Can Artificial Intelligence Transform the way we Estimate the Cost of Construction Projects?

Broadly, AI can assist in the development of company-specific and industry-wide benchmarks that can reduce optimism bias and strategic misrepresentation. Specifically, we envision that AI can help the estimating process in the following ways:

1.Classify and categorize unclassified cost data

2.Develop statistical models for parametric estimating

3.Identify reference class projects

Classify and categorize unclassified cost data

Organizations have an abundance of granular cost data that is generally unclassified and unstructured rendering it unusable. To utilize this type of historical data, it is essential that the data is classified using a high-level cost classification system such as the International Construction Measurement Standards (ICMS). Unclassified cost data is generally available in a variety of electronic formats. AI tools that perform natural language processing can be used to classify this type of data into a predefined cost classification system such as ICMS. A natural language classifier can be first trained and tested using historical cost data. Once the system is trained, it can process large volumes of cost data from historical projects and classify it into cost categories and cost groups (as shown in the figure below). After the data is classified, several important analyses including cost benchmarking can be performed.

Develop Statistical Models for Parametric Estimating

Artificial neural networks (ANN) are a form of AI that is inspired by the human nervous system; more precisely the neurons. It is one of the main tools used in machine learning. Graphically explained in the figure below an ANN consists of an input layer, an output layer, and one or more hidden layers. All these layers consist of neurons that are connected by adjustable weights. The input layer receives inputs, and the output layer provides an answer to the problem that is being solved using the ANN. It is a good tool for parametric estimation where a statistical model between historical data and other variables is used to estimate the cost of a project. For example, if we have historical cost data of 100 similar buildings that consist of key project attributes such as height of the building, constructed area, type of construction, class of building, building use, etc. along with the actual cost of the building we can design an ANN to predict the cost of a new building. Out of the sample data, we will use most of the data to train the ANN so that it ‘learns’ (by repeatedly comparing predicted output with the known output of the training data set and constantly adjusting the connection weights until predictions become accurate) to predict cost and then use the remaining sample data to test and validate the ANN. After this process of supervised learning and testing, we can use input for a new building project to predict the cost by using the trained ANN.

Identify Reference Class Projects

For analogous estimating it is vital to identify a comparable historical project that can be used to estimate the cost of a new project. Identifying and recalling the comparator project can be enhanced by using case-based reasoning (CBR) which is another form of AI. In CBR a historical database of projects is used to ‘retrieve’ a similar project by matching a set of project attributes using a metric called similarity index. The project estimation information and knowledge from the retrieved project are ‘reused’ to arrive at a preliminary estimate. The data from the retrieved data is called the ‘suggested’ cost data, and the resulting solution is called the suggested solution. This solution is further improved via the ‘revise’ process which leads to the confirmed data for the cost estimate. The new project itself is ‘retained’ in the historical project database as a learned case or project for future use.

What does this Mean?

In conclusion, the authors envision that the overall estimation process and its accuracy can be enhanced by using historical cost data and analytics powered by AI. AI-based predictive tools can harness the power of cost data that idly sits in an organization’s estimating systems, cost management systems, and procurement systems. These tools can inform early-stage cost advice for new projects, provide accurate and precise cost estimates, help in monitoring and controlling project costs, and at times assist in forensic cost analysis. Market conditions and economic factors such as commodity prices can also be brought into the mix to allow estimators to consider an outside view.

While this is a promising prospect, some implementation challenges remain. Foremost amongst these is the hesitation amongst project proponents to share cost data. While the data may be there the willingness to share the data may be low. Initially, data internal to the organization can be collected and used for AI-powered analytics. For industry-wide benchmarks and related solutions, clients must lead the way. For example, clients such as the UK Department for Transport have started a benchmarking program under the aegis of a program entitled transport infrastructure efficiency strategy (TIES). Professional bodies such as the RICS can play an important role in data collection, validation, and curation since they can act impartially and independently supporting the public good. Such industry-wide initiatives can provide practitioners with the support they need to conduct important analysis such as value-for money, lifecycle costing, and performance benchmarking.

This, in turn, will open a new debate relating to the future of our profession. What will this mean for the professionals especially those working in the quantity surveying, project management, and related professions? Will we have to retrain and reeducate to embrace AI and the power that it brings? There is little doubt AI will enhance professional skills and professional judgment so that we can provide sound advice to our clients.

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