<?xml version="1.0" encoding="UTF-8"?>
<mods xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.loc.gov/mods/v3" version="3.1" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-1.xsd">
  <titleInfo>
    <title>Developing a model to predict time delay in road construction projects using bayesian networks</title>
  </titleInfo>
  <name type="personal">
    <namePart>Mohammad Almohammad</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
    </role>
    <role>
      <roleTerm type="text">author.</roleTerm>
    </role>
  </name>
  <typeOfResource manuscript="yes">text</typeOfResource>
  <originInfo>
    <place>
      <placeTerm type="code" authority="marccountry">my</placeTerm>
    </place>
    <dateIssued encoding="marc">2020</dateIssued>
    <issuance>monographic</issuance>
  </originInfo>
  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
  </language>
  <physicalDescription>
    <extent>xiii,120 pages : illustrations (some color) ; 30 cm. + 1 CD ROM</extent>
  </physicalDescription>
  <abstract>Time  is  one  of  the  three  leading  indicators  by  which  project  success  measured.  As  Malaysia is looking forward to becoming an advanced nation, efficient infrastructure is  needed. Therefore, completing these projects on time is very important to achieve this  goal.  However,  a  considerable  number  of  construction  projects  in  Malaysia  have  experienced poor time performance. Time delay  is considered to be one  of the major  problems faced by Malaysian construction projects. Thus, this research is carried out to  investigate  the  causes  of  delay  in  construction  projects  and  further  identify  key  risk  indicators that have a significant effect on project duration. Bayesian networks (BNs)  utilized  for  time-delay  prediction  by  which  project  status  in  terms  of  time  can  be  examined.  Scope of this study focus  to federal road projects in Malaysia. A literature  review was undertaken covering construction projects in Malaysia and road projects in  developing countries which resulted in 67 causes of delay divided into 12 groups. Semistructured  interview  with  three  expert  panels  nominated  by  Public  Work  Department  (JKR) conducted to evaluate the delay causes. A total of 56 causes were determined as  relevant  to  Malaysian  road  projects.  Data  collection  was  then  carried  out  using  a  questionnaire  survey  in  which  respondents  were  randomly  selected.  The  targeted  population  was  drawn  from  construction  practitioners  involved  in  road  construction  representing four entities, namely: owner, contractor, sub-contractor and consultant. A  total of 500 copies were distributed and 219 valid responses were received. The data were  then analysed using relative importance index (RII) for risk frequency and impact. Risk  rating (RR) was further established based on the multiplication of both attributes leading  to rank the delay factors from the most to least important. Bayesian networks (BNs) were  employed to develop a prediction model of time delay based on significant factors causing  the  delay.  The  structure  and  parameters  for  the  BNs  model  were  defined  based  on  knowledge of road experts who have been also approached to verify and validate the BNs  outputs. The results indicated that the most significant factors causing delay in federal  road construction projects in Malaysia are: financial difficulties faced by owner/ client,  bad weather conditions, delay in payment for completed work by owner, material price  fluctuation/  increase,  cash  flow  of  contractor  is  insufficient,  equipment  failure  (breakdown), inadequate contractor’s experience, ineffective scheduling and planning of  project  by  contractor,  slow  equipment  movement  and  slow  decision  making.  The  RR  value for top ten delay causes ranges between 13.818 related to financial difficulties faced  by owner/ client and 9.993 related to slow decision making. In addition, the validation of  this model through expert’s  opinion confirms that the BNs  model is adequate to represent  the timeframe of the road projects  and can be used for other construction projects with  minor  modifications.  However,  it  is  recommended  to  apply  more  reliable  methods  to  identify  prior  and  conditional  probabilities  for  the  model  to  obtain  more  reliable  outcomes.</abstract>
  <targetAudience authority="marctarget">specialized</targetAudience>
  <note type="statement of responsibility">Mohammad Almohammad</note>
  <note>Faculty of Civil Engineering Technology</note>
  <note>Thesis (Master of Science) -- Universiti Malaysia Pahang – 2020</note>
  <note>Includes bibliographical references</note>
  <subject authority="lcsh">
    <name type="corporate">
      <namePart>Faculty of Civil Engineering Technology</namePart>
    </name>
  </subject>
  <subject authority="lcsh">
    <topic>Universities and colleges</topic>
    <topic>Dissertations</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Theses</topic>
  </subject>
  <identifier type="isbn">THE0009110(Local)</identifier>
  <recordInfo>
    <recordContentSource authority="marcorg">UMP</recordContentSource>
    <recordCreationDate encoding="marc">220217</recordCreationDate>
    <recordChangeDate encoding="iso8601">20251125110016.0</recordChangeDate>
    <languageOfCataloging>
      <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
    </languageOfCataloging>
  </recordInfo>
</mods>
