᮉ₉ᯕෝእƱ⦹ʑ᭥⧕༉ߙᄥ⠪Ɂ᪅₉ᮉᨱ

ݡ⧕ ᇥᔑᇥᕾᮥ ⦽ đŝ ₉ᯕa ᨧ۵ äᮝಽ ᇥᕾࡹᨩ݅.

4.2 ॷߢ׆ࢱ౟ߨࡦ܄

4.2.1 ॷߢ׆ࢱ౟ߨ(CBR) ׆࣑ඹߦপਆ

ᙹಽƱᔍಡʑၹ᮹ʑᅙǍᖒᮡƱℕᩑᰆ(¥), Ğeᙹ(§_Ƒ), Ʊb ᙹ(§Ǝ), _{Þ âÏ¡ß¥}, _¡_Ǝ_읆§_Ǝ᮹5}ᩢ⨆᫵ᯙŝⅾŖᔍእ᮹6}

ᗮᖒsᮝಽࡽ᜾(2)ಽ⢽⩥ࡹ໑, ⅾŖᔍእ۵⧕ݚŖᔍ᜽ʑᨱ

঑௝ Ŗᔍእḡᙹෝ ၹᩢ⦹ᩍ ᅕᱶ⦹ᩡ݅.

ᅙםྙᨱᕽ۵ᔍಡʑၹᮥʑ᳕ᔍಡ50}ᗭ᪡á᷾ᔍಡ5}ᗭ ಽ Ǎᖒ⦹ᩡ݅.

Ƈჩṙ ᔍಡjƇáåſƇƈì ƒ_Ƈæ (2)

ᩍʑᕽ, ſƇƈ:_Ƈჩṙ ᔍಡ᮹ƈჩṙ ᩢ⨆᫵ᯙ᮹ s, ƈ= 1 ~ 5 ƒ_Ƈ:_Ƈჩṙ ᔍಡ᮹ ⅾŖᔍእ

ᙹಽƱ᮹}ఖŖᔍእᔑᱶᮥ᭥⦽CBR ʑჶ⥥ಽᖙᜅ۵Fig.

7ŝ z݅

ℌṙ, ʑ᳕ᔍಡᗮᖒs(Attribute) ᱶᅕ᪡ᝁȽᔍಡᱶᅕෝ᯦ಆ

⦽݅.

ࢹṙ, ᮁⓕญॵᦩÑญෝᯕᬊ⦽ᩢ⨆᫵ᯙᄥᮁᔍࠥ(Attribute Similarity)۵ ᜾ (3), (4)᪡ zᯕ ᔑᱶࡽ݅.

Ƈჩṙ ᔍಡ᮹ƈჩṙ ᗮᖒᮁᔍࠥƑƇƈ

ç

^ć^ſ^lƇƈ^ſ^{à ſ}^uƈ ^uƈ

ç

^{= Ƃ}^ƈ ^{ᯝ ভ,} ^Ƒ^Ƈƈ^{áÎ à}

ç

(3)

ç

^{ð Ƃ}^ƈ ^{ᯝ ভ,} ^Ƒ^Ƈƈ^á 0 (4) ᩍʑᕽ, ſlƇƈ : ʑ᳕ᔍಡƇჩṙ᮹ƈჩṙ ᩢ⨆᫵ᯙ᮹ s _ſ_uƈ : ᝁȽᔍಡƈჩṙ ᩢ⨆᫵ᯙ᮹ s _Ƃ_ƈ : _ƈჩṙ ᩢ⨆᫵ᯙ᮹ ⠙₉ʑᵡ

ᝁȽᔍಡ᪡⠙₉aᯩ۵ʑ᳕ᔍಡෝᨕ۱ჵ᭥ʭḡᖁ┾⧁äᯙ aaๅᬑᵲ᫵⦹໑, ࠥಽƱ᮹Ğᬑᨱᕽ۵ᅕ☖ᩢ⨆᫵ᯙ᮹⠙₉ʑ ᵡƂ_ƈෝŁᱶࡽsᮥᔍᬊ⦹ᩡᮝӹ, ᯕᩑǍᨱᕽ۵⠙₉ʑᵡᮥ↽ᱢ

⪵ݡᔢᮝಽᔝᦥGAෝᯕᬊ⦹ᩍ↽ᱢsᮥ₟ᦥԕ۵ႊჶᮥᱢᬊ⦹

ᩡ݅.

ᖬṙ, ᔍಡᮁᔍࠥ(Case Similarity)۵᜾(5)᪡zᯕĥᔑࡽ݅.

Ƈჩṙᔍಡ᮹ᔍಡᮁᔍࠥ Ƒ_Ƈáā_ƈ ^ƕ^ƈ^Ƒ^Ƈƈ (5) ᩍʑᕽ, ƕƈ : _ƈჩṙ ᩢ⨆᫵ᯙ᮹ aᵲ⊹

ā_ƈ ^ƕ^ƈ^{á Î}

ᩢ⨆᫵ᯙ᮹aᵲ⊹ƕƈ۵GAෝᯕᬊ⦽↽ᱢ⪵ŝᱶᨱᕽǍ⧕ḡ ໑ ə ⅾ⧊ᮡ 1ᯕ ࡽ݅.

ᩩ⊂Ŗᔍእ۵ᮁᔍ⦽ᔍಡෝ₟ᮡ⬥ᮁᔍࠥᙽ᭥ᨱ঑௝ᯝᱶ

}ᙹ᮹ᮁᔍᔍಡෝᖁᱶ⦹ᩍᱢᱶ⦹íĥᔑ⦹íࡹ۵ߑᯕᩑǍᨱ ᕽ۵ᮁᔍᔍಡᙹ(ᙽ᭥ʑᵡ)ࠥ↽ᱢ⪵ݡᔢᨱ⡍⧉⦹ᩡ݅. ᷪ, ᝅᱢ Ŗᔍእ᪡ᩩ⊂Ŗᔍእ᮹᪅₉a↽ᗭaࡹࠥಾᮁᔍᔍಡᙽ᭥ʑᵡ

ᮥ ↽ᱢ⪵⦹ᩡ݅.

օṙ, ᔍಡaᵲ⊹(Case Weight)۵ ᜾ (6)ŝ zᯕ ĥᔑࡽ݅.

Ɖჩṙ ᔍಡ᮹ ᔍಡaᵲ⊹ƅ_Ɖá ć¬ ƑƉ

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ᩍʑᕽ, Ɖ : ᮁᔍࠥ ᙽ᭥

_¬ : ᮁᔍᔍಡ ᙽ᭥ʑᵡ ԕ᮹ ᔍಡᮁᔍࠥ᮹ ⅾ⧊

¬ áā^ƌ_Ɖ^Ɖ ^Ƒ^Ɖ

ƌ_Ɖ : ᮁᔍᔍಡ ᙽ᭥ʑᵡ

݅ᖐṙ, ᩩ⊂Ŗᔍእ۵ᮁᔍᔍಡᙽ᭥ʑᵡԕ᮹ᮁᔍᔍಡŖᔍእ

ƒƉᨱᔍಡaᵲ⊹ෝŒ⦽sᮥ⧊ᔑ⦹ᩍ᜾(7)ŝzᯕĥᔑࡽ݅.

ᩩ⊂Ŗᔍእ áā^ƌ_Ɖ^Ɖ ^ƅ^Ɖ^ƒ^Ɖ (7)

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Fig. 8. Process of Optimization using GA

Fig. 9. An Example of Optimized Output 4.2.2 କୢੵճࠤஶ(GA) ଲ૳ౖୡฃ

CBRʑჶ⥥ಽᖙᜅᨱᕽᩩ⊂Ŗᔍእ᮹᪅₉a↽ᗭaࡹࠥಾ

GAෝᯕᬊ⦽↽ᱢ⪵ႊჶᮥ᯦ࠥ⦹ᩡᮝ໑əݡᔢᮡᩢ⨆᫵ᯙ᮹

aᵲ⊹ƕ_ƈ,ᩢ⨆᫵ᯙ᮹⠙₉ʑᵡƂ_ƈ,ᮁᔍᔍಡᙽ᭥ʑᵡƌ_Ɖᯕ݅.

ੱ⦽↽ᖁ⧕᮹┱ᔪ⬉ᮉᮥ׳ᯕŁ᪅₉ෝᵥᯕʑ᭥⦹ᩍᩢ⨆

᫵ᯙᄥaᵲ⊹᮹ᱽ⦽ჵ᭥ෝᖅᱶ⦹ᩡ݅. ᩢ⨆᫵ᯙᵲÞ âÏ¡ß¥

۵ᙹಽᇡŖᔍእ᪡,¡Ǝ읆§Ǝ۵ƱbŖᔍእ᪡ၡᱲ⦽šĥaᯩᮝအ ಽ ᯕॅ Ŗᔍእa ₉ḡ⦹۵ እᮉᮥ ᩢ⨆᫵ᯙ᮹ aᵲ⊹ ჵ᭥a

ࡹࠥಾ⦹ᩡ݅. ᷪ, bᔍಡ᮹ᝅᱢŖᔍእᨱݡ⦽ᙹಽᇡŖᔍእ᪡

ƱbŖᔍእ᮹እᮉಽᇡ░ᝁ഑ࠥ99.74% ᯕᔢᯕࡹ۵↽ᗭᙹಽᇡ Ŗᔍእ᪡↽ݡƱbŖᔍእእᮉᮥǍ⦹ᩍᯕእᮉᮥᩢ⨆᫵ᯙ᮹

aᵲ⊹᮹ჵ᭥ෝᖅᱶ⦹۵ߑᱢᬊ⦹ᩡ݅. ᯕᨱ঑௝Þ âÏ¡ß¥۵

58% ᯕᔢ, ¡Ǝ읆§Ǝ۵ 38% ᯕ⦹a ࡹࠥಾ ᱽ⦽ࡹᨩ݅.

GA᮹↽ᱢ⪵ŝᱶᮡFig. 8ŝzᮝ໑, 50}ᔍಡᵲ45}ෝ

ᔍಡʑၹᮝಽ⦹Ł5}ෝ⦺᜖ᔍಡಽ⦹ᩍᩩ⊂Ŗᔍእ᪡ᝅᱢŖᔍ እ᮹ ᪅₉ᮉᯕ ↽ᗭಽ ࡹࠥಾ ⦹ᩡ݅.

⦽⠙, GAෝᯕᬊ⦽↽ᱢ⪵⥥ಽəఉᮝಽEXCEL 2010ᮥᔍᬊ

⦹ᩡᮝ໑, ᱽ⦽᳑Õ(Option)ᮝಽ ⧕Ḳ݉ ᙹ۵ 150, ᄡᯕᮉᮡ

0.075ಽ ⦹ᩡŁ(Hiller, F. S. and Hiller, M. S., 2004) ə᫙᮹

GA᮹᪖ᖹᮡᙹಕࠥ0.0001, }ᖁᮥ⡍⧉⦹ḡᦫ۵᜽eᮡ300Ⅹ ಽ ⦹ᩡ݅.

4.2.3 ॷߢ׆ࢱ౟ߨࡦ܄ଭૈఙଘ

CBR ༉ߙ᮹ ᇥᕾႊჶᮡ ↽ᱢ⪵᮹ ݡᔢŝ ᱽ⦽᳑Õ, ⢽ᵡ⪵

ᩍᇡ॒ᨱ঑௝ฯᮡĞᬑaᯩᮥᙹᯩ݅. GAෝᯕᬊ⦽PSC BeamƱᨱᕽ۵ᮁᔍࠥ⠙₉ʑᵡᮥ5, 10, 15, 20%᮹ჵ᭥ಽ, ᙽ᭥

ʑᵡᮥ1 ~ 5ᙽ᭥ʭḡಽᖅᱶ⦹Ł↽ᱢ᮹⠙₉᪡ᙽ᭥ෝᖁᱶ⦹

ᩡᮝ໑(Kim, G. J. et al., 2011), vၶᜅƱᨱᕽ۵⠙₉10%, ᙽ᭥

3%ෝᱢᬊ⦹ᩡ݅(Kang, S. H., 2010). ᯕᩑǍᨱᕽ۵ᖁ⧪ᩑǍ đŝ᪡ ᙹಽƱᨱ ݡ⦽ ᩩእᇥᕾᮥ ☖⧕ ݅ᮭŝ zᮡ ႊჶᮝಽ

ᙹ⧪⦹ᩡ݅.

ℌṙ, ᙹಽƱᨱᕽ۵ᩢ⨆᫵ᯙ᮹aᵲ⊹᫙ᨱ⠙₉ʑᵡၰᙽ᭥ʑ ᵡᮥ↽ᱢ⪵ݡᔢᮝಽ⦹ᩡᮝ໑✚⯩⠙₉ʑᵡᮡᩢ⨆᫵ᯙsॅ

e᮹እᮉᯕ↽ᗭ27.5 ႑(Ʊℕᩑᰆ) ~ ↽ݡ52.5 ႑[Þ âÏ¡ß¥] ಽๅᬑ⍅ᕽᮁᔍࠥ20% ᯕ⦹ෝอ᳒⦹۵ᔍಡaÑ᮹ᨧᮝအಽ

⠙₉ʑᵡᮥ100%(እƱݡᔢe2႑₉ᯕ) ᯕ⦹ಽմ⩡ᕽᖅᱶ⦹۵

ႊჶᮥ ᱢᬊ⦹ᩡ݅.

ࢹṙ, ᩢ⨆᫵ᯙ᮹aᵲ⊹ჵ᭥ෝᱽ⦽⦹۵ႊჶᮥᱢᬊ⦹ᩡᮝ ໑⠙₉ʑᵡᮡTable 9᪡zᯕ0 ~ 10%, 0 ~ 20%, zᮡႊჶᮝಽ

30, 40, 50, 60, 100%᪡↽ᗭ᪅₉ᮉᔢ⦹± 5%ᯙࢱ᳑Õᮥ

⧊ℱ 9} ᳑Õ᮹ ᪅₉ᮉᮥ Ǎ⦹ᩡ݅.

á᷾ᔍಡ۵RA ༉ߙŝ࠺ᯝݡᔢᮝಽ⦹ᩡ݅. ↽ᱢ⪵ෝᙹ⧪⦹

ᩍǍ⦽ᩢ⨆᫵ᯙaᵲ⊹, ⠙₉ʑᵡ, ᙽ᭥ʑᵡŝəᨱ঑ෙŖᔍእ

(11)

Table 9. Error Rate by of Construction Cost Estimate by Maximum Deviation

Stat Maximum Deviation

10% 20% 30% 35% 40% 45% 50% 60% 100%

Aver (Training) 5.21 4.67 4.85 4.54 4.12 3.00 2.12 2.11 2.50 Aver (Testing) 27.46 9.23 13.88 13.06 7.19 9.13 11.96 12.01 11.58

Min. Error 1.97 3.23 9.18 9.86 0.96 3.62 1.32 1.58 0.27

Max. Error 74.46 15.11 20.20 16.44 21.36 16.16 18.45 18.56 19.46

Rank 2 8 5 5 6 5 6 6 6

*20% : 0% ~ 20%, 30% : 0% ~ 30%

*Rank : number of similar cases

Table 10. Comparison of Error Rates by Optimization Conditions

Statistics Maximum Deviation under Limitation of Weight Maximum Deviation under Unlimitedness of Weight

30% 40% 50% 30% 40% 50%

Average 13.88 7.19 11.96 25.73 9.12 14.45

Minimum 9.18 0.96 1.32 0.43 0.05 0.81

Maximum 20.20 21.36 18.45 64.40 22.33 31.07

Table 11. Error Rates of Estimated Cost by Standardized CBR Model

Statistics Maximum Deviation

10% 20% 30% 40% 45% 50% 55% 60% 100%

Average(Training)

Unsolved

5.88 6.54 12.82 7.00 8.32 6.30 6.45 6.29

Average(Testing) 31.98 16.93 12.22 15.80 7.12 10.38 11.97 9.28

Minimum 8.53 10.71 2.78 4.07 1.38 0.73 8.73 0.78

Maximum 90.52 30.13 30.10 30.04 17.59 16.10 16.10 14.81

Rank 8 8 4 5 5 5 5 5

᪅₉ᮉ đŝෝ Fig. 9ᨱ ᯝಡಽ ӹ┡ԕᨩ݅.

CBR ༉ߙ᮹Ŗᔍእ᪅₉ᮉᮡTable 9᪡zᯕ⠙₉ʑᵡ40%ᯙ

᳑Õᨱᕽaᰆԏᦹᮝ໑Fig. 9᪡zᯕ5}á᷾ᔍಡᨱݡ⦽᪅₉ᮉ

ᮡ ⠪Ɂ 7.19%ᯕᨩ݅.

ᖬṙ, ᩢ⨆᫵ᯙ᮹aᵲ⊹ჵ᭥ෝᱽ⦽⦹ḡᦫᮡႊჶᨱݡ⦽

᪅₉ᮉᮡTable 10ŝzᯕ⠙₉ʑᵡ40% ᯝভ⠪Ɂ9.12%ಽ

ӹ┡ԍ݅. aᵲ⊹ჵ᭥ෝᱽ⦽⦹۵ႊჶŝእƱ⧕ᅕ໕40% ᯕ᫙᮹

Ğᬑᨱࠥ᪅₉ᮉᯕⓍíӹ┡ԍᮝ໑ᯕ۵aᵲ⊹ჵ᭥ෝᱽ⦽⦽

đŝಽ ❱݉ࡽ݅.

֘ṙ, ᩢ⨆᫵ᯙ᮹݉᭥ᩢ⨆ᮥᱽÑ⦹ʑ᭥⧕ᯱഭsᮥ⢽ᵡ⪵⦹

ᩍǍ⦽᪅₉ᮉᮡTable 11ŝzᮝ໑⠙₉ʑᵡ50%ᯙ᳑Õᨱᕽ

⠪Ɂ 7.12%ಽ ↽ᗭa ࡹᨩ݅.

↽ᗭ᪅₉ᮉอᮥእƱ⦹໕⢽ᵡ⪵⦽ႊჶ᮹7.12%a⢽ᵡ⪵⦹

ḡᦫᮡႊჶ᮹7.19%ᨱእ⧕᯲ᮝӹ, ⠙₉ʑᵡ20% ~ 100%᮹

8aḡᱥℕෝݡᔢᮝಽ᪅₉ᮉ᮹₉ᯕෝእƱ⧕ᅕʑ᭥⧕ᇥᔑᇥᕾ

ᮥ⦽đŝ95% ᝁ഑ᙹᵡᨱᕽᮁ᮹ᖒᯕᨧ۵äᮝಽӹ┡ԍᮝ໑,

ੱ⦽↽ᗭ᪅₉ᮉ᮹ᔢ⦹5}⠙₉ʑᵡᨱݡ⦽⠪Ɂ᪅₉ᮉᮥ

ࢱႊჶeእƱ⧕ᅕᦥࠥᮁ᮹ᖒᯕᨧ۵äᮝಽӹ┡ԍ݅. ঑௝ᕽ

⢽ᵡ⪵ᩍᇡᨱ ঑ෙ ᪅₉ᮉ ₉ᯕ۵ ᨧ۵ äᮝಽ ❱݉ࡽ݅.

ᯕᔢ᮹đŝಽᇡ░CBR ༉ߙᨱᕽ۵ᩢ᧲᫵ᯙ᮹sᮥᬱs

əݡಽᔍᬊ⦹Ł, ᩢ⨆᫵ᯙaᵲ⊹᮹ᱽ⦽ჵ᭥ෝᖅᱶ⦹ᩍᩢ⨆᫵

ᯙ᮹aᵲ⊹᪡⠙₉ʑᵡ, ᮁᔍᔍಡᙽ᭥ʑᵡᮥ↽ᱢ⪵⦹۵ႊჶᯕ

e݉⦹໕ᕽࠥ ᪅₉ᮉᮥ ↽ᗭ⪵⧁ ᙹ ᯩ۵ äᮝಽ ᇥᕾࡹᨩ݅.

4.3 ฎֱंজࡦ܄րॷߢ׆ࢱ౟ߨࡦ܄ଭण֗

⫭ȡᇥᕾ(RA) ༉ߙŝᔍಡʑၹ⇵ು༉ߙ᮹ᬱᯱഭsəݡಽ

ᔍᬊ⦽༉ߙ(CBR)ŝ⢽ᵡ⪵⦽ᯱഭෝᔍᬊ⦽༉ߙ(CBRST)ᮥ

እƱ⦽đŝ, ↽ᗭ⠪Ɂ᪅₉ᮉᮡbb11.1%, 7.19%, 7.12% ᯕŁ,

⢽ᵡ⠙₉۵10.9%, 8.4%, 6.5% ಽᇥᕾࡹᨩᮝ໑ᯕෝ5}á᷾ᔍ ಡᄥಽ ࠥ᜽⦹໕ Fig. 10ŝ z݅.

(12)

Fig. 10. Comparison of Error Rates between RA Model and CBR Models

3} ༉ߙᮥ እƱ⦹ʑ ᭥⧕ 5} ᔍಡ᮹ ᪅₉ᮉᮥ ᇥᔑᇥᕾ⦽

đŝ, ᪅₉ᮉ᮹₉ᯕaᨧ۵äᮝಽӹ┡ԍ݅. ঑௝ᕽCBR ༉ߙᯕ

RA༉ߙᅕ݅᪅₉ᮉᯕԏ݅Ł❱݉⦹ʑᨱ۵ྕญa঑ෙ݅. ⦽⠙

ࢱ༉ߙᨱݡ⦽ᰆ݉ᱱᮥእƱ⧕ᅕ໕CBR ༉ߙ᮹Ğᬑᱥᚁ⦽

ᩩእᱢŁₑᨱᕽŖ࠺ᵝ┾ŝĞపᱥ℁ᱶÑᰆᨱᕽ᪅₉ᮉᯕRA

༉ߙᅕ݅ԏíӹ┡ӽᩑǍđŝaᯩ݅. ၹ໕, RA ༉ߙᮡᔢݡᱢᮝ ಽ ᇥᕾᯕᬊᯕ⦹ӹ ᙹಽᇡ อ᮹ᄡᙹಽ Ǎᖒࡹᨕ ᯩᨕƱbŝ

Ʊݡ᮹Ŗᔍእෝၹᩢ⧁ᙹᨧ݅۵᧞ᱱᮥwŁᯩ݅. ə్အಽ

ࢱ༉ߙᮥᔍᬊ⧁Ğᬑᨱ۵ƱℕݡᔢᮝಽƱb⡍⧉ᩍᇡ᪡ᔍᬊ᮹

⠙᮹ᖒ ॒ᮥ Łಅ⦹ᩍ᧝ ᳬᮥ äᮝಽ ❱݉ࡽ݅.

5. đು

ᅙםྙᮡᙹಽƱaי⬥⪵ࡹÑӹʑ܆}ᖁᯕ⦥᫵⦹ᩍƱℕ⧁

Ğᬑ}ఖŖᔍእෝᔑᱶ⦹۵ႊჶᮥᱽ᜽⦹ᩡ݅. ᯕෝ᭥⧕2003֥

ᯕ⬥Ʊℕ⦽ᙹಽƱⅾ61}ᔍಡ᮹ᝅᱢŖᔍእᯱഭෝᇥᕾ⦹Ł

ᙽŖᔍእ᪡ᩑࠥᄥÕᖅŖᔍእḡᙹෝᱢᬊ⦹ᩍ݉ᙽ⦽ʑᅙᱶᅕ᮹

᯦ಆอᮝಽ}ఖŖᔍእෝᔑ⇽⦹۵⫭ȡᇥᕾ(RA) ༉ߙŝᔍಡʑ ၹ⇵ು(CBR) ༉ߙᮥ}ၽ⦹ᩍእƱ⦹ᩡᮝ໑ᵝ᫵ᩑǍđŝ۵

݅ᮭŝ z݅.

(1) ᙹಽƱ᮹ Ŗᔍእ ᩢ⨆᫵ᯙᮝಽ ☖ᙹ݉໕⡎()ŝ ׳ᯕ(¡), Ʊℕᩑᰆ(¥),Ğeᙹ(§_Ƒ),Ʊb׳ᯕ(¡_Ǝ)᪡Ʊbᙹ(§_Ǝ), Ʊݡ

׳ᯕ(¡_ſ)᪡Ʊݡᙹ(§_ſ)ෝݡᔢᮝಽ⫭ȡᇥᕾᮥ⦹ᩍƱℕᩑ ۵ᄡᙹ᳑⧊ᮥᔩಽᬕᩢ⨆᫵ᯙᮝಽᱽᦩ⦹ᩍÞ âÏ¡ß¥᪡

¡_Ǝ읆§_Ǝෝ ⇵a⦹ᩡ݅.

(2) RA ༉ߙᨱݡ⦽⫭ȡḥ݉ᮥ⦹ᩍࠦพᄡᙹಽĞeᙹ, Ʊbᙹ, Þ âÏ¡ß¥, _¡_Ǝ_읆§_Ǝ᮹ 4}ෝ ᖁᱶ⦹ᩡ݅. ⫭ȡᇥᕾ ༉ߙ᮹

᪅₉ᮉᮥእƱ⦹ʑ᭥⧕15}݅ᵲ⫭ȡ༉ߙᵲđᱶĥᙹa

׳ᮡ ᳑Õ ॒ᮥ อ᳒⦹۵ 5}ෝ ᖁᱶ⦹ᩍ ᪅₉ᮉ᮹ Ⓧʑ᪡

ᇥ⡍ෝእƱ⦹ᩡŁੱ⦽ᄡᙹ᮹݉᭥ᩢ⨆ᮥᱽÑ⦹ʑ᭥⧕

ᩢ⨆᫵ᯙ ᯱഭෝ ⢽ᵡ⪵⦽ ༉ߙŝࠥ እƱ⦹ᩡ݅.

(3) RA ༉ߙᨱᕽ۵ࠦพᄡᙹෝÞ âÏ¡ß¥᮹1}ಽ⦹۵Ğᬑ (SRA)᮹ ⠪Ɂ ᪅₉ᮉᯕ 11.1%ෝ ӹ┡ԕᨩŁ ᩍ్ }ᯙ

Ğᬑ(MRA)۵20.8%ෝӹ┡ԕᨩ݅. SRA᪡MRA᮹☖ĥ ᱢᯙ₉ᯕෝḥ݉⦹ʑ᭥⧕ᬱsᮥəݡಽᔍᬊ⦽Ğᬑ᪡

⢽ᵡ⪵⦽Ğᬑ᮹ᱥℕෝݡᔢᮝಽ᪅₉ᮉᮥᇥᔑᇥᕾ⦽đŝ,

₉ᯕaᯩ۵äᮝಽᇥᕾࡹᨩ݅. ⦽⠙⢽ᵡ⪵⦽Ğᬑ۵ᬱ

sᮥəݡಽᔍᬊ⦽Ğᬑ᪡₉ᯕaᨧ۵äᮝಽᇥᕾࡹᨩ݅.

RA ༉ߙᮡᬱsᮥᔍᬊ⦹۵݉ᙽ⫭ȡᇥᕾ༉ߙᯕᱢ⧊⦽

äᮝಽ ❱݉ࡽ݅.

(4) CBR ༉ߙᨱᕽ۵ᮁᱥ᦭Łญ᷹(GA)᮹↽ᱢ⪵ᄡᙹಽᖁ⧪ᩑ Ǎ᮹ᩢ⨆᫵ᯙᄥaᵲ⊹᫙ᨱ⠙₉ʑᵡŝᙽ᭥ʑᵡᮥ⡍⧉⦹

ᮥอ᳒⦹ࠥಾ⦹ᩍÞ âÏ¡ß¥۵ⅾŖᔍእ᮹ᙹಽᇡŖᔍእ ᮹እᮉᯕᔢ, ¡_Ǝ읆§_Ǝ۵ⅾŖᔍእ᮹ƱbŖᔍእ᮹እᮉᯕ⦹a

ࡹࠥಾᱽ⦽⦹۵ႊჶᮥ᯦ࠥ⦹ᩍ᪅₉ᮉŝ┱ᔪ⬉ᮉᮥ}ᖁ

⦹ᩡ݅.

(5) CBR ༉ߙᨱᕽ۵ᔢʑ᮹5}ᩢ⨆᫵ᯙᮥᔍᬊ⦹ᩡᮝ໑, ᩢ⨆᫵

ᯙ᮹aᵲ⊹ෝᱽ⦽⦹۵ႊჶ᮹᪅₉ᮉᮡ⠪Ɂ7.19%ᯕᨩᮝ໑,

⢽ᵡ⪵⦽Ğᬑ۵⠪Ɂ7.12%ᯕᨩ݅. ࢱႊჶᨱ঑ෙ᳑Õᄥ

᪅₉ෝ ᇥᔑᇥᕾ⧕ᅕ໕ ₉ᯕa ᨧ۵ äᮝಽ ӹ┡ԍ݅.

(6) RA ༉ߙŝCBR ༉ߙ᮹᪅₉ᮉᮥእƱ⦽đŝࢱ༉ߙᔍᯕᨱ۵

₉ᯕaᨧ۵äᮝಽӹ┡ԍ݅. ঑௝ᕽࢱ༉ߙᮥᔍᬊ⦹ᩍᙹಽ ƱƱℕ}ఖŖᔍእෝᔑᱶ⧁Ğᬑᨱ۵Ʊℕݡᔢŝ⠙᮹ᖒ॒ᮥ

Łಅ⧁ ⦥᫵a ᯩ۵ äᮝಽ ❱݉ࡽ݅.