Applications of artificial intelligence for cost management in construction projects

Applications of artificial intelligence for cost management in construction projects


A primary need of any contractor, or indeed any business, is to be profitable. Controlling cost is not easy and cost estimation is the most important preliminary process in any construction project. It means that control of spend is crucial. We collected some applications of artificial intelligence techniques that can be used for efficient cost management in construction projects.

Cost estimation is the most important preliminary process in any 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. Accurate cost estimation is crucial to ensure the successful completion of a construction project. Estimating construction cost is an example of a knowledge-intensive engineering task.

Different cost models have been developed that contribute to a more efficient financial project management. 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. There are 15 main cost models, which have been published recently and significantly help in managing the technical costs.

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.

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. ML systems have been defined as a construction of a system that can learn from data. 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. This research has opened the door for many proposals that suggest ML as the preferred method to tackle various challenges in the construction industry. Petroutsatou introduced the ANN as a technique for early cost estimation of road tunnel construction. The data collection strategy of this research was based on structured questionnaires from different tunnel construction sites.

Furthermore, Hola and Schabowicz, developed an ANN model for determining earthworks’ execution times and costs. 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. 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.

Knowledge-based systems use logical rules for deducing the required conclusions. The main strengths of KBS are the ability to justify any result and uncomplicated methods. Ji proposed case-based reasoning to prepare strategic and conceptual estimations for construction budgeting. The data for this project were collected from 129 military barrack projects. Choi proposed a cost prediction model for public road planning. The research data had been collected from a total of 207 real public road projects.

Evolutionary systems is a group of intelligent systems concerned with continuous optimization with heuristics. Rogalska proposed a method based on genetic algorithm to deal with the problem of construction project scheduling. De Albuquerque developed a tool for estimating the cost of concrete structures. This tool is developed based on genetic algorithm. The cost has been estimated in all construction phases, such as manufacture, transport, and erection.

Agent-based systems have been considered as one of the main tracks in artificial intelligence, simulating the actions and interactions of autonomous agents with a view of assessing their effects on the system as a whole. Karakas developed a multivalent system that simulates the negotiation process between contractor and client regarding risk allocation and sharing of cost overruns in construction projects. This agent-based system was tested by interviewing eight professionals from the construction industry.

Hybrid system is defined as a collection of techniques used together to solve a specific problem. Usually, researchers use HS to overcome the techniques’ individual limitations. Cheng proposed a hybrid intelligence system for estimating construction cost. This hybrid system was developed based on support vector machine and differential evolution.

This analysis of the ongoing approaches reveal the extended and increasing adoption of AI in the management of civil engineering and construction projects and especially project cost management.

Author: AI Business