U LY UIAM | T H E O R E T I C : A L STUDY)
Svt dung mgng Neuron nhan tao de Ude luong chi phi thUc hien du dn khu ddn cU vUOt lu
* TS. l£ HOAI LONG
K H O A K Y T H U A T X A Y D U N G . TRUftNG DAI HOC B A C H KHOA. DAI HQC QUOC GIATP. H 6 CHl M M H '
* VU DUY LINH
T ^ H d S i M N ^ ^"^ ™^^ ™"™'^ ™"*"' ""'^ " * " " ° ""'^ ™"'*'^ ^^J HOC M6
* LV THIEN DUY
HQCVIEN CAO HOC TRUONG DAI H O C M O T P . H A C H I M I N H
* ThS. B«NG NGOG G H A U
CHUYEN NGANH Q U A N LY XAY DUNG. DAI HOC BACH KHOA, DAI HQC Q U d c GIA TP. H 6 CHf M I N H ' IBai nhan ngdy 05/12/201S, hoin chlnh sia chOa ngay 19/12/2015)
TOM TAT: Bift m khf hJu ISm cho muc mlitc bl«n tang ISn vS ISm cho difn tfch ait (fai cOa dong bSng s6ng Olu Long ngSy cing bl thu hep. VSn SI tiiy co the Snh hutSng den !»
phSt trift qudc gla. 0 * ngSn chSn vin d l nay. Chfnh phd da dSu tu cSc du Sn khu dSn cu vUdt lu nhSm cung d p thSm nhS tt cho ngudi dSn vS glijp d i thift cSc v f t de an sinh xS hOi. Tuy nhift, vi«c thuc hlft cSc du Sn nSy dang gip nhift khd khan. Mat trong nhOng khd khan nay IS utfc tfnh c4c chf phf thuc hfft cOa duSn khf khdng cd thfft k l chf tilt, dac biet IS trong giai doan dSu oia dit Jn. BS giijp nhiing ngudi thuc hifn giSi guyet cSc vSn de cCia ho trong quS tiinh thuc hlft dU an, nghift cUu nay dS phst trift mot md hinh mang neuron nhSn tao de ude luang chi phithuc hlft duSn sildung 34 du Sn hoSn thSnh. Ket quS cd the giijp nhUng ngudi thuc hlft dU Sn mdt cdng cu bd sung cho u8c iuong chi phi thuc hift cSc duSn trong khu VUc Id lut
Til khda; Ude ludng, chi phi, khu dSn cu vuot iii, guSn ly xSy dung
ABSTRACT: Climate change makes the sea level incr!.^..
and tiierefere, the land area of the IWekong Delta is nalTonvw«
Ihis problem cQuid affect the natfdnal development iniO^e^*
to prevent this problem, the Government has investedjj fIood.areas-resldential projects, which cduld pravide,i homes for resfdents and help to Improve the social s e c u . issues. However, the implementation of these projects « . , counters many difficulties. One of these difficulties is the esti-' mation of the proJKt5"implementation costs without detailed designs, especially in the early stage of the projects In order to help practitioners to sojve their problems in the project implementation, this study developed an artificial neural net- work model for estimating the cost of project Implementation using 34 competed projects. The results could provide ptjol- tioners an additional tool for estimating the i "
OKI of the piftjectsfn the ffoottareas.
Ift^nirorilsl Estimate, tm fe(id*8as-resfdential p r r j / a i J constnjction managemett
(THEORETICAL STUDY) N
i.ra
Biln doi khi hlu II mot van d l nong dang dupe ca t h l gidl quan Iam slu sac Biln doi khf h l u la nguyin nhln chinh lam cho muc nuoc bien ding cao, khien khu vUc ddng blng song Cifu Long dang mat dan difn tich dit. Chinh phu da dau tU xly dung nhilu kfch bin de dfli phd vdi biln d6i khi hau. Khu dan or vuot lu la mpt trong nhung bifn phlp d l doi pho vdi vin d l nay vh no gop phln gilm bdt ap liicvl ehfl dcho chinh quyln dja phuang.
Hiln nay, c6 tit nhieu cdng trinh xly dung dang bj tam ngung hole chSm t r i tiln do xly dung bdi vi c5c nhl dau tu khong cd kl hoaeh tot v l ngufin vfln diu tucho dU an. Mpt trong nhUng nguyin nhln g3y ra v3n de nay II viec ude lugng chl phi xly dg'ng thilu chinh xac llm cho nhUng nha diu tu khdng co k l hoach ehuin bi trudc ngufin vdn. Vi vly, vile xic dinh chi phi thuc hi|n du I r II tit quan trpng. Trong glai doan ban diu khi chua co thiet k l ehi tilt thl vile ude luong chi phi thUc hien du In phy thufle nhilu vio ngucfi quin ly di; In. Ude lugng chi phi eang chinh xle thl cing giijp cho chu diu tu ed t h l xly dung d c ke hoaeh. chiln luge quin ly du In hilu qui, d^c bilt II ke hoach v l chi phi cho duin.
Myc tilu eua nghiln eOu nly II xly dyng mo hinh mgng neuron nhln tao ude lupng chi phf thUc hifn du an khu din cu vupt lu trong giai doan ban dau khi chua cd thilt k l ehi tilt, Nghifn ciJu nly SlJ dyng dS lifu cCia 34 du In khu din eu vupt lu da d i ^ thuc hien tai tinh An Giang.
2. IH«NS MBURON NHllV
Mgng nairal nhln tgo {Artificial Neural Network. ANN) mfl phdng cich tmyin tai. xiJ 1|? thflng lin giila cic neuron GJa bp
nao con ngUcfi blng nhCmg thuat toan Gidng nhu con ngudi, ANN cd khi nang hpc hdi tir nhimg dC lilu trong qua khii vl tim mdi lien he giOa cae diJ lieu de du3 ra du tao eho nhiing trUcmg hop tuong ty. Md hinh mang ANN ed khi nang dy bao manh me ngay ca vdi dU lieu bi khuyet hay phi tuyln. Qua trinh hpe hdi cua mang ANN la mpt qua trinh lap di lap lai. Mdi lln lap lai, md hinh ANN se tudilu chlnh bp uong so de kit qua du bao tiln gan kit qua thyc te hon. Mang ANN d l dupc SLf dyng rdng rai trong nhieu ITnh vycnhUxCrlyhtnh anh, xLtly dii iieu, nhan dang, y hpc, quin su, kinh te,... va gan dly, ANN cdn dupc ung dung nhilu trong Isnh vuc quin ly xay dung Mflt cau true mang ANN bao gdm mpt Idp diu vao (input layer), mgt hole nhilu Idp $r\ (hidden layer) v l mdt idp diu ra (output layer) (Hinh 1). Odi vdi nghien ciiu nly, dti lilu dau vio la cac yeu tfl duoc xac dinh blng cich phong van chuyin gia va ket qui dau ra la chi phf thuc hien du m.
Input Layer Hidden Layer O u ^ u i L a y e i
ANN Ude lupng chi phi xly dung chung cu vdi 6 biln dau vao. Adelt & Wu [4] da Sli dung mang neuron nhan tao de Ude lupng chi phi be tdng via he cho cac du an duflng cao toc. Emsley va cac tac gil [5] da sir dung dii lieu quyet toln eua 300 du an xay d m g de xay dUng md hinb mang neuron nhan tao v l so sanh vdr md hinh hdi quy
3. PHinnVG PHAP NGWEN CUU
Hinh 1. Clu tnic mang neuron nhan too dien hinh [7]
Mang neuron nhln tao da dupe ung dyng trong kha nhilu nghiln ciiu d cl trong va ngoai nudc Ching han nhu. Lmh va cac tac gil [1] d l siidyng mang neuron nhln tao de ude luong thdi gian thue hiln dU an clu tai khu vuc ddng blng sdng Ciiu Long v l miln Ddng Nam Bd. Hoa [2] da ling dyng mang neuron nhln tao de ude lupng chi phi xly dung cao dc tai Thanh phfl Hd Chi Mmh Khoa va cic tac gia [3]
3.1. Ihu thdp du^iiiu
Nghien cdu nay duoc thue hien blng eac giai doan nhU: giai doan thii nhlt II phdng van cac ehuyen gia d l xle dmh cac yeu td ehfnh llm Inh hudng din ehi phi thue hten du an. Giai doan thii hai dUa trln ket qua cua giai doan thd nhlt de thu thip sd lilu thi/c t l va sii dung mang neuron nhln tao d^ xay dung mfl hinh ude luong chi phi cho dgng du an nly.
Cac budc dupe t h l hiln chi ttit nhu sau.
Giai doan thiJ nhlt \h phdng van ehuyin gia de xic djnh eac ylu td Inh hudng den chl phf thuc hifn dU 3n khu din cu vupt lu. Dua trln tai lifu, sach, blo va nhiing nghiln ciiu trudc, mdt blng khio sit da dupc thiet ke so bd Cd 10 ehuyin gia trong do cd 7 chuyin gia tren 10 nim kinh nghilm, v l 3 ehuyin gia trfn 5 nam fcinh nghilm trong Unh vuc nly da dUOc mdl tham gia ddng gdp y kiln v l blng clu hdl v l mit hinh thifc va ndi dung.
Blng nhUng kmh nghilm tfch luy duoc trong qua tnnh thuc hiln du In khu din cu vuot lu, nhdm chuyen gia da cd ^ kien bd bdt mdt sd ylu td ft Inh hudng va d f xult thlm vao cac ylu td mdi Blng khio sat duoc dtlu chinh dUa tren nhUng y kien ddng gdp va sau do duoc gUi lai nhom chuyin gia Qua trinh dd duoc lap di lap lai eho din khi t i t cl cac chuyen gia deu ddng y vdl bang khio sat. Sau dd, blng da sir dyng matlab d l xaydung mdhinh | khio sit duae gifi tdi 25 ngudi co it nhat
II vi Uli^H (THEOREnCAL STUDY)
5 nam kmh nghifm thue hien du an khu din cU vupt lu. Tat cl cac bang cau hdi deu duoc giii blng hinh thUc true tiep.
Nhu'ng ngudi tham gia khao sat diu duoc hudng din va gili thich tam quan trpng cua bang cau hdi. Ket qui duoc tdng hc^
va dlnh gia blng each tfnh diem, nhfing yeu tfl cd diem trung binh nhd hon 3 0 b!
loai bfl Kft qui thu duoc ciJa giai doan nly la 14 yfu td chfnh anh hudng din chf phi thuc hiln d\J In khu din cu vUpt lu (Bang 1).
Giai doan thii hai la thu thap dii lieu tU cac du In da thuc hifn. DCi lieu dUOc thu thap ti^ cac dutoln eiia nhting dUin khu dan cU vuot 10 duoc thue hiln tai tinh An Giang, thUc hifn tCf nam 2005 din nam 2014, dii lifu duoc lay theo 14 ylu td
duoc xac dmh tir giai doan thd nh3t.
3.2. Mo hinh
Md htnh ANN dupe xay dung vdi cac bien dau vao la 14yfutd (Blng l ) v l biln dau ra Y la chi phf thue hien dy an khu dan cU vuot lu. Sd neuron ciia md hinh tir 2-Jn+m den 2n + i, vdi n II sd bien dau vao va m la sd bifn dau ra [5], tUOng ling vdi so neuron trong khoing tit 8 den 29 neuron. Sd Idp an (hidden layer) dupe mac dinh la mdt Idp an, Lan lucft khSo sit timg ham truyen vdi timg neuron de tim ra him truyen va sfl neuron toi uu nhlt cho md hinh Ty If cua bd sd lilu sir dung de hoc va kilm tra duoe mac dinh lln lucft II 24 bo (chilm 70,6%) va 10 bd (chilm 29,4%) Sealed conjugate gradient la gili thuat toi Uu hda duac sd dung trong md
B A N G 1. CAc Y f u T 6 A N H Hi/dNG D E N C H I PHf T H I / C HrEN D I / A N K H U D A N C I / V I T O T L O
Chl phi Ihuc hi^n d u i n
hinh d l ilm tang nhanh dd hfli ty trorig qui trinh hu5n luyen, lam gilm thdi gjaffl huan luyfn.
DCf lieu diu vio dupc md hinh g3n c h ^ mdt bd trong s6 nglu nhifn d^ dy bi gia tri dau ra. Gil trj dU bao nSy dU^Jc^
sinh vdi wS gia tri thuc t l da bilt ti nlu sai sfi Icm hon sai sfl ylu clu t t i l ^ hinh tilp tuc truyen dd lilu ngugc Igl J idp dung trudc de tfnh toln vS d l S u ' ^ bd Irpng sd vl tifp tyc qui trlnh dy fe Qua trinh lan truyfn ngupc nSy i dimg lai khi sai sd giu'a gil trj dy b l d ' ^ gia tri thuc t l dat yfu cau.
Qui trinh lan truyen ngupc giiip cho md MSffl ed kit qui tdt hon nhung c6 thl gly ra H^i\\^
tirang qui khdp (lack of generality). N61^ hijh tuong m6 hinh ed khS nSng di; bSo rft tfiH nhung bo so lieu dupc hpc nhimg Ig! khfijiaJ c6 khi nSng long quit nfn khi dy bio nKfffli I bo sd hf u mdl thi ket qu3 dy blo c6 saf sd Idfiif^^
Nguyen nhan ciia hien tupng qui kh(fe \l dt ySu c3u sai s6 trong qui trlnh huSn luyfn nh^
. hon mUc cln thilt. Mdt trong nhOtig blgfi,; -j phap de kilm tra hiSn tuong qui khdp II so M sSnh cac gia trf sai s6 phln tram P£ (percentr ] age error) vh sai sd phSn tram tuyft S6\ trung I binh MAPE (mean absolute peixientage ems^ J giiJa nhung bo sd lifu dCtng ai huSn luygn vii | dung al kilm tra. Nlu khoing chlnh Ifch nJiy, 1 Idn th) mfl hlnh b| qui khdp.
Sau 132 lln chay mfl hlnh tdl uu dS iJU(Jc I Iua chpn bSng elch so sinh kit qui gKi, 1 tri gdm sal sfi phan trim PE, sal sfi phlrt | tram tuyft ddi trung binh MAPE vl hf srfj xic dinh (coefficient of determination) R ' l ciJa timg lln chay. Kit qui cho thSy mSiJ hlnh ed 25 neuron vdi cic ham truyla j ldp input vh ldp output dupc mic djnh { II sigmoid vl identity da cho kit quMfit I nhSL Cic gil tr, PE v^ MAPE V| R= Q^QC 1 tinh theo cdng thUc sau; *
(THEORETICAL STUDY) HBHlSN CttU it LI^H |
pg^(Ptedicted-ActuaD^,O0^
Actual dat duoc kitqui dau ra nhumong mufln.
WIe nay eo the giup hgn chf nhimg thay doi trong qui trinh thUe hien dU In va llm giam bdt rui ro cho dyln •
Hlnh 2 . Gid tri ph^n tram sai s6 PE cda bd huln luyfn
4.KiTQUAGUAMaHilUH
Kit qui ciia mfi hlnh dupc thl hifn trong blng 2, hlnh 2 vl hlnh 3. Gil trj PE cua md hlnh dao dfing tnsng khoing ty-14.85%
dfn 15,63%. Gil tri MAPE cua bp huln luyfn vl bd kilm tra lln lupt II 4,24%
vl 8,16%. Gil tri R' ciia bfi huln luyfn vl hd kilm tra lln luot II 0,99 vl 0,96, cho thiy ring mfi hlnh cd thf gili thfch dupc khoing hon 95% phudng sai. Hinh 4 cho thiy elc dilm gil tri phln bfl gan sit vdi dudng 450, chifng td ring mde dd phu hpp cua md hlnh bfl sfi lifu II rat cao. Gil trj PE cOa bfi huln luyfn vl bfi kifm tra chgy trong khoing ±15% II rit tdt, chdng tfi rSng md hlnh cfl khi nlng tdng quit hda caa
5. KET LUAN VA KliM MGH!
Nghifn ciiu dl xle djnh dupc )4 ylu td chlnh lam Inh hudng din chi phi thuc hifn du In khu din cu vupt lij thfing qua vile khio sit y kiln chuyin gia. Nhiing ngudi tham gia thyc hifn du In nfn luu )? tdi 14 yfu tfi da dupc xle (^nh II cd Inh hudng idn din chi phi thyc hifn dy an, vl nln kilm solt tdt nhutig ylu 16 nly df trlnh vifc phlt sinh chi phf khi thuc hifn duin.
Nghien cdu cung dl xly dyng dupc md hlnh tyddng hda ude lupng chi phf thyc hiln dy at) khu dan eu vupt lii trong giai dogn ban dau khi chua cd thiet kl chi tift.
Md hinh II mdt eo sd giup chu dau tu vl nhiing ngudi quin i^ dy In cd thf ude lupng dupe ehi phf thye hien dy In eiia minh trong giai doan ban dau vl tii do cd thf dua ra quyft dinh cd nln tilp tyc elc bude tilp theo hay khdng. Tir md hinh, chO dau tuvi nhtJng ngudi quin ly dyln cd the thay doi cle thflng sd diu vio de
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[2] NJi.T. Hda (2011). "Ifdc luang chl phi xdy dung cao oc dda trin hieu chinh phuang phdp Storey En- closure Method", Ludn vdn Thgc sf, Dgihoc Bdch Khoa TP Ho Qii Minh.
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(2004). "A neural network ap- proach for early cost estimation of structural systems of buildings." In- ternational Journal of Project Man- agement 22 (7), 595-602.
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(2002). "Data modelling and the application of a neural network approach to the prediction of total construction cost", Construction Management and Economics. 20 (6), 465-472.
[6} Fletcher, D., Goss, E. (1993).
'Forecasting with neural networks:
An application using bankruptcy data" Information & Management 24(3), 159-167.
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IHlExAYDUNGjia
