From Activity Recognition to Simulation: The Impact of Granularity on Production Models in Heavy Civil Engineering

• Published in: Algorithms Vol. 16; no. 4; p. 212
• Main Authors: Fischer, Anne, Beiderwellen Bedrikow, Alexandre, Tommelein, Iris D., Nübel, Konrad, Fottner, Johannes
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2023
• Subjects: Activity recognition; Artificial intelligence; Bored piles; Boring machines; Case studies; Civil engineering; Classification; Construction equipment; Construction industry; Construction sites; deep learning; digital twin in construction; Digital twins; Digitization; Discrete event systems; discrete-event simulation; Embedded systems; heavy civil engineering equipment; Industry 4.0; Internet of Things; Machine learning; Modelling; Monte Carlo simulation; Motion perception; process reference model; Recurrent neural networks; Simulation; Software; Germany
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a16040212

As in manufacturing with its Industry 4.0 transformation, the enormous potential of artificial intelligence (AI) is also being recognized in the construction industry. Specifically, the equipment-intensive construction industry can benefit from using AI. AI applications can leverage the data recorded by the numerous sensors on machines and mirror them in a digital twin. Analyzing the digital twin can help optimize processes on the construction site and increase productivity. We present a case from special foundation engineering: the machine production of bored piles. We introduce a hierarchical classification for activity recognition and apply a hybrid deep learning model based on convolutional and recurrent neural networks. Then, based on the results from the activity detection, we use discrete-event simulation to predict construction progress. We highlight the difficulty of defining the appropriate modeling granularity. While activity detection requires equipment movement, simulation requires knowledge of the production flow. Therefore, we present a flow-based production model that can be captured in a modularized process catalog. Overall, this paper aims to illustrate modeling using digital-twin technologies to increase construction process improvement in practice.