Internet of things data analysis methodology for hydraulic structures of hazard classes III and IV based on mathematical experiment planning
DOI:
https://doi.org/10.55287/22275398_2026_59_5Keywords:
hydraulic structures, hazard class, Internet of things, mathematical experiment planning, fractional factor experiment, regression modelAbstract
This study presents a methodology for analyzing (evaluating) Internet of Things (IoT) data for hydraulic structures of hazard classes III and IV, based on mathematical experimental design for the seepage flow function. The relevance of this work stems from the need to ensure the safety of small hydraulic structures under strict budget constraints, when traditional monitoring systems with a large number of sensors are economically impractical. An approach is proposed using a fractional factorial 2k-p experiment for the optimal placement of IoT sensors, which reduces their number by 40–60% compared to a uniform grid without loss of information content. A regression model for the response function—seepage flow—is developed, including main effects and pairwise interactions of factors (water level, temperature, operating time, precipitation). The pre-emergency condition criterion is based on residual analysis with thresholds differentiated by hazard class: for class III – 2σ (pre-emergency) and 3σ (emergency), for class IV – 3σ and 4σ, respectively. Monitoring the dynamics of pairwise interaction coefficients is used as an additional diagnostic indicator. An algorithm for analyzing data obtained using IoT has been developed. The methodology can be implemented by owners of existing hydraulic structures quickly and without significant capital expenditures.
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