Other language title :
ارزﯾﺎﺑﯽ زﻣﺎن اﺟﺮاي ﺗﻮﻧﻞ ﺳﺎزي ﺑﺎ ﻣﺎﺷﯿﻦ ﺣﻔﺎري TBM ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ دوره زﻣﺎﻧﯽ ﯾﺎدﮔﯿﺮي
Title of article :
TBM Tunneling Construction Time with Respect to Learning Phase Period and Normal Phase Period
Author/Authors :
Farrokh, E Department of Mining and Metallurgy Engineering - Amirkabir University of technology - Tehran, Iran
Abstract :
In every tunnel boring machine (TBM) tunneling project, there is an initial low production
phase so-called the Learning Phase Period (LPP), in which low utilization is experienced
and the operational parameters are adjusted to match the working conditions. LPP can be
crucial in scheduling and evaluating the final project time and cost, especially for short
tunnels for which it may constitute a major percentage of the total project completion
time. The contractors are required to have a better understanding of the initial phase of a
project to provide better estimates in the bidding documents. While evaluating and
shortening of this phase of low production is important for increasing the productivity and
daily advance rate of the machine, there has been limited a direct study and assessment of
this period. In this work, we discuss the parameters impacting LPP, and introduce a new
methodology for its evaluation. In this regard, an algorithm is introduced for estimation
of the approximate extent of LPP based on some TBM tunneling case histories. On the
basis of many statistical analyses conducted on the actual data and application of two
different shapes of linear and polynomial for the description of LPP, a linear function is
proposed for estimation of the learning phase parameters. The major parameters of this
function are the learning conditions’ rating and the proportion of LPP to tunnel diameter
(X1/D). Analysis of the correlation between these two parameters show a very good
coefficient of determination (R2 = 92%). This function can be used for the evaluation of
TBM advance rates in LPP and for adjusting the TBM utilization factor in the initial stages
of a TBM tunneling project. The learning phase can affect the overall utilization rate and
completion time of the tunnels, especially when their lengths are around a couple of
kilometers. A true understanding of the LPP characteristics can help the contractors to
come up with a more accurate bidding time and cost evaluation, and may also benefit the
clients to arrange a better schedule for the final project delivery to the public.
Farsi abstract :
در ﻫﺮ ﭘﺮوژه ﺗﻮﻧﻞ ﺳﺎزي ﺑﺎ ﻣﺎﺷﯿﻦ ﺗﻮﻧﻞ زﻧﯽ TBM)(، ﯾﮏ ﻣﺮﺣﻠﻪ اوﻟﯿﻪ ﺑﺎ ﻧﺮخ ﭘﯿﺸﺮوي ﮐﻢ وﺟﻮد دارد ﮐﻪ اﺻﻄﻼﺣﺎَ ﺑﻪ آن دوره زﻣﺎﻧﯽ ﯾﺎدﮔﯿﺮي )LPP( اﻃﻼق ﻣﯽ ﺷـﻮد. در اﯾﻦ دوره، ﭘﺎراﻣﺘﺮﻫﺎي ﻋﻤﻠﯿﺎﺗﯽ ﻣﺎﺷـﯿﻦ ﺑﺮاي ﻣﻄﺎﺑﻘﺖ ﺑﺎ ﺷـﺮاﯾﻂ ﮐﺎر ﺗﻨﻈﯿﻢ ﻣﯽﺷـﻮﻧﺪ. اﻫﻤﯿﺖ اﯾﻦ دوره زﻣﺎﻧﯽ در ﺑﺮﻧﺎﻣﻪرﯾﺰي و ارزﯾﺎﺑﯽ زﻣﺎن و ﻫﺰﯾﻨﻪ ﻧﻬﺎﯾﯽ ﭘﺮوژه، ﺑﻪ وﯾﮋه ﺑﺮاي ﺗﻮﻧﻞﻫﺎي ﮐﻮﺗﺎه ﮐﻪ ﻣﻤﮑﻦ اﺳﺖ درﺻﺪ ﻋﻤﺪهاي از ﮐﻞ زﻣﺎن اﺗﻤﺎم ﭘﺮوژه را زﻣﺎن ﯾﺎدﮔﯿﺮي ﺗﺸﮑﯿﻞ دﻫﺪ، ﺑﯿﺸﺘﺮ اﺳﺖ. در ﺣﺎﻟﯽ ﮐﻪ ارزﯾﺎﺑﯽ و ﮐﻮﺗﺎه ﺷﺪن اﯾﻦ ﻣﺮﺣﻠﻪ ﺑﺮاي اﻓﺰاﯾﺶ ﺑﻬﺮهوري و ﺳﺮﻋﺖ ﭘﯿ ﺸﺮوي روزاﻧﻪ ﻣﺎ ﺷﯿﻦ ﺣﻔﺎري ﺑ ﺴﯿﺎر اﻫﻤﯿﺖ دارد، ﻣﻄﺎﻟﻌﺎت ﺑ ﺴﯿﺎر ﮐﻤﯽ در اﯾﻦ زﻣﯿﻨﻪ اﻧﺠﺎم ﺷﺪه ا ﺳﺖ .در اﯾﻦ ﻣﻄﺎﻟﻌﻪ ، در ﻣﻮرد ﭘﺎراﻣﺘﺮﻫﺎي ﻣﺆﺛﺮ ﺑﺮ LPP ﺑﺤﺚ ﺷﺪه ا ﺳﺖ و ﯾﮏ روش ﺟﺪﯾﺪ ﺑﺮاي ارزﯾﺎﺑﯽ آن اراﺋﻪ ﺷﺪه ا ﺳﺖ. در اﯾﻦ را ﺳﺘﺎ از ﯾﮏ اﻟﮕﻮرﯾﺘﻢ ﺑﺮاي ﺗﺨﻤﯿﻦ زﻣﺎن ﺗﻘﺮﯾﺒﯽ LPP ﺑﺮ اﺳﺎس اﻃﻼﻋﺎت واﻗﻌﯽ ﺗﻌﺪاد زﯾﺎدي ﺗﻮﻧﻞﻫﺎي ﺣﻔﺮ ﺷﺪه ﺑﺎ TBM اﺳﺘﻔﺎده ﺷﺪه اﺳﺖ. ﺑﺮ اﺳﺎس ﺗﺠﺰﯾﻪ و ﺗﺤﻠﯿﻞ آﻣﺎري اﻧﺠﺎم ﺷﺪه ﺑﺮ روي دادهﻫﺎي واﻗﻌﯽ و اﺳــﺘﻔﺎده از دو ﺷــﮑﻞ ﻣﺨﺘﻠﻒ ﺧﻄﯽ و ﭼﻨﺪ ﺟﻤﻠﻪ اي ﺑﺮاي ﺗﻮﺻــﯿﻒ ﺗﺎﺑﻊ LPP ، ﯾﮏ ﺗﺎﺑﻊ ﺧﻄﯽ ﺑﺮاي ﺗﺨﻤﯿﻦ ﭘﺎراﻣﺘﺮﻫﺎي آن اراﺋﻪ ﺷــﺪه اﺳــﺖ. ﻣﻬﻤﺘﺮﯾﻦ ﭘﺎراﻣﺘﺮﻫﺎي ﺗﺎﺛﯿﺮﮔﺬار در اﯾﻦ زﻣﯿﻨﻪ ﺷﺎﻣﻞ اﻣﺘﯿﺎز ﺷﺮاﯾﻂ ﯾﺎدﮔﯿﺮي و ﻧﺴﺒﺖ زﻣﺎن LPP ﺑﻪ ﻗﻄﺮ ﺗﻮﻧﻞ )X1 / D( اﺳﺖ. ﺗﺤﻠﯿﻞ ﻫﻤﺒﺴﺘﮕﯽ ﺑﯿﻦ اﯾﻦ دو ﭘﺎراﻣﺘﺮ، ﺿﺮﯾﺐ ﺗﻌﯿﯿﻦ ﺧﻮﺑﯽ را ﻧﺸـــﺎن ﻣﯽدﻫﺪ. دوره زﻣﺎﻧﯽ ﯾﺎدﮔﯿﺮي ﻣﯽﺗﻮاﻧﺪ ﺑﺮ ﻣﯿﺰان ﺑﻬﺮهوري ﮐﻠﯽ و زﻣﺎن اﺗﻤﺎم ﺗﻮﻧﻞﻫﺎ ﺗﺄﺛﯿﺮ ﺑﮕﺬارد، ﺧﺼـــﻮﺻـــﺎ زﻣﺎﻧﯽ ﮐﻪ ﻃﻮل آﻧﻬﺎ ﮐﻤﺘﺮ از دو ﮐﯿﻠﻮﻣﺘﺮ ﺑﺎ ﺷﺪ. درك ﺻﺤﯿﺢ از ﺧ ﺼﻮ ﺻﯿﺎت LPP ﻣﯽﺗﻮاﻧﺪ ﺑﻪ ﭘﯿﻤﺎﻧﮑﺎران ﮐﻤﮏ ﮐﻨﺪ ﺗﺎ ارزﯾﺎﺑﯽ ﻣﻨﺎ ﺳﺒﯽ از زﻣﺎن و ﻫﺰﯾﻨﻪﻫﺎ ي ﭘﺮوژه دا ﺷﺘﻪ ﺑﺎ ﺷﻨﺪ، ﻫﻤﭽﻨﯿﻦ اﻣﮑﺎن ﺑﺮﻧﺎﻣﻪرﯾﺰي ﺑﺮاي ﺗﺤﻮﯾﻞ ﻧﻬﺎﯾﯽ ﭘﺮوژه را ﻧﯿﺰ ﻓﺮاﻫﻢ ﻣﯽﮐﻨﺪ
Keywords :
Time evaluation , Tunnel boring machine , Advance rate , Learning phase , Normal phase
Journal title :
Journal of Mining and Environment