Construction Automation Overview

Principles

Definition

Construction automation is an academic concept and research field focused on the application of advanced technologies to automate tasks and processes within the construction industry. It investigates the integration and deployment of robotics, artificial intelligence, machine learning, data analytics, and other automated systems to improve efficiency, safety, quality, and productivity across the construction lifecycle, from planning and design through execution and maintenance.

Ontological type

Core Technologies

Automation Applications

Implementation Challenges

Key developments

Key Figures

Resource-Centric Simulation era

Iris D. Tommelein [1] is a prominent scholar associated with the University of Washington [2] and the University of California, Berkeley [3] during the Resource-Centric Simulation era (1993–2006). Her key contribution in this era centers on interactive dynamic layout planning, exemplified by the 1993 paper Interactive Dynamic Layout Planning [4], which advanced constraint-aware, resource-centric approaches to construction planning and demonstrated how interactive tools could reduce modeling effort. Iris D. Tommelein [1] contributed during this era by helping institutionalize reusable components and interfaces that enabled scalable simulation experiments and a tighter coupling between planning artifacts and on-site constraints. Iris D. Tommelein [1] continued to influence the development of resource-first representations and constraint-aware scheduling, laying technical primitives that later supported model-based integration and automated control workflows.

Model-Driven Automation era

Feniosky Peña‑Mora [1] is a prominent figure associated with Stanford University [3] and Columbia University [4] during the Model-Driven Automation era. His key contribution in this era is the 2012 paper Automated Progress Monitoring Using Unordered Daily Construction Photographs and IFC-Based Building Information Models [6], which demonstrates how unordered daily photos can be fused with IFC-Based BIM to track on-site progress and feed automated control loops, improving predictability and reducing rework. Silvio Savarese [2] is a researcher affiliated with Stanford University [3] and the University of Washington [5] during the same era. As a co-author of the same 2012 paper Automated Progress Monitoring Using Unordered Daily Construction Photographs and IFC-Based Building Information Models [6], his work contributed to enabling model-driven automation by applying AI-based analysis to photographs and BIM data, supporting automated progress measurement and coordinated actuation on site.

Digital Twin Ecosystems era

Mehrdad Arashpour [1] is a scholar affiliated with Monash University [2] and RMIT University [3] in the Digital Twin Ecosystems era (2017–2023). His key contributions in this era center on machine learning and smart vision approaches for building and construction, as reflected in the 2022 paper Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications [4], which illuminated how AI can enhance perception, analytics, and automation across design, fabrication, and operations. This work provided foundational methods for real-time data interpretation and predictive insight within digital twin workflows, enabling more responsive scheduling and risk-aware decision support across lifecycle stages. By demonstrating scalable AI and vision-enabled capabilities in construction contexts, Mehrdad Arashpour [1]'s contributions helped anchor interdisciplinary integration and standardization efforts in this era.