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<p>During asphalt pavement construction, compaction is conventionally assessed by coring to measure density, with the destructive and time-consuming nature of this method limiting its frequent application. In contrast, intelligent compaction (IC) technologies enable real-time quality control and have driven significant advances in asphalt pavement construction. Building on these advancements, this study presents a comprehensive review of research published between 2010 and 2025, synthesizing developments in IC and integrated multi-sensor systems, which combine data from accelerometers, temperature sensors, and GPS to monitor compaction quality and spatial variability, and highlighting their implications for pavement performance. Recent developments demonstrate the growing use of machine learning (ML) for processing IC data to enhance predictions of stiffness, modulus, and density. Meanwhile, Digital Twin (DT) frameworks have emerged as powerful tools for data-driven decision-making for preventive maintenance and rehabilitation of pavements. The integration of IC with Building Information Modeling (BIM) and the Internet of Things (IoT) further enhances construction management and quality monitoring. Additionally, the fusion of LiDAR and camera data offers millimeter-level precision in paving operations. Despite these advances, key research gaps persist, particularly in establishing quantitative relationships between Intelligent Compaction Measurement Values (ICMVs) and asphalt modulus evolution, and in understanding the effects of temperature variation and underlying layer conditions. This review concludes that continued innovation in Artificial intelligence (AI)-driven modeling, multi-sensor fusion, and mechanistic interpretation is essential to fully realize the potential of intelligent, data-centric pavement construction.</p>

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Computational Methods for Intelligent Compaction in Asphalt Pavement Construction: A State-of-the-Art Review

  • 期刊论文

  • 发表日期：

    2026年4月4日

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  • Kamal Nasir Ahmad,
  • Xianhua Chen,
  • Qing Lu

摘要

During asphalt pavement construction, compaction is conventionally assessed by coring to measure density, with the destructive and time-consuming nature of this method limiting its frequent application. In contrast, intelligent compaction (IC) technologies enable real-time quality control and have driven significant advances in asphalt pavement construction. Building on these advancements, this study presents a comprehensive review of research published between 2010 and 2025, synthesizing developments in IC and integrated multi-sensor systems, which combine data from accelerometers, temperature sensors, and GPS to monitor compaction quality and spatial variability, and highlighting their implications for pavement performance. Recent developments demonstrate the growing use of machine learning (ML) for processing IC data to enhance predictions of stiffness, modulus, and density. Meanwhile, Digital Twin (DT) frameworks have emerged as powerful tools for data-driven decision-making for preventive maintenance and rehabilitation of pavements. The integration of IC with Building Information Modeling (BIM) and the Internet of Things (IoT) further enhances construction management and quality monitoring. Additionally, the fusion of LiDAR and camera data offers millimeter-level precision in paving operations. Despite these advances, key research gaps persist, particularly in establishing quantitative relationships between Intelligent Compaction Measurement Values (ICMVs) and asphalt modulus evolution, and in understanding the effects of temperature variation and underlying layer conditions. This review concludes that continued innovation in Artificial intelligence (AI)-driven modeling, multi-sensor fusion, and mechanistic interpretation is essential to fully realize the potential of intelligent, data-centric pavement construction.