An accident during the transport of liquefied petroleum gas (LPG) via a tanker vehicle leads to the leakage of a flammable substance, causing devastation. In such a situation, the appropriate action with the shortest possible delay can minimize subsequent losses. However, the decision-making mechanism remains unable to detect the occurrence of an accident and evaluate its extent within the critical time. This paper proposes an automatic framework for leakage detection and its consequence prediction during the external transportation of LPG using artificial intelligence (AI) and the internet of things (IoT). An AI model is developed to predict the probable consequences of the accident in terms of the diameter of risk contours. An IoT framework is proposed in which the developed AI model is deployed in the edge device to detect any leakage of gas during transportation, to predict its probable consequences, and to report it to the remotely located disaster management team for initiating appropriate action. A prototype of the proposed model is built and its performance is successfully tested. The proposed solution would significantly help to identify efficient disaster management techniques by allowing for quick leakage detection and the prediction of its probable consequences.

In this study, a three-dimensional trabecular structure is constructed with the help of micro-computed tomography scan data of a rat distal femur, and the finite element analysis (FEA) is carried out to analyze the stress distribution. Compressive loads 1000 N, 2000 N, and 4000 N were applied in longitudinal and transverse directions. The FEA showed that the percentage increase of stress in the transverse direction is 22% compared to that in the longitudinal direction. An analytical analysis was conducted on a small section to validate the results and the estimated error is 7.16% between the analytical and finite element simulation results.