Intelligent Construction Monitoring Systems: Foundations, Architectures, and Future Directions

Document Type

Article

Source of Publication

Intelligent Construction Monitoring Systems Real Time Safety Environmental Prediction and Risk Management

Publication Date

2-4-2026

Abstract

The rapid digital transformation of the construction industry has driven the adoption of intelligent construction monitoring systems to enhance safety, productivity, and risk management in complex and dynamic project environments. These systems integrate advanced sensing technologies, artificial intelligence, Internet of Things (IoT) platforms, and data analytics to enable real-time observation, prediction, and decision-making throughout the construction lifecycle. This chapter presents a comprehensive overview of the foundations, system architectures, and emerging trends in intelligent construction monitoring systems. It examines key enabling technologies, including computer vision, machine learning, digital twins, and cloud–edge computing frameworks, and discusses how these components are orchestrated to support real-time safety monitoring, environmental impact prediction, and proactive risk mitigation.

ISBN

[9798337392455, 9798337392462, 9798337392479]

Publisher

IGI Global Scientific Publishing

First Page

1

Last Page

48

Disciplines

Computer Sciences

Keywords

Industry 4.0 (0.55), Key (lock) (0.54), Computer science (0.46), Safety monitoring (0.46), Systems engineering (0.44), Engineering (0.44), Internet of Things (0.43), Intelligent decision support system (0.42), Construction industry (0.39), Analytics (0.38), Risk analysis (engineering) (0.38), Intelligent sensor (0.34), Digital transformation (0.33), Risk management (0.33), Intelligent environment (0.32), Construction engineering (0.29), The Internet (0.29), Big data (0.29), Condition monitoring (0.28), Management system (0.27), Emerging technologies (0.26), Data science (0.26), Cloud computing (0.26), Construction management (0.26), Knowledge-based systems (0.25), Engineering management (0.25), System lifecycle (0.25)

Scopus ID

105033832398

Indexed in Scopus

yes

Open Access

no

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