An Integrated Environment–Cost–Time Optimisation Method for Construction Contractors Considering Global Warming

Abstract

Construction contractors play a vital role in reducing the environmental impacts during the construction phase. To mitigate these impacts, contractors need to develop environmentally friendly plans that have optimal equipment, materials and labour configurations. However, construction plans with optimal environment may negatively affect the project cost and duration, resulting in dilemma for contractors on adopting low impacts plans. Moreover, the enumeration method that is usually used needs to assess and compare the performances of a great deal of scenarios, which seems to be time consuming for complicated projects with numerous scenarios. This study therefore developed an integrated method to efficiently provide contractors with plans having optimal environment-cost-time performances. Discrete-event simulation (DES) and particle swarm optimisation algorithms (PSO) are integrated through an iterative loop, which remarkably reduces the efforts on optimal scenarios searching. In the integrated method, the simulation module can model the construction equipment and materials consumption; the assessment module can evaluate multi-objective performances; and the optimisation module fast converges on optimal solutions. A prototype is developed and implemented in a hotel building construction. Results show that the proposed method greatly reduced the times of simulation compared with enumeration method. It provides the contractor with a trade-off solution that can average reduce 26.9% of environmental impact, 19.7% of construction cost, and 10.2% of project duration. The method provides contractors with an efficient and practical decision support tool for environmentally friendly planning.

An Integrated Multi-Objectives Optimization Approach on Modelling Pavement Maintenance Strategies for Pavement Sustainability

“…PSO is a population-based optimization tool for solving various complex problems (Eberhart & Kennedy, 1995;Feng et al, 2018). For the problem of the present research, the PSO is applied to search the optimal strategy to maintain the road pavement for sustainable service under various constraints.…”

“…is the updated position in i-th particle at iteration k + 1. c 1 and c 2 are two acceleration coefficients called learning factors, namely cognitive learning factor and social learning factor, respectively; their reasonable values are assigned to 2.5 respectively according to the study (Feng et al, 2018); r 1 and r 2 are uniformly disturbed random numbers within 0 and 1; ()…”

“…where ini w is the initial inertia weight and end w is the end inertia weight when iterating to the maximum evolution algebra. Their reasonable values are assigned as 1.3 and 0.1, respectively, according to studies (Feng et al, 2018;Wang et al, 2017). A generic PSO is depicted in Figure 2.…”

Performance Assessment of Algorithms for Building Energy Optimization Problems with Different Properties

“…Such studies are the major portion of this body of literature, and many algorithms have been used in optimization of the energy generation, building envelope and systems [6]. Such studies can be found in [19][20][21][22]. • Experimental design for the control parameter settings in algorithms.…”

Minimising the Deviation between Predicted and Actual Building Performance via Use of Neural Networks and BIM

“…Such significant environmental concerns have led to the adoption of certain measures to enhance the sustainability of the built environment. Common strategies adopted in the building sector include the use of low-carbon materials [9], recycling [10] and optimising the planning operations of construction [11]. For governments to achieve set targets, there is a need to place emphasis on the delivery of energy efficient buildings, through adoption of current advances in energy efficiency measures proposed for buildings [12].…”