POTENTIAL APPLICATIONS OF NEURAL NETWORKS IN CONSTRUCTION

During the past decade, several engineering disciplines, including construction, have embarked on developing "intelligent" decision support systems based on artificial intelligence (AI) techniques, including expert systems, symbolic knowledge representation, and logic programming. These systems attempt to capture the domain experts' intelligent behaviour and reasoning process utilized in decision-making, without regard to the underlying mechanisms producing that behaviour. This approach involves describing behaviours, usually with rules and symbols. In contrast, neural networks (NN), another Al-based technique that has been pursued on a large scale during the past few years, does not describe behaviours but rather imitate them. Neural networks are particularly superior to traditional expert systems in providing timely solutions based primarily on analogy with previous experience, rather than reasoning or computation. As such, neural networks have a great potential to work either as a supplement or as a complement to algorithmic and (or) other Al-based systems, providing more suitable tools for solving the industry ill-structured problems. This paper describes several characteristics of neural networks and outlines the advantages and limitations of commonly used NN paradigms. Potential applications of each paradigm in construction are identified. Two example applications are provided to demonstrate the problem-solving capabilities of neural networks: (i) estimation of hourly production rate of an excavation equipment; and (ii) estimation of productivity level for a construction trade. Future possibilities of integrating neural networks with other problem-solving techniques are described.