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Multistep prediction for earthworks unloading duration: a fuzzy Att-Seq2Seq network with optimal partitioning and multi-time granularity modeling

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Abstract

Unloading activities directly impact the progress of earthmoving and filling projects, and a reliable multistep-ahead unloading duration prediction could help optimize equipment scheduling and improve operational efficiency. However, unloading prediction is greatly challenging owing to the complex uncertainties and nonlinearities implied in the unloading process, as well as the difficulty of modeling long-term temporal dependencies. Thus, this study devises a new fuzzy sequence-to-sequence network for unloading time forecasting. First, a quantization error-improved information granulation method is exploited to establish the fuzzy partition function. The global and localized distribution characteristics of unloading time are utilized to adaptively optimize the number and distribution of nonuniform fuzzy intervals. Then, periodic and recent branches were developed to model the variation of unloading time in multiple temporal granularities. In each branch, based on the encoder–decoder structure, the underlying gated recurrent units learn the sequence features collaborating with the attention mechanism to capture implicit long-term dependencies, mitigating the effects of error accumulation in multistep forecasting. Finally, the temporal information of different granularities is fused to form the final prediction. We evaluate the proposed model using an unloading dataset from a heavy infrastructure project in southwest China. We conducted geo-fencing and unloading operation analysis to extract the unloading information of different construction areas. The experimental results show that our method can generate high-quality multistep predictions for unloading duration, and exhibits superior performance compared with baseline models. The novel approach has great potential to support earthwork management in complex environments.

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Data availability

The data that support the findings of this study are available from the corresponding author, Jia Yu, yujia@tju.edu.cn, upon reasonable request.

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Funding

This research was funded by the Yalong River Joint Funds of the National Natural Science Foundation of China (Grant no. U1965207).

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  1. State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin, 300350, China

    Yunuo Zhang, Xiaoling Wang, Jia Yu, Tuocheng Zeng & Jiajun Wang

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  1. Yunuo Zhang
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Zhang, Y., Wang, X., Yu, J. et al. Multistep prediction for earthworks unloading duration: a fuzzy Att-Seq2Seq network with optimal partitioning and multi-time granularity modeling. Neural Comput & Applic 35, 21023–21042 (2023). https://doi.org/10.1007/s00521-023-08883-5

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