prediction of construction dispute using artificial...

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International Journal of Civil Engineering and Technology (IJCIET) Volume 10, Issue 01, January 2019, pp. 582-594, Article ID: IJCIET_10_01_054

Available online at http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=10&IType=01

ISSN Print: 0976-6308 and ISSN Online: 0976-6316

© IAEME Publication Scopus Indexed

PREDICTION OF CONSTRUCTION

DISPUTE USING ARTIFICIAL NEURAL

NETWORKS TESTIMONIES FROM

INDIAN CONSTRUCTION PROJECTS

Asra Fatima

Research scholar, GITAM (Deemed to be University), Hyderabad, India.

Dr. Bellam Sivarama Krishna Prasad

Head & Professor GITAM (Deemed to be University), Hyderabad, India.

Dr. T.Seshadri Sekhar

Dean& Professor, NICMAR, Hyderabad, India

ABSTRACT

The purpose of this paper is to facilitate construction project team members to

figure out the factors that they must firmly monitor to complete projects without any

dispute and also to predict construction dispute attributes during the course of a

project. The study conferred here is an extension of past research in which 53

project dispute attributes were determined based on expert’s opinion and literature

survey which after analysis resulted in 4 factors which have been used to develop

the dispute attribute prediction model based on artificial neural networks (ANN).

The analyses of the responses led to the conclusion that factors such as Time

phasing and requisite contracting legislation, Project financials and client contractor

partnering, Quality and risk management under ambiguity, Non-responsive owner and

unrealistic contractor rules significantly affect the project dispute attributes. The

best prediction model was found to be a 5-9-1 feed forward neural network based on

back propagation algorithm with a mean absolute percentage deviation (MAPD) of

12.22 percent.

Keywords: Project dispute attributes, Construction works, India, Neural nets.

Cite this Article: Asra Fatima, Dr. Bellam Sivarama Krishna Prasad and Dr.

T.Seshadri Sekhar, Prediction of Construction Dispute Using Artificial Neural

Networks Testimonies From Indian Construction Projects. International Journal of

Civil Engineering and Technology, 10(01), 2019, pp. 582–594

http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=01

Asra Fatima, Dr. Bellam Sivarama Krishna Prasad and Dr. T.Seshadri Sekhar


1. INTRODUCTION

Management of engineering of construction projects is a complex affair (Anderson, 1992). The

participation of the owners, designers, contractors, sub-contractors, specialists, consultants etc.

define the multidisciplinary outlook of the constructions projects. With the increase in extent

of the project, the number of participants in the project is also compelled to increase which

leads to dispute.

The construction sector asserts to account for 11 percent of India’s GDP and is predicted

to be on the rise. Infrastructure, airport, metro rail and power sector projects constitute an

important role in this sector. Cost and time are considered as important elements in most

construction projects. The objectives of the study are set as follows:

• Identify the factors that can be used to predict the project dispute attributes of a

BOT project; and

• Develop a model to predict the project dispute attributes of a BOT project using

artificial neural network (ANN).

2. LITERATURE REVIEW

Some of the past researchers have taken up questionnaire survey approach for collection of

data and employed mathematical tools like AHP, Neural networks and statistical techniques

like factor analysis and multivariate regression, etc., for analysis and drawing conclusions.

Summary of studies in the field of project dispute factors are as follows,

Willaim H.Ross Jr (1988) stated that a survey was conducted to investigate the beliefs of

alternative dispute resolution (ADR) professional about the impact of various situational

factors on the likelihood of negotiating an agreement.

Charles et al(1990) stated that the results of an investigation that was conducted to assess

professional opinion regarding the course of construction disputes, the types of owners most

often involved in disputes, the attorney's role in dispute resolution and the keys for avoiding

disputes.

S. O. Cheun et al (2000) stated that an artificial neural network analysis helps in

determining the important factors affecting the construction dispute resolution processes in

Hong Kong.

K.N. Jha et al (2009) described that project manager always encounters difficulties in

predicting the performance of a construction project.

Ibrahim Yitmen & Ebrahim Soujeri (2010) stated that the purpose of this paper was to

identify the negative impact of change orders on construction productivity, which leads to

decrease in labor efficiency, loss of man hours and considers an ANN Model which estimates

the probable disputes which may be avoided or resolved before litigation occurs.

N. B. Chaphalkar et al (2015) stated that the use of artificial neural networks can be a

cost-effective method to help to forecast the outcome of construction claims and disputes.

3. RESEARCH METHODOLOGY

The methodologies to achieve the objectives of identification of factors which can be used to

predict the dispute attributes of BOT projects and development of prediction model for the

same have been dealt with in this section. This study identified the relative importance of

project dispute attributes in Indian construction industry and also found the effect of these

Factors BOT project. The analyses of the first stage questionnaire survey resulted in the

exclusion of the 53 project dispute attributes identified based on experts opinion and literature

Prediction of Construction Dispute Using Artificial Neural Networks Testimonies From Indian

Construction Projects


survey. The analyses resulted in a number of common factors among them, while a few other

factors emerged predominantly in only one or other criterion. The union of all common and

uncommon factors across two criteria resulted in18 factors. These factors are reproduced in

Table I.. In the survey, the respondents were asked to consider a ‘‘choice’’ project, and were

asked to give the extent of contribution of the 20 factors in a 5-point scale (1-5, with 1

indicating high negative contribution, 0 being no effect and 5 indicating high positive

contribution) on the project dispute of the project. A total of 30 responses were received out of

the150 mailed for the second stage questionnaire survey. With the 18 factors as explanatory

variables and the contribution of these factors on the arousal of dispute as the response variable,

multiple regression was carried out in the study and the conclusions were drawn. Thus, there

is a need to identify the predictor variables among the 18 factors and develop a project dispute

forecasting model.

Table 1.

Table 1.Table 1.

Table 1. Factors affecting dispute in BOT project

Factors Description of factors

F1 Technical inadequacy of the contractor

F2 Arability of Resources

F3 Lack of Construction legislation

F4 Payments Delays

F5 Work availability

F6 Client experience

F7 Escalation payable to the contractor as per the Provision in contract

F8 Escalation due to price variation during contract period

F9 Non retention of refund money

F10 Previous working

F11 Client Budget

F12 Relationship of contractor with Prime contractor

F13 Ambiguities in contract documents

F14 Risk Allocation

F15 Quality of Work

F16 Failure respond in timely manner

F17 Lack of information from design team /Client

F18 Restriction Place don contractor working method



Figure 1. Research methodology as a flow chart.

4. PREDICTION MODEL

To ensure that these factors are the key determinants that affect project dispute

and also to develop the dispute prediction model for the same, ANN methodology

was adopted. ANN has been adopted to consider the factors that alter the different aspects of

DB project performance in Singapore construction projects (Ling et al., 2004a). It is actually a

mathematical model in which the information transformation take place in a number of simple

elements called neurons (nodes), and the signals are transmitted between neurons over




connection links that has a correlated weight with it. Each neuron donate an activation function

(transfer function) to the incoming signal to determine its output signal (Zurada,

1992).McCulloch and Pitts chose a binary threshold unit as a computational modelfor an

artificial neuron as shown in Figure 2. This mathematical model calculate aweighted sum of its

n input signals Xj = (1, 2. . . n) and developed an output of 1, if it is above a certain threshold

or else give an output of 0 (referred in Zurada, 1992).The ANN models choose various

activation functions such as linear, sigmoid, andGaussian functions out of which the sigmoid

is the most commonly used function (Jainetal.1996).To specify mathematically, each neuron j

sums its weighted input as mentioned below:

The output of a neuron, y is a function of its weighted input, mentioned as follows:

Figure 2. MuCuulloch-Pitts model of a neuron

4.1. Network Structure

Feed forward neural network architecture has been chosen for constructing the ANN model. It

consists of an input layer, output layer and hidden layers if required. The input layer provide

input data to the network, and the extent of the input layer, or the number of neurons (nodes)

Which are associated factors for the project dispute criterion in this case. When the hidden

layers are used, the number of nodes in the hidden layer is determined by trial and error. The

hidden nodes on accepting the values of the inputs compute the weighted sum of the inputs and

according to the transfer function compress the values to a limited range (Edwards, 2007).

Table II gives the information of the sigmoid and linear transfer functions used in MATLAB

software.

4.2. Training model

The main goal of neural network training is to decrease the output error by regulating network

weights and biases. Many learning algorithms have been developed for ANN. According to

Jain et al. (1996) back propagation learning algorithm with feed forward network architecture

is most appropriate for predictions. Back propagation is a learning algorithm for a feed forward

network which is an administrated learning process based on error correction learning rule. In

back propagation input and the corresponding output are used to train a network continuously

so that it can approximate a function and correlate input with specific output. Properly trained

back propagation networks provide reasonable answer when given with new inputs in most

cases. The inputs are shipped forward and then the errors are propagated backwards. The

training generally starts with random weights and biases, which are adjusted by the algorithm

for decreasing errors. The regular back propagation in MATLAB is gradient descent algorithm.



Other valuable algorithms in MATLAB are Leven berg-Marquardt, Conjugate gradient

algorithm, resilient back propagation algorithm etc. In general no single algorithm matches all

applications, the constants strategy is to examine with these algorithms and find the most

suitable one for the given application.

4.3. COMPOSITION OF ANN

Once the above particulars are defined, the input–output data is divided towards training and

validation data sets. There are two methods applicable for training and testing the models – the

hold out method and a re-sampling method called random sub-sampling (Edwards, 2007). In

case of the hold out method, the data sets are split into two–usually 2/3 for designing (training

set) and 1/3 for the reckoning the true performance(testing and validation set). While in the

latest, various random train and test experiments are performed on training and testing samples.

And according to Edwards (2007) and Goh (1995) when the number of data sets is adequate

the hold out method proves to be effective. Hence, about two-thirds of the data are used for

training and the remaining for testing and validation. As per the procedure mentioned in Figure

1, the network is trained with training data set, which is halted once the mean square error

(MSE) is satisfactory, and for this trained network the validation process is carried out. If the

mean absolute percentage deviation (MAPD) is found to be satisfactory it is treated as the

validated prediction model.

Table 2. Description of transfer function

4.3. Validation

The objective of operating validation was to test the competence of this trained network for

assessing unknown data sets. Predicted dispute attributes measures derived from the models

are compared with actual dispute attributes of projects and Table3givesthedispute attributes

measures used to validate the prediction models. In Table 3, ‘‘n’’ denotes for the number of

predictions. When the results of the validation are found to be satisfactory, then the model

would be expected to perform well when exposed to new data sets.

4.4. Data analysis

In this section correlation analysis and training and validation of developed ANN models for

prediction of dispute attributes have been discussed. As discussed earlier, 30 data sets received

from the second stage questionnaire survey were available for analysis.




Table 3. Measures to validate the prediction models

4.5. Dispute prediction model.

Among the 72 data sets, 11 user’s data sets were considered for training and the left over 11

data sets were utilized for validation. MATLAB 17 software was used to design and train the

ANN models. A feed forward neural network based on back propagation was considered in the

ANN model training and sigmoid transfer function was used for the nodes. Training algorithm

and transfer functions were chose based on trial and error procedures.

The Levenberg-Marquardt back propagation algorithm (trainlm in MATLAB) and the

transfer function especially hyperbolic tangent function (tansig in MATLAB) for the neurons

in the hidden layers was found to give brisk convergence and better results amidst training and

validation. Summary of the dispute measures such as MAPD, MSE and root mean square error

(RMSE) for the models derived by differing the number of hidden layers and the number of

neurons in each layer is given in Table 4.

Based on the MAPD of the validation data, the 5-9-1 structure has the lowest MAPD among

all others in single hidden layer network structure, The MAPD for the 5-9-1 model developed

to predict dispute occurrence is found to be 12.22417 percent and emerge to be relatively robust

and the RMSE for the same is found to be ± 0.99 percent, which is acceptable (Ling et al.,

2004a). The training curve of 5-9-1 model for ‘‘dispute attributes’’ is shown in Figure3.

Figure 3. Training Curve of ANN “5-9-1project dispute attribute Model”



Table 4. Summary of ANN model network structure

S

no

Number of

hidden

layer

Network

structure Training

cycle MSE

(Train) MAPE (%)

MSE

(Validation) RMSE

(Validation)

1 1 4-1-1 1000 0.25 27.8597 0.762129 0.873

2 2 4-2-1 1000 0.1936 18.4192 0.7921 0.89

3 3 4-3-1 100 0.1024 17.59896 0.7744 0.88

4 4 4-4-1 1000 0.4356 17.19583 0.935089 0.967

5 5 4-5-1 100 0.5625 19.19278 0.8464 0.92

6 6 4-6-1 1000 0.4624 20.96509 0.9604 0.98

7 7 4-7-1 100 0.3844 18.75861 0.81 0.9

8 8 4-8-1 100 0.64 17.11852 0.9604 0.98

9 9 4-9-1 100 0.81 12.22417 0.9801 0.99

10 10 4-10-1 500 0.7569 17.58769 0.9604 0.98

Table 5. Comparison between Actual and predicted for 5-9-1 ANN network

Sno Actual prediction ANN 5-9-1 prediction Absolute (PD) % 1 2 1.9758 1.21 2 4 3.9612 0.97 3 4 4.0005 0.0125 4 4 4.1631 4.0775 5 5 4.9133 1.734 6 4 4.0601 1.5025 7 3 3.0385 1.283333 8 4 4.0899 2.2475 9 5 4.979 0.42

10 3 3.0187 0.623333 11 1 1.1072 10.72 12 3 3.0185 0.616667 13 4 4.0385 0.9625 14 4 3.4461 13.8475 15 4 3.989 0.275 16 5 4.2825 14.35 17 3 3.9527 31.75667 18 5 3.9527 20.946 19 5 4.8862 2.276 20 5 4.8862 2.276 21 5 4.8862 2.276

The factors identified critical for project dispute are Time phasing and requisite contracting

Legislation, Project financials and client contractor partnering, Quality and risk management

under ambiguity, Non-responsive owner and unrealistic contractor rules the developed ANN

models had a feed forward network based on back propagation algorithm and the 5-9-1

structure gave the least MAPD of 12.22 percent considering the single hidden layer models.

Table VI gives the comparison of the actual and predicted performance for the 5-9-1 mode




5. VALIDATION OF FINDINGS THROUGH CASE STUDIES AND

SECOND QUESTIONNAIRE

Details of Case Study: This research work deals with 3 case studies namely NH 44, NH65,

and NH163.

Description of NH 44 Case Study: National Highway 44 (NH 44) is the longest-running

major north–south National Highway in India. It starts from Srinagar and terminates in

Kanyakumari. NH-44 was laid and is maintained by Central Public Works Department This

highway starts from Srinagar and connects several cities and it is officially listed as running

over 3,745 km (2,327 mi) from Srinagar to Kanyakumari. It is the longest national highway

in India. The present study of NH 44 deals with the following route which is handled by

National Highways Authority of India (Project Implementing Unit (PIU) (Nirmal)). It started

on 2-11-2017 and planned to complete on 26-09-2009. There were 6 contractor associated with

the work and the following routes are given below.

Route -1 : It starts from 175.000 Km (Maharastra / AP Border) to Km. 230.000 (Islam

Nagar) on NH 44 in the State of Telangana under North – South Corridor (NHDP Phase II) on

Build, Operate and transfer (Annuity) Basis – Contract Package No. NS2/BOT/AP-6, Km.

230.000 (Islam Nagar) to Km. 278.000 (Kadthal) on NH 44 in the State of Telangana under

North – South Corridor (NHDP Phase II) on Build, Operate and transfer (Annuity) Basis –

Contract Package No. NS2/BOT/AP-7

Route -2: It starts from 230.000 Km. (Islam Nagar) to Km. 278.000 (Kadthal) on NH 44

in the State of Telangana under North – South Corridor (NHDP Phase II) on Build, Operate

and transfer (Annuity) Basis – Contract Package No. NS2/BOT/AP-7

Route-3: It starts from 278.000 Km. (Kadthal) to Km. 308.000 (Armur) on NH-7 in the

State of Andhra Pradesh under North – South Corridor (NHDP Phase II) on Build, Operate and

transfer (Annuity) Basis – Contract Package No. NS2/BOT/AP-8

Route -4: It starts from four laning of Armur – Adloor – Yellareddy section on Nagpur –

Hyderabad of NH 44 from Km 308.000 to Km 367.000 in the State of Telangana on DBFOT

Basis (Package No. NS2/BOT/AP-1)

Route -5: It starts from 4 laning from 367.000 Km to (Adloor Yellareddy) to Km 447.000

& Improvement, Operation & Maintenance from Km 447.000 to Km 464.000 (Gundla

Pochamapalli) of Nagpur – Hyderabad section of NH 44 in the State of Telangana (Package

No. NS-2/BOT/AP-2)

Route -6: It starts from Construction of four laning Km. 464.00 to Km. 474.00 Nagpur-

Hyderabad (NS-23). The following contractors dealt with the following routes and below are

their project cost.

Table 6. Details of list of contractors their respective routes and cost

Sno Name of the

stretch

List of Contractors handling the project as

per the route enlisted above

Project cost(Rs)

1 Route-1

Adilabad Expressway Pvt. Ltd. Rs. 360.42 Cr.

2 Route-2 Patel KNR Heavy Infrastructures Pvt. Ltd. Rs. 518.46 Cr.

3 Route-3 Nirmal BOT Ltd Rs. 271.73 Cr.

4 Route-4 Navayuga Dichpally Tollway Pvt. Ltd. Rs. 490.50 Cr.

5 Route-5 GMR Pochanpalli Expressway Pvt. Ltd Rs. 546.15 Cr.

6 Route-6 You-One Maharia (JV) Rs. 74.89 Crs



The old name of highway was NH 7 connecting Varanasi to kanyakumari passing through

Uttar Pradesh, Madhaya Pradesh, Maharastra, Telangana, Andhra Pradesh, Karkataka and

Tamil nadu. The present study deals with the highway details of telangana state. There was an

increase in the traffic flows which led to congestions and reported more accident cases since

1998 to 2006, so the government took the decision of widening the national highway 7 from 2

lane to 6 lanes which was the renamed to NH44. This BOT project had many claims because

of the effect of identified factors they are as follows.

Adilabad Expressway Pvt. Ltd. commenced the work on 02.11.2007 during the period of

execution they claimed 48, 06, 35,404.00 rs against NHAI due to changes in design, idling of

plant and resources and their loss of overhead and fixed costs during the period of idling of

resources, which falls in the category of Non responsive owner and time phasing and requisite

contractor legislation (4th identified factor). Patel KNR Heavy Infrastructures Pvt. Ltd

commenced the work on 02.03.2008 during the period of execution they claimed

1,27,89,65,505.00 against NHAI due to Compensation For Non-Handing Over Of Land and

Claims For Arbitration Cost. which falls in the category Non responsive owner and time

phasing and requisite contractor legislation(4th identified factor).GMR Pochanpalli

Expressway Pvt. Ltd started the work on 26.09.2006 during the execution period they claimed

1152642326 rs against NHAI due to project financial and client contractor partnering and

quality and risk management under ambiguity(3rd identified factor).. You One - Maharia (JV)

commenced the work on 30.8.2001 during the execution period they claimed 61, 89, 45,600.00

rs due to Non responsive owner and unrealistic contractor rules and 3,42,00,44,032.00 rs for

project financial and client contractor partnering and quality. (2nd and 4th identified factor).

For validating the above case study details sample response of second questionnaire from

the companies is also considered and shown below and Spearman correlation is also performed

which is used to identify the factors that have high degree of association with the project dispute

factors using Statistical package for social sciences (SPSS 23.0). Therefore the 4 identified

factors were the key factors responsible for delays and claims for NH44.

5.1. Description of NH 163 Case Study

Four Laning of Hyderabad –Yadgiri section of NH 163(Old NH 202) from Km 18.600 to Km

54.000 in the State of Telangana under NHDP Phase –III on Design, Build, Finance, Operate

and Transfer (DBFOT) Toll Basis Package No. NHDP-III/BOT/AP-04 it is entrusted to NHAI

PUI Warangal and the project cost is Rs. 488 Cr. M/s. Hyderabad Yadgiri Tollway Pvt. Ltd

was selected as contractor for Design, Build, Finance, Operate and Transfer (DBFOT) for a

length of 38.6 km highway and for a concession period of 23 years including 650 days of

Construction period. They commenced the project on 30.07.2010 during the execution period

they claimed 72,29,92,708.00 Rs due to Compensation for Non-Handing Over Of Land and

payments due on account of utility shifting ,which falls in the category of Non responsive

owner and time phasing and requisite contractor legislation.

Therefore the 1st and 4th identified factors were the key factors responsible for delays and

claims for NH163.

5.2. Description of NH 65 Case Study

GMR infrastructure limited led consortium was selected for NH 65 Hyderabad to Vijayawada

highway project on built operate and transfer (Toll) basis through international competitive

bidding route, the project faced a lot of challenges due to heavy built up area but connected on

schedule by good time phasing, client contractor partnering, quality and lastly project

management skills.




They started the project on 9th October 2009 with NHAI as client and the project cost of

1740rs for a length of 181.5km and for a concession period of 15 years-4 lanes and 25years-

for 6 lanes, Toll revenue is share with NHAI during the execution of project there no claims,

no delays were encountered and so the project could completed the within the budget and time.

Therefore NH65 was a successful highway project with no delays and claims. This could

be because of good time phasing, client contractor partnering, quality and lastly project

management skills. Not all BOT projects will have delays but the key factors identified from

the study were found responsible for delays and claims for NH44 and NH 163.

6. SPEARMAN’S CORRELATION ANALYSIS

Spearman’s correlation analysis was used to identify the factors that have a high degree of

correlation with the dispute attribute using Statistical package for social sciences (SPSS 23.0).

Correlation analysis was preferred as correlation coefficient measures the strength of

association between a pair of random variables. Based on the results of the spearman’s

correlation analysis (Table VI) the factors such as Time phasing and requisite contracting

legislation, Project financials and client contractor partnering, Quality and risk management

under ambiguity, Non responsive owner and unrealistic contractor rules were found to be

significantly correlated.

Table 7. Spearmen correlation for Project dispute attributes

Pd

a8

Pd

a4

0

Pd

a3

4

Pd

a4

Pd

a4

5

Pd

a4

6

pd

a5

0

pd

a5

1

pd

a2

0

pd

a4

8

pd

a4

7

pd

a1

9

pd

a1

4

pd

a1

5

pd

a1

0

pd

a2

9

pd

a2

7

pd

a2

3

O

Pd

a8

1

Pd

a4

0

.25

2

1

Pd

a3

4

-.01

8

-.17

3

1

Pd

a4

-.02

2

-.16

5

.62

4**

1

Pd

a4

5

-.01

4

.85

4**

-.30

5

-.35

6

1

Pd

a4

6

.15

5

.78

4**

-.38

3*

-.40

3*

.84

2**

1

pd

a5

0

.14

0

.52

8**

.33

8

.46

7**

.30

3

.10

2

1

pd

a5

1

.21

0

-.19

7

.52

8**

.61

2**

-.39

0*

-.42

7*

.34

5

1

pd

a2

0

.02

1

-.23

8

.42

2*

.75

5**

-.43

6*

-.38

1*

.33

3

.49

9**

1

pd

a4

8

-.09

6

-.09

6

-.16

6

-.41

1*

.00

5

.20

5

-.38

4*

-.29

9

-.29

4

1

pd

a4

7

-.09

2

.57

9**

-.56

1**

-.60

3**

.67

4**

.71

6**

-.24

2

-.67

8**

-.63

1**

.21

7

1



pd

a1

9

-.22

4

-.66

9**

.45

3*

.36

2*

-.61

1**

-.58

1**

-.24

9

.44

8*

.25

0

.27

9

-.50

6**

1

pd

a1

4

-.02

7

-.37

2*

.49

1**

.49

4**

-.48

6**

-.68

6**

.31

1

.65

1**

.47

2**

-.35

2

-.65

1**

.33

0

1

pd

a1

5

-.31

7

-.80

4**

.13

8

.11

4

-.62

5**

-.52

3**

-.47

4**

.05

6

.11

5

.35

9

-.35

2

.83

9**

.13

5

1

pd

a1

0

.04

5

.68

6**

-.41

8*

-.45

8*

.76

8**

.82

6**

.11

1

-.58

8**

-.40

1*

.19

1

.77

9**

-.56

8**

-.54

6**

-.36

2*

1

pd

a2

9

-.13

3

-.71

3**

.22

9

.16

8

-.65

2**

-.53

3**

-.50

0**

.22

4

.11

0

.21

0

-.29

4

.80

2**

.17

7

.84

2**

-.38

4*

1

pd

a2

7

.23

9

.04

2

.13

2

-.10

3

.07

6

.03

8

-.33

4

-.04

3

-.17

5

.20

9

.11

0

.05

2

-.07

6

-.11

6

-.16

3

.01

8

1

pd

a2

3

.15

3

-.09

2

.47

6**

.69

3**

-.38

1*

-.29

4

.43

9*

.29

8

.56

4**

-.27

5

-.48

0**

.05

0

.32

8

-.02

4

-.25

1

.00

8

-.20

3

1

O

.21

6

.90

**

-.23

1

-.17

3

.84

4**

.76

9**

.53

1**

-.27

6

-.17

8

-.17

6

.56

4**

-.80

3**

-.34

9

-.85

4**

.70

7**

-.80

2**

-.00

9

-.04

2

1

7. SUMMARYAND CONCLUSIONS

The purpose of this study was to identify the critical factors that affect the project dispute

criterion which could be used to develop a project dispute prediction model. The conclusions

derived from the study are given below: Models to predict the project dispute criterion have

been developed through correlation studies (see Table IV) and with the usage of ANN in

MATLAB. When project dispute attributes is the prime objective, the following are the factors

that have been found significant Time phasing and requisite contracting legislation, Project

financials and client contractor partnering, Quality and risk management under ambiguity, Non

responsive owner and unrealistic contractor rules. This is consistent with the findings of Ling

et al. (2007) and Tam and Le (2007) that adequate training and good coordination are a must

for enhanced performance. The developed ANN models had a feed forward network based on

back propagation algorithm and the 5-9-1 structure gave the least MAPD of 12.22percent.The

high degree of predictive ability shows that the factors identified from correlation analysis are

correct and can be used to predict the project dispute attributes.

The project professionals can concentrate on certain factors instead of handling all the

factors at the same time to achieve the desired objectives. The study may be helpful to the

project manager and his team to predict the project dispute attributes during its course. Further

study In order to provide simple access to the developed prediction model, an interface may be

developed to promote data input and also to know the predictions. As such the user interface is

being developed and once completed it would be able to help project team members to feed the

factor inputs directly so as to know the corresponding project dispute attributes.




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Journal of Civil Engineering (2015) 19(1):10-16

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