This tool is developed based on genetic algorithm. De Albuquerque developed a tool for estimating the cost of concrete structures. Rogalska proposed a method based on genetic algorithm to deal with the problem of construction project scheduling. The research data had been collected from a total of 207 real public road projects.Įvolutionary systems is a group of intelligent systems concerned with continuous optimization with heuristics. Choi proposed a cost prediction model for public road planning. The data for this project were collected from 129 military barrack projects. Ji proposed case-based reasoning to prepare strategic and conceptual estimations for construction budgeting. The main strengths of KBS are the ability to justify any result and uncomplicated methods. Knowledge-based systems use logical rules for deducing the required conclusions. Son developed a hybrid prediction model that combines principal component analysis with a support vector regression predictive model for cost performance of commercial building projects. Basically, this model was developed on the basis of a database created from several studies that were carried out during large-scale earthwork operations on the construction site of one of the largest chemical plants in central Europe. The data collection strategy of this research was based on structured questionnaires from different tunnel construction sites.įurthermore, Hola and Schabowicz, developed an ANN model for determining earthworks’ execution times and costs. Petroutsatou introduced the ANN as a technique for early cost estimation of road tunnel construction. This research has opened the door for many proposals that suggest ML as the preferred method to tackle various challenges in the construction industry.
One of the earliest papers to introduce the benefits and the implementation of ANN in the civil engineering community is published by, Flood and Kartam. ML systems have been defined as a construction of a system that can learn from data. Project cost estimation methods have been categorized into five groups, based on the intelligent technique that is used in each group: machine-learning, knowledge-based systems, evolutionary systems, agent-based system and hybrid systems. Some of the cost models include prediction of the life cycle cost using statistical and artificial neural network methods in conceptual product design, project cost estimation using principal component regression, web-based conventional cost estimates for construction projects using evolutionary fuzzy neural inference model and others. There are 15 main cost models, which have been published recently and significantly help in managing the technical costs. Cost models give a more vivid picture of the costs for the various elements of the project, they help to identify the most appropriate subheadings to monitor the cost reduction and they allow comparison between different approaches to select the optimal solution. Estimating construction cost is an example of a knowledge-intensive engineering task.ĭifferent cost models have been developed that contribute to a more efficient financial project management. Accurate cost estimation is crucial to ensure the successful completion of a construction project. In the construction industry, cost estimation is the process of predicting the costs required to perform the work within the scope of the project. We collected some applications of artificial intelligence techniques that can be used for efficient cost management in construction projects.Ĭost estimation is the most important preliminary process in any construction project. It means that control of spend is crucial. Controlling cost is not easy and cost estimation is the most important preliminary process in any construction project.
Applications of artificial intelligence for cost management in construction projectsĪ primary need of any contractor, or indeed any business, is to be profitable.
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