Abstract
Large-scale mine water inrush accidents can be reduced in number and severity by better understanding the causes of these accidents and the internal relationships between the various causal factors. Based on 70 major events from 2010 to 2020, we constructed a multi-level hierarchical structural model using DEMATEL and ISM techniques, and then mapped the model to a BN network, using posterior probability computation to enable reverse diagnosis reasoning recognition. The results indicated that inadequate knowledge of water disasters and inadequate hydrogeological detection are the key factors, with posterior probabilities of 91 and 82%, and the highest centre degrees of 5.400 and 4.917. The fundamental contributing factors are disordered safety management and imperfect supervision, with causal degrees of 1.402 and 2.038. It is clear from the model’s causal structure and maximum causal chain analysis that these factors are quite likely the cause of most water inrush accidents. Paying closer attention to these fundamental factors can help to more effectively control these accidents and minimize losses.
Resumen
Los accidentes de irrupción de agua en minas a gran escala pueden reducirse en número y gravedad si se comprenden mejor las causas de estos accidentes y las relaciones internas entre los diversos factores causales. Sobre la base de 70 eventos importantes entre 2010 y 2020, construimos un modelo estructural jerárquico de varios niveles utilizando técnicas DEMATEL e ISM, y luego mapeamos el modelo a una red BN, utilizando el cálculo de probabilidad posterior para permitir el reconocimiento de razonamiento de diagnóstico inverso. Los resultados indicaron que el conocimiento inadecuado de las catástrofes hídricas y la detección hidrogeológica inadecuada son los factores clave, con probabilidades posteriores del 91 y el 82%, y los grados centrales más altos de 5,400 y 4,917. Los factores fundamentales que contribuyen son la gestión desordenada de la seguridad y la supervisión imperfecta, con grados causales de 1,402 y 2,038. De la estructura causal del modelo y del análisis de la cadena causal máxima se desprende que es muy probable que estos factores sean la causa de la mayoría de los accidentes por irrupción de agua. Para controlar más eficazmente estos accidentes y minimizar las pérdidas, resulta fundamental dedicar mayor atención a dichos factores.
Zusammenfassung
Die Anzahl und Schwere von Grubenwasserunfällen kann durch ein besseres Verständnis der Ursachen dieser Unfälle und der internen Beziehungen zwischen den verschiedenen ursächlichen Faktoren verringert werden. Auf der Grundlage von 70 Großereignissen aus den Jahren 2010 bis 2020 haben wir ein mehrstufiges hierarchisches Strukturmodell unter Verwendung von DEMATEL- und ISM-Techniken erstellt und das Modell dann auf ein BN-Netzwerk abgebildet, wobei wir die Berechnung von Posterior-Wahrscheinlichkeiten verwendet haben, um Erkenntnisse aus einer umgekehrten Diagnose zu gewinnen. Die Ergebnisse zeigten, dass unzureichendes Wissen über Wasserkatastrophen und unzureichende hydrogeologische Erkundung die Schlüsselfaktoren sind, mit Posterior-Wahrscheinlichkeiten von 91 und 82 % und den zentral höchsten Kausalgraden von 5.400 und 4.917. Die wichtigsten Einflussfaktoren sind ein ungeordnetes Sicherheitsmanagement und eine unzureichende Überwachung mit Kausalgraden von 1,402 und 2,038. Aus der Kausalstruktur des Modells und der Analyse der maximalen Kausalkette wird deutlich, dass diese Faktoren sehr wahrscheinlich die Ursache für die meisten Unfälle durch Wassereinbruch sind. Eine stärkere Beachtung dieser grundlegenden Faktoren kann dazu beitragen, diese Unfälle wirksamer zu steuern und die Verluste zu minimieren.
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Funding
National Natural Science Foundation of China (Grant no. 71971003); Natural Science Foundation of Anhui Province, (Grant no. 1808085MG212); Anhui University Provincial Science Fund Project (Grant no. yjs20210411).
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Hong, W., Sheng, W. A DEMATEL-ISM-BN Model of Mine Water Inrush Accidents. Mine Water Environ 42, 178–186 (2023). https://doi.org/10.1007/s10230-022-00907-1
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DOI: https://doi.org/10.1007/s10230-022-00907-1