A multicriteria lifespan energy efficiency approach to intelligent building assessment

This paper presents a multicriteria decision-making model for lifespan energy efficiency assessment of intelligent buildings (IBs). The decision-making model called IBAssessor is developed using an analytic network process (ANP) method and a set of lifespan performance indicators for IBs selected by a new quantitative approach called energy-time consumption index (ETI). In order to improve the quality of decision-making, the authors of this paper make use of previous research achievements including a lifespan sustainable business model, the Asian IB Index, and a number of relevant publications. Practitioners can use the IBAssessor ANP model at different stages of an IB lifespan for either engineering or business oriented assessments. Finally, this paper presents an experimental case study to demonstrate how to use IBAssessor ANP model to solve real-world design tasks

THE POTENTIAL FOR ENERGY CONSERVATION IN THE CONSTRUCTION OF CIVIL WORKS PROJECTS

Energy use, by nature of its economic and political impact is becoming a vital consideration. Energy cost now represents a sizeable portion of the total project cost for civil works projects. This study presents an evaluation of civil works projects to determine if a potential for energy reduction or control exists and the nature of possible opportunities for more efficient utilization of energy resources. Using an earth dam project, a method for estimating the energy requirements is demonstrated and the major energy use activities are identified. A scheme for classification of energy use is presented and a method for establishing the energy content for a construction end product or an in-process component of the construction end product is explained and illustrated by example. Based on the estimate of energy requirements for the earth dam project, the activities representing the most intensive use of energy, earthwork operations, are examined in greater detail to determine the primary factors affecting energy use and productivity. The possibility of a correlation between energy use and productivity is evaluated. Methods for coordination and control of the factors affecting energy use and productivity are discussed. Various opportunities for energy reduction and control applicable to civil works projects are identified and evaluated. Further research efforts necessary to develop the potential for energy conservation in the construction of civil works projects are presented and briefly explained

Construction labor productivity modeling with neural networks

Construction labor productivity is affected by several factors. Modeling of construction labor productivity could be challenging when effects of multiple factors are considered simultaneously. In this paper a methodology based on the regression and neural network modeling techniques is presented for quantitative evaluation of the impact of multiple factors on productivity. The methodology is applied to develop productivity models for concrete pouring, formwork, and concrete finishing tasks, using data compiled from eight building projects. The predictive behaviors of the models are compared with the previous productivity studies. Model results, advantages of the methodology, and study limitations are discussed

Labor productivity modeling with neural networks

Regression analysis has been the common tool used in construction productivity studies, but in recent years, neural networks have been a successful alternative to regression analysis for other problems similar to construction labor productivity modeling. However, the potential capabilities of neural networks for construction labor productivity modeling have not been examined. This paper discusses the development of multivariate productivity models for concrete pouring by regression analysis and neural networks