Automation and Technology in Building and Construction
Summary
Automation and digital technologies are transforming the building and construction sectors by introducing robotics, advanced control systems and data-driven workflows. Autonomous machinery now performs earth-moving, excavation and material handling tasks with minimal human supervision, guided by sensor suites that include cameras, lidar and GPS. Machine-learning techniques refine control policies and adapt to changing site conditions, while spline-based trajectory planners generate smooth, dynamically feasible motion paths. Concurrently, digital tools such as building information modelling (BIM), digital twins and unmanned aerial systems (UAS) enable real-time monitoring, remote inspection and predictive maintenance. Prefabrication and modular construction extend factory-style processes to on-site assembly, improving quality control, accelerating schedules and reducing material waste. These advances promise substantial gains in productivity, safety and sustainability, yet they also pose challenges in terms of high capital costs, integration with legacy workflows and the need to model complex tool–environment interactions. Research efforts continue to bridge these gaps through lightweight robotic manipulators, shared-autonomy paradigms and standardised interfaces that foster human–machine collaboration on dynamic, unstructured worksites.
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Automation and Technology in Building and Construction publication trend
The graph below shows the total number of articles in automation and technology in building and construction across all publications each year (not limited to Nature Index journals).
Technical terms
Autonomous excavation: A fully automated process by which robotic machinery performs digging and loading without direct human control.
Deep reinforcement learning: A machine-learning paradigm in which algorithms learn optimal control policies through trial-and-error interactions with a simulated or real environment.
Adversarial training: A regularisation technique that introduces perturbed or synthetic inputs to improve the robustness and generalisability of learned policies.
Spline trajectory: A piecewise polynomial curve used to represent motion paths that satisfy smoothness, continuity and dynamic feasibility requirements.
Bucket fill factor: The ratio of material volume actually captured in an excavator’s bucket to its maximum theoretical capacity, serving as a key performance metric.
References
- Autonomous Loading System for Load-Haul-Dump (LHD) Machines Used in Underground Mining. Applied Sciences (2021).
- Spline-Based Optimal Trajectory Generation for Autonomous Excavator. Machines (2022).
- Deep Reinforcement Learning With Adversarial Training for Automated Excavation Using Depth Images. IEEE Access (2022).
- A Review of the Advantages and Disadvantages of the Use of Automation and Robotics in the Construction Industry.
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