Correlation Between the Set of Knowledge, Perception, Attitude, and Motivation with the Fire Risk Control Behaviour

A fire accident may happen at anytime and anywhere. Such an accident happened in 2017 on the campus of Bogor Agriculture University (IPB). It was caused by an electricity short circuit. In 2013, an electricity panel exploded in a building at Faletehan University campus. A fire accident is an imminent threat that will result in a great loss so that an occupational health and safety program to deal with fire risks needs to be implemented in an academic community and other public space. To find out the correlation between the set of knowledge, perception, attitude, and motivation and the fire risk control behaviour. A quantitative statistical analysis was conducted on cross-sectional. The research was carried out at the Falatehan campus from July to September 2021. data obtained from all members of the academic community at Faletehan University (2144 persons). Out of that population of 2144 respondents, 377 respondents were made as samples. Data obtained from answers in the questionnaires distributed among all respondents were processed with univariate, bivariate, and multivariate analyses. Each member of the set of knowledge, perception, attitude, and motivation is correlated with the fire risk control behaviour. Knowledge and fire risk control behaviour have an OR (odds ratio) of 2.193 and p-value of 0.0000. Perception and fire risk control behaviour have an OR of 1.962 and p-value of 0.001. Attitude and fire risk control behaviour have an OR of 1.785 and p-value of 0.006. Motivation and fire risk control behaviour have an OR of 1.962 and p-value of 0.001. The dominating factor associated with the fire risk control behaviour is motivation with an OR of 2,538 and p-value of 0.000 on their relationship. Knowledge, perception, attitude, and motivation are strongly correlated with fire risks control behaviour. Suggestions: The top management of Faletehan University needs to maintain the motivation of all its academic community members in fire risk control behaviour and to strengthen occupational health and safety programs related to fire safety.

Fire Safety of Integrated Pipeline Corridor Cable Block Based on Numerical Simulation

The comprehensive pipe gallery is a tunnel space built underground in the city, which can centrally lay all kinds of municipal pipes in the tunnel for unified management, which can not only protect the pipes, but also improve the land utilization rate. However, with the increasing of integrated pipe gallery construction and pipeline inclusion, various safety accidents have appeared, among which the fire risk of cable cabin is the greatest. This paper mainly studies the fire safety of cable cabins of integrated pipe gallery based on numerical simulation. In this paper, PyroSim fire software was used to simulate the fire accident in construction. Through the investigation of the construction site, simulation parameters were set to simulate the fire situation in a real and objective way. The temperature, smoke and visibility changes under different working conditions were studied by setting several groups of thermocouples, smoke sensing points and visibility slices.

A Study on Decisional Factors for Service Life of Fire Trucks

The lifetime of fire trucks depends on their built-in pumps. A fire truck delivers water to the site of a fire accident via its pump that is composed of an impeller and a casing. As its service time increases, the clearance between the two elements increases. This causes leakage in the pump to increase beyond its limit and the pump can no longer fulfill its principal role. In general, fatigue, erosion, and corrosion are considered to be the major mechanisms for pump failure. From this study, it has been observed that fatigue is not the main factor for failure because of the small magnitude of stress it induces. Erosion by particles shows a high erosion rate of the suction area of the impeller. Cavitation, which is a key mechanism of corrosion, is observed at a suction depth of 7 m and is observed to widen at 7.1 m depth. Finally, the lifetime of the fire trucks is found to be affected by the motion of sucking the natural water of ponds, rivers, etc.

IoT Based Fire Accident Detection System with Deep Learning Intelligence

A fire accident can be caused by many hazards, such as a propane tank, a defective product, a vehicle crash, or poor workplace safety. Because accidents involving fire are often unexpected and sudden, there isn’t a standard legal process for dealing with them, other than filing a negligence or workers compensation claim. This project aims to detect and monitor Fire Accident incidents well in advance and alert the surroundings to minimize the losses. This is an integration of IoT and Deep Learning Technologies, where sensors are used to collect the relevant data under the supervision of a controller unit. The controller unit collects and sends this data to a cloud database, from where the data for the Deep Learning model is fetched. This data is then used for making some insights and further predictive analytics. From the insights, many variables were found to be one of the reasons for a fire accident to take place. We considered the information about variables like Flame sensor, Temperature, Heat Index, GPS coordinates, Smoke, Type of Gases, Date, and Time for feature set generation and fed the model to a deep neural network for making future predictions. Comparing to existing conventional methods, this proposed method is different in terms of integrating deep learning with IoT. This method of approach will predict the chance of accidents priorly by classification of data.

An Improved Failure Risk Assessment Method for Bilge System of the Large Luxury Cruise Ship under Fire Accident Conditions

This paper develops an improved failure risk assessment method and discusses the risk control measures for a large luxury cruise ship’s bilge system under fire accident conditions. The proposed method incorporates an expert weight calculation model and a risk coefficient calculation model. The expert weight calculation model considers the differences in experts’ expertise levels (i.e., qualification level, decision-making capacity, and decision-making preference). Further, the method integrates the evaluations resulting from fuzzy analytic hierarchy process (FAHP) and extended fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) of different experts. The risk coefficient (RC) calculation model utilizes a three-dimensional continuous matrix, serving to determine the risk factors’ ratings. The influences of the expert weight and RC calculation models on the proposed method’s performance are studied through a sensitivity analysis. The work demonstrates that the proposed method minimizes the issues encountered when using conventional methods for determining risk ratings. Finally, the results of an empirical study comprising ten experts evaluating the VISTA cruise ship’s bilge system prove the applicability of the proposed method and offer practical design guidelines to meet the regulations for Safe Return to Port (SRtP).

Fire Fighting Robot using IOT

Fire accidents are undesirable event which emits heat, smoke or flame. Fire accident can cause a huge number of loss because of the danger and risk involved in rescuing victims out of the fire. In everyday life, it is not possible to always rely on human patrol for detecting and extinguishing fire at a fire accident scene. Fire fighting Robot is very effective in detecting fire accidents in industrial and residential areas where fire possibilities are high. In order to achieve this, this robot is used to detect a flame locate it and extinguishes the fire immediately before it leads to a dangerous accident. In case of risky situations, firefighting robot effectively find the fire and suppresses it. Effective monitoring, fire identification and extinguishing of fire are the problems to be solved. Our project is designed to solve those problems. Fireman can send commands to robot through raspberry pi module which is mounted on robot itself. Sprinkler start sprinkling water when it detects fire using sensor.