Machine Learning Approaches for Construction Cost Estimation

Machine learning techniques are transforming the practice of estimating construction costs by automating pattern recognition in historical project data, improving accuracy, and quantifying uncertainty. Supervised learning models such as artificial neural networks and support vector machines extract nonlinear relationships between design parameters and costs, while ensemble methods—including random forests and boosting regression trees—combine multiple learners to enhance generalisation. Natural language processing has been applied to parse unstructured cost descriptions and assign work breakdown structure codes automatically. Metaheuristic algorithms like particle swarm optimisation are used both to tune model parameters and to explore complex design–cost trade-offs. Interval estimation frameworks built on support vector machines provide point estimates accompanied by confidence bounds, addressing the inherent uncertainty of the construction environment. Collectively, these advances enable rapid preliminary estimates in early project phases, support value engineering, and offer decision-support tools for stakeholders, thereby reducing cost overruns and enhancing global competitiveness in the built environment sector.

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Machine Learning Approaches for Construction Cost Estimation publication trend

The graph below shows the total number of articles in machine learning approaches for construction cost estimation across all publications each year (not limited to Nature Index journals).

Technical terms

Artificial neural network (ANN): A computational model inspired by biological neural structures, capable of approximating complex nonlinear relationships between inputs and outputs.

Support vector machine (SVM): A supervised learning algorithm that constructs hyperplanes in a high-dimensional space to perform regression or classification with maximised margin.

Ensemble learning: A methodology that combines multiple individual models to produce a single, more accurate and stable prediction.

Boosting regression tree: An ensemble technique that sequentially fits decision trees to the residuals of preceding models, improving predictive performance.

Particle swarm optimisation (PSO): A population-based optimisation algorithm inspired by collective movement patterns in nature, used for tuning model parameters or solving design trade-off problems.

Natural language processing (NLP): A set of algorithms for analysing and deriving structured information from unstructured text data.

Work breakdown structure (WBS): A hierarchical decomposition of a construction project into discrete cost and scope categories for ease of management and estimation.

References

1. An automated machine learning approach for classifying infrastructure cost data. Computer-Aided Civil and Infrastructure Engineering (2023).
2. Interval estimation of construction cost at completion using least squares support vector machine. Journal of Civil Engineering and Management (2014).
3. Application of Boosting Regression Trees to Preliminary Cost Estimation in Building Construction Projects. Computational Intelligence and Neuroscience (2015).
4. Particle Swarm Optimization Based Approach for Estimation of Costs and Duration of Construction Projects. Civil Engineering Journal (2020).
5. Construction cost estimation of reinforced and prestressed concrete bridges using machine learning. Journal of the Croatian Association of Civil Engineers (2021).