^ A Multi-objective Decision-support Model for Selecting Environmentally Conscious Highway Construction Methods | Structurae
  • DE
  • EN
  • FR
  • International Database and Gallery of Structures


A Multi-objective Decision-support Model for Selecting Environmentally Conscious Highway Construction Methods


Medium: journal article
Language(s): Latvian
Published in: Journal of Civil Engineering and Management, , n. 6, v. 21
Page(s): 733-747
DOI: 10.3846/13923730.2014.893915

The construction industry has a considerable share in overall resource and energy consumption. Consequently, decision-makers try to achieve environmentally conscious construction by integrating environmental objectives into the selection of construction elements. Due to the complexity of construction projects, it is a known challenge to provide an effective mechanism to select the most feasible construction methods. Thus, it is crucial to learn the interdependency between various resource alternatives, such as material and equipment type, under various project conditions like unavailability of resources. An analytic network process (ANP) was used in this study to construct a decision model for selecting the most feasible construction method. Data collected via interviews with highway construction experts were used to model the dependency between decision parameters, such as project conditions and resource performance indicators. The proposed ANP model output the relative importance weights of decision parameters so that they can be used to identify environmentally conscious construction methods. The proposed mechanism is a valuable asset for construction decision-makers especially when their ability to select construction methods is limited by project constraints. Although the model was tested in a highway project in this paper, it can be further extended to benefit building construction and sustainable decision-making problems.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.3846/13923730.2014.893915.
  • About this
    data sheet
  • Reference-ID
  • Published on:
  • Last updated on: