Ijraset Journal For Research in Applied Science and Engineering Technology
Authors: Tejaswini Potdar, Yogita Fulse
DOI Link: https://doi.org/10.22214/ijraset.2022.44558
Certificate: View Certificate
We know nowadays selection of appropriate site for any project is influenced by several factors that can be ecological and environmental awareness, social acceptance of land development activity, geological factors and many more. And selection of land is considered as very important phase of whole process of land development. Many times we see that number of experienced decision makers makes decision for site selection randomly on the basis of prior experiences, But due to numerous factors influence the choice of site selection some times decision may go wrong when took randomly. Hence the scope of this paper deals with formulating a preliminary model for primary site selection of the residential building and then developing an support system that can be used by decision makers for site selection. Which complies the large data required to take the decision. F-AHP uses a hierarchical structure comprising factors that are based on factual data and the knowledge and experience of the decision makers. This data was collected by conducting a questionnaire survey amongst the decision makers.
I. INTRODUCTION
The selection of site is foremost thing that has to considered before commencement of any project. The factors that influence the process of site selection can physical factors, economical factors, environmental factors, geological factors. Despite having all the data the decision makers make the decision based on there gut feelings and experiences, reason behind this is the data is very complex and not in proper format which can be help full for the decision makers to take the quick decision. Hence to make multi criteria decision easy, We are trying to implement Fuzzy-Analytical Hierarchy Process. Fuzzy-Analytical Hierarchy Process method is one of the best methodology based on triangular fuzzy scale. It is used to solve the Multi Criteria Decision Making problems (MCDM).
II. FUZZY-ANALYTICAL HIERARCHY PROCESS
F-AHP enables evaluation of MCDM and uses triangular fuzzy scale. F-AHP is an analytical approach that provides measurement and assessment using pairwise comparison between criteria and then alternatives, developed by Thomas Saaty. FAHP helps to provide a mechansim that helps the decision makers to reduce confusion and baisness in decision making. Here by using F-AHP method we proposed new method for safety impact factors selection problem. By considering different residential projects in city, knowledge of the project manager. In simple words we can say that that this mechanism consist of objective, criteria and alternatives levels and each criteria is divided in sub-criteria. And we give priority weights to each criteria, then by doing pair-wise comparison weights are given to each alternative and then final site selection is done.
A. Criteria
Criteria for F-AHP which was decided on the basis of interviews taken of various decision makers, project managers and knowledgeable persons. And further those criteria were considered while application of F-AHP model for site selection of residential project.
III. FUZZY ANALYTICAL HIERARCHY MODEL (F-AHP)
TABLE I
Linguistic terms and the corresponding triangular fuzzy numbers[1]
Saaty Scale (Score) |
Scale of Relative Imporatance |
Fuzzy Triangular Scale |
1 |
Equally Important |
(1,1,1) |
3 |
Weakly Important |
(2,3,4) |
5 |
Fairly Important |
(4,5,6) |
7 |
Strongly Important |
(6,7,8) |
9 |
Absolutey Important |
(9,9,9) |
2 |
The intermittent values between two adjacent scales |
(1,2,3) |
4 |
(3,4,5) |
|
6 |
(5,6,7) |
|
8 |
(7,8,9) |
According to knowledge of decision maker select the scale and respective fuzzy triangular scale. For example if criteria 1 is weakly important than criteria 2 then it will have score as 3 and hence fuzzy triangular scale as (2,3,4) and if vice versa than (1/2,1/3,1/4).
2. Step 2-According to averaged preferences, pair wise contribution matrices is prepared.
3. Step 3-According to Buckley, the geometric mean of fuzzy comparison values (ri) of each criteria is calculated. It still represents triangular values.
4. Step 4-The fuzzy weight of each criteria is found by
a. vector summation of each fuzzy value
b. Find (-1) power of summation vector and arrange in increasing order.
5. Step 5-The fuzzy weight of quality criteria is found by multiplying each relative weight with values arranged in increasing order.
6. Step 6-The relative non-fuzzy weight of each criteria is calculated by taking average of fuzzy numbers for each criteria.
7. Step 7-By using non-fuzzy weight normalized weights of each criteria is calculated.
8. Step 8-Weight of alternatives is calculated with respect to each criteria.
IV. APPLICATION OF FUZZY ANALYTICAL HIERARCHY MODEL
Questionnaire survey was carried out among project managers, contractors, owners, small construction firms which are located in Nashik district of Maharashtra. This firms majorly works in residential project. Survey was carried face to face and through online forms. Here we have selected four alternatives sites in Nashik District of Maharashtra for residential building construction. And by using F-AHP best alternative will selected with highest normalized weight for residential site construction.
TABLE II
PAIRWISE COMPARISON OF MAIN CRITERIA
|
MA |
FA |
EF |
PF |
MA |
(1,1,1) |
(1,1,1) |
(1/9,1/9,1/9) |
(4,5,6) |
FA |
(1,1,1) |
(1,1,1) |
(1,1,1) |
(6,7,8) |
EF |
(9,9,9) |
(1,1,1) |
(1,1,1) |
(6,7,8) |
PF |
(1/4,1/5,1/6) |
(1/6,1/7,1/8) |
(1/6,1/7,1/8) |
(1,1,1) |
Geometric mean of Fuzzy comparison values (ri)
MA= (1x1x1/9x4)1/4; (1x1x1/9x5)1/4; (1x1x1/9x6)1/4 = 0.816,0.863,0.903
FA= (1x1x1x6)1/4; (1x1x1x7)1/4; (1x1x1x8)1/4 = 1.56,1.626,1.681
EF= (9x1x1x6)1/4; (9x1x1x7)1/4; (9x1x1x8)1/4 = 2.710,2.817,2.912
PF= (1/4x1/6x1/6x1)1/4; (1/5x1/7x1/7x1)1/4; (1/6x1/8x1/8x1)1/4= 0.28,0.252,0.22
TABLE III
RELATIVE WEIGHTS
Criteria |
|
ri |
|
MA |
0.816 |
0.863 |
0.903 |
FE |
1.56 |
1.626 |
1.681 |
EF |
2.710 |
2.817 |
2.912 |
PF |
0.288 |
0.252 |
0.225 |
Total |
5.374 |
5.558 |
5.721 |
Power of -1 |
0.186 |
0.179 |
0.174 |
Increasing order |
0.174 |
0.179 |
0.186 |
TABLE IV
FUZZY WEIGHTS OF EACH CRITERIA (WI)
Criteria |
|
Wi |
|
MA |
0.147 |
0.154 |
0.167 |
FA |
0.271 |
0.291 |
0.312 |
EF |
0.471 |
0.504 |
0.541 |
PF |
0.05 |
0.045 |
0.041 |
TABLE V
NON-FUZZY WEIGHT AND NORMALISED WEIGHT
Criteria |
Mi |
Ni |
MA |
0.156 |
0.156 |
FA |
0.291 |
0.291 |
EF |
0.508 |
0.508 |
PF |
0.045 |
0.045 |
Level 2 - Determining the weight of each aletrnative with respect to each criteria
TABLE VI
PAIR-WISE COMPARISON
Market Analysis |
Site 1 |
Site 2 |
Site 3 |
Site 4 |
Site 1 |
(1,1,1) |
(1/5,1/6,1/7) |
(1/7,1/8,1/9) |
(1,1,1) |
Site 2 |
(5,6,7) |
(1,1,1) |
(1/4,1/5,1/6) |
(5,6,7) |
Site 3 |
(7,8,9) |
(4,5,6) |
(1,1,1) |
(7,8,9) |
Site 4 |
(1,1,1) |
(1/5,1/6,1/7) |
(1/7,1/8,1/9) |
(1,1,1) |
TABLE VII
RELATIVE WEIGHTS
Market Analysis |
|
ri |
|
Site 1 |
0.411 |
0.379 |
0.354 |
Site 2 |
1.581 |
1.638 |
1.690 |
Site 3 |
3.741 |
4.229 |
4.695 |
Site 4 |
0.411 |
0.379 |
0.354 |
Total |
6.144 |
6.625 |
7.093 |
Power of -1 |
0.162 |
0.150 |
0.140 |
Increasing order |
0.140 |
0.150 |
0.162 |
TABLE VII
FUZZY WEIGHTS OF EACH CRITERIA (WI)
Market Analysis |
|
Wi |
|
Site 1 |
0.057 |
0.056 |
0.057 |
Site 2 |
0.221 |
0.245 |
0.273 |
Site 3 |
0.523 |
0.634 |
0.760 |
Site 4 |
0.057 |
0.056 |
0.057 |
TABLE IX
NON-FUZZY WEIGHT AND NORMALISED WEIGHT
Market Analysis |
Mi |
Ni |
Site 1 |
0.056 |
0.057 |
Site 2 |
0.246 |
0.249 |
Site 3 |
0.639 |
0.635 |
Site 4 |
0.056 |
0.057 |
2. Criteria- Feasibility Analysis
TABLE X
PAIR-WISE COMPARISON
Feasibility Analysis |
Site 1 |
Site 2 |
Site 3 |
Site 4 |
Site 1 |
(1,1,1) |
(1/6,1/7,1/8) |
(1/9,1/9,1/9) |
(1,1,1) |
Site 2 |
(6,7,8) |
(1,1,1) |
(1/4,1/5,1/6) |
(5,6,7) |
Site 3 |
(9,9,9) |
(4,5,6) |
(1,1,1) |
(7,8,9) |
Site 4 |
(1,2,3) |
(1/5,1/6,1/7) |
(1/7,1/8,1/9) |
(1,1,1) |
TABLE XI
RELATIVE WEIGHTS
Feasibility Analysis |
|
ri |
|
Site 1 |
0.368 |
0.354 |
0.343 |
Site 2 |
1.654 |
1.702 |
1.747 |
Site 3 |
3.987 |
4.355 |
4.695 |
Site 4 |
0.411 |
0.379 |
0.354 |
Total |
6.420 |
3.790 |
7.139 |
Power of -1 |
0.155 |
0.263 |
0.140 |
Increasing order |
0.140 |
0.155 |
0.263 |
TABLE XII
FUZZY WEIGHTS OF EACH CRITERIA (WI)
Feasibility Analysis |
|
Wi |
|
Site 1 |
0.051 |
0.054 |
0.090 |
Site 2 |
0.231 |
0.263 |
0.459 |
Site 3 |
0.558 |
0.675 |
1.234 |
Site 4 |
0.057 |
0.028 |
0.093 |
TABLE NO XIII
NON-FUZZY WEIGHT AND NORMALISED WEIGHT
Feasibility Analysis |
Mi |
Ni |
Site 1 |
0.065 |
0.051 |
Site 2 |
0.317 |
0.250 |
Site 3 |
0.822 |
0.650 |
Site 4 |
0.059 |
0.046 |
3. Criteria- Environmental Factor
TABLE XIV
PAIR-WISE COMPARISON
Environmental Factor |
Site 1 |
Site 2 |
Site 3 |
Site 4 |
Site 1 |
(1,1,1) |
(1/1,1/2,1/3) |
(1/5,1/6,1/7) |
(1,1,1) |
Site 2 |
(1,2,3) |
(1,1,1) |
(1/4,1/5,1/6) |
(3,4,5) |
Site 3 |
(5,6,7) |
(4,5,6) |
(1,1,1) |
(4,5,6) |
Site 4 |
(1,1,1) |
(1/3,1/4,1/5) |
(1/4,1/5,1/6) |
(1,1,1) |
TABLE XV
RELATIVE WEIGHTS
Environmental Factor |
|
ri |
|
Site 1 |
0.668 |
0.537 |
0.467 |
Site 2 |
0.930 |
0.945 |
0.955 |
Site 3 |
2.990 |
3.499 |
3.984 |
Site 4 |
0.537 |
0.472 |
0.427 |
Total |
5.125 |
5.453 |
5.833 |
Power of -1 |
0.195 |
0.183 |
0.171 |
Increasing order |
0.171 |
0.183 |
0.195 |
TABLE XVI
FUZZY WEIGHTS OF EACH CRITERIA (WI)
Environmental Factor |
|
Wi |
|
Site 1 |
0.114 |
0.098 |
0.091 |
Site 2 |
0.159 |
0.172 |
0.186 |
Site 3 |
0.511 |
0.640 |
0.776 |
Site 4 |
0.091 |
0.086 |
0.083 |
TABLE NO XVII
NON-FUZZY WEIGHT AND NORMALISED WEIGHT
Environmental Factor |
Mi |
Ni |
Site 1 |
0.101 |
0.1008 |
Site 2 |
0.172 |
0.171 |
Site 3 |
0.642 |
0.641 |
Site 4 |
0.086 |
0.085 |
4. Criteria- Physical Factor
TABLE XVIII
PAIR-WISE COMPARISON
Physical Factor |
Site 1 |
Site 2 |
Site 3 |
Site 4 |
Site 1 |
(1,1,1) |
(1/7,1/8,1/9) |
(1/9,1/9,1/9) |
(1/2,1/3,1/4) |
Site 2 |
(1/7,1/8,1/9) |
(1,1,1) |
(1/2,1/3,1/4) |
(5,6,7) |
Site 3 |
(9,9,9) |
(2,3,4) |
(1,1,1) |
(5,6,7) |
Site 4 |
(2,3,4) |
(1/5,1/6,1/7) |
(1/5,1/6,1/7) |
(1,1,1) |
TABLE XIX
RELATIVE WEIGHTS
Physical Factor |
|
ri |
|
Site 1 |
0.298 |
0.260 |
0.235 |
Site 2 |
1.654 |
1.702 |
1.747 |
Site 3 |
3.984 |
4.355 |
4.695 |
Site 4 |
0.411 |
0.379 |
0.354 |
Total |
6.347 |
6.696 |
7.031 |
Power of -1 |
0.157 |
0.149 |
0.142 |
Increasing order |
0.142 |
0.149 |
0.157 |
TABLE XX
FUZZY WEIGHTS OF EACH CRITERIA (WI)
Physical Factor |
|
Wi |
|
Site 1 |
0.042 |
0.038 |
0.036 |
Site 2 |
0.234 |
0.253 |
0.274 |
Site 3 |
0.565 |
0.648 |
0.737 |
Site 4 |
0.058 |
0.056 |
0.055 |
TABLE XXI
NON-FUZZY WEIGHT AND NORMALISED WEIGHT
Physical Factor |
Mi |
Ni |
Site 1 |
0.0386 |
0.0386 |
Site 2 |
0.2536 |
0.2539 |
Site 3 |
0.6500 |
0.6509 |
Site 4 |
0.0563 |
0.0563 |
TABLE XXII
WEIGHT OF EACH ALETRNATIVE W.R.T EACH CRIETRIA
|
MA (0.156) |
FE (0.291) |
EF (0.508) |
PF (0.045) |
Site1(MA) |
0.057 |
|
|
|
Site2(MA) |
0.249 |
|
|
|
Site3(MA) |
0.635 |
|
|
|
Site4(MA) |
0.057 |
|
|
|
Site1 (FE) |
|
0.051 |
|
|
Site2 (FE) |
|
0.250 |
|
|
Site3 (FE) |
|
0.650 |
|
|
Site4 (FE) |
|
0.046 |
|
|
Site1 (EF) |
|
|
0.1008 |
|
Site2 (EF) |
|
|
0.171 |
|
Site3 (EF) |
|
|
0.641 |
|
Site4 (EF) |
|
|
0.085 |
|
Site1 (PF) |
|
|
|
0.0386 |
Site2 (PF) |
|
|
|
0.2539 |
Site3 (PF) |
|
|
|
0.6509 |
Site4 (PF) |
|
|
|
0.0563 |
TABLE XXIII
FINAL WEIGHTS OF EACH ALTERNATIVE
Alternatives |
|
Final Weight |
Site 1 |
(0.057x0.156) + (0.051x0.291) + (0.1008x0.508) + (0.0368x0.045) |
0.0765
|
Site 2 |
(0.249x0.156) + (0.250x0.291) + (0.171x0.508) + (0.0386x0.045) |
0.2001 |
Site 3 |
(0.635x0.156) + (0.650x0.291) + (0.641x0.508) + (0.6509x0.045) |
0.6430 |
Site 4 |
(0.057x0.156) +(0.046x0.291) + (0.085x0.508) + (0.0563x0.045) |
0.0679 |
IV. ACKNOWLEDGMENT
We feel proud and find privileged to express deep sense of gratitude to all faculty members of Department Of Civil Engineering, Sandip University, and Nashik for constant encouragement and valuable guidance during completion of this project. The special gratitude towards my project guide, HOD Civil Department, & Principal of Sandip University, Nashik
The objective of this study is to identify, compare and define the optimization of site selection that leads to economical factor and safe use of workers and ultimately the site success in the construction industry. And major three objectives were 1.To determine various criteria for selection of site of residential project.2.To decide the impact of various factors responsible for site selection of residential project by using FAHP modelling.3.And finally suggesting best alternative. Hence from the research we conclude that- 1) Determined various criteria for site selection of residential buildings. Environmental factors, Physical factors, Market analysis, Feasibility Analysis are most important factors considered while selecting site for any residential project. 2) Determined relative importance of each factors and score was given to each factors with pair wise comparision of each factor. And application of F-AHP model was done. 3) According to ranking of each alternative, Alternative 3 have highest weightage. Hence alternative 3 is best suited site for residential project
[1] Mr. Harshal S Patel, Prof. Bhushan Tatar, Deelip B Kalekar (2020) “Framework the Application of MCDM for Residential Land Development Site Selection in Nashik region” [2] Muheeb Majid, Bashir Ahmed Mir (2021) “Landfill site selection using GIS based multi criteria evaluation technique. A case study of Srinagar city, India” [3] Muhammet Deveci, Umit Cali, Dragan Pamucar (2021) “Evaluation of criteria for site selection of solar photovoltaic (PV) projects using fuzzy logarithmic additive estimation of weight coefficients” [4] Ze-hui Chen, Shu-ping Wan, Jiu-ying Dong (2021) “An efficiency-based interval type-2 fuzzy multi-criteria group decision making for makeshift hospital selection” [5] Muhammet Deveci, Nuh Erdogan, Umit Cali, Joseph Stekli , Shuya Zhong (2021) “Type-2 neutrosophic number based multi-attributive border approximation area comparison (MABAC) approach for offshore wind farm site selection in USA” [6] Sunny Joseph Kalayathankal, Joseph Varghese Kureethara, Samayan Narayanamoorthy (2021) “A modified fuzzy approach to project team selection” [7] Phuong H.D. Nguyen, Aminah Robinson Fayek (2021) “Applications of fuzzy hybrid techniques in construction engineering and management research” [8] Bhanu Chander Balusa, Amit Kumar Gorai(2019) “Sensitivity analysis of fuzzy-analytic hierarchical process (FAHP) decision making model in selection of underground metal mining method.” [9] Yunna Wu , Yao Tao, Buyuan Zhang, Shiman Wang, Chuanbo Xu, Jianli Zhou (2019) “A decision framework of offshore wind power station site selection using a PROMETHEE method under intuitionistic fuzzy environment: A case in China” [10] Galih Pambudi, Narameth Nananukul (2019) “A hierarchical fuzzy data envelopment analysis for wind turbine site selection in Indonesia” [11] Sunita Bansal, Srijit Biswas, S.K. Singh (2017) “Fuzzy decision approach for selection of most suitable construction method of Green Buildings” [12] Romualdas Bausys, Birute Juodagalviene (2017) “Garage location selection for residential house by waspas-svns method”
Copyright © 2022 Tejaswini Potdar, Yogita Fulse. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Paper Id : IJRASET44558
Publish Date : 2022-06-19
ISSN : 2321-9653
Publisher Name : IJRASET
DOI Link : Click Here