Indoor search and rescue using a 3D indoor emergency spatial model

Abstract

Buildings are becoming a major subject of recent disasters resulting in huge amounts of casualty and damage costs for both public and emergency managers. In the context of emergency management, the Search and Rescue procedures in indoor environments face various complications and uncertainties due to increasing urbanization and complexity of urban structures. In case of an indoor disaster it is unknown to emergency managers how many first responders are required and how the routes should be assigned to them prior to entering the scene in order to have the most efficient plan.

An important factor in enabling effective search route assignment to first responders on scene is the existence of an indoor model addressed at emergency applications. Currently, most of the information from inside the structures is unrevealed to emergency managers prior to physical entrance to the scene exposing their lives to risk and danger and complicating decision making. Unfamiliarity with the indoor environments, limited visualization due to smoke, and collapsed and blocked areas all increase the difficulty of emergency response and rescue as well as unwanted wandering and uncertainty routing in indoor environments. A key limitation is that the current indoor spatial models do not fit emergency response requirements and thus cannot be effectively used for decision making in this area leaving the Indoor Search and Rescue Problem a challenge for first responders. Thus, an effective indoor search and rescue solution needs to be based on a detailed 3D indoor model consisting of both the geometric and semantic information of the structure which can be leveraged to build the indoor search and rescue navigation model.

This thesis addresses this knowledge gap by first developing a new 3D Indoor Emergency Spatial Model (IESM) aimed at emergency response operations that contains geometric and semantic information of building utilities required by first responders. And second, formulating and solving the indoor Search and Rescue Problem (SRP) by leveraging IESM for creating the indoor navigable network. IESM and the SRP Algorithm form the two main contributions of the research. The study uses a data modelling technique, a mathematical formulation, and an evolutionary optimization for developing the IESM model and SRP Algorithm.

The underlying IESM is based on mission critical information required on-scene by fire fighters which is developed as a data model using the BIM (Building Information Modelling) representation of the building. To develop the Search and Rescue Algorithm, an integrated geometric/semantic indoor route network is extracted based on IESM to support emergency response routing requirements. Using the indoor navigable model, the indoor Search and Rescue Problem is formulated mathematically. The Search and Rescue Problem is proved to be in the category of integer linear programming problems, making it an NP-hard problem. Thus, an ant colony based algorithm is developed to solve the problem for both the initial state of the algorithm considering the building settings are intact, and for the dynamic state of the problem in which the algorithm has the ability to adapt the assigned routes to conform to the new conditions of the problem where the search area is changed due to real time updated information.

To evaluate the feasibility and applicability of the proposed 3D indoor situational awareness model, the model was evaluated using an observational case study and descriptive scenario method at The Department of Infrastructure Engineering at The University of Melbourne under various scenarios. In addition, experimental simulation and analytical methods are used for evaluating the performance of the SRP algorithm. To prove the effectiveness of the proposed model, the results are validated with current practice strategies of fire fighters using an agent based simulator. These fire fighter decision strategies were collected through a series of interviews with the Dandenong Country Fire Authority (CFA) of Victoria in Australia.

The research proved that availability of IESM based model of the structure consisting of detailed geometric and semantic information required by emergency responders allows the SRP approach to identify the number of crew and the designated 3D routes for each person (including which entrance/exit and ingress/egress path to use) to minimize total response time; saving critical on-scene investigation and planning time. While IESM was proved to facilitate indoor emergency response by enabling 3D seamless indoor/outdoor visualization, spatial analysis, routing, and situational awareness; the empirical analysis from the SRP algorithm proved that the total response time and number of rescuers can be significantly improved using the SRP solution approach compared to the usual first responders’ strategies. Also, the results prove that the SRP algorithm finds paths that are balanced in terms of on-route travel time and distance, thus distributing the workload evenly amongst the rescuers which is very helpful for effective resource management. In addition, the dynamic SRP approach was shown extremely efficient as it adapts the assigned routes to the new circumstances of the structure without violating the constraints and conditions of the problem.

Overall, based on the obtained results it can be inferred that while the current firefighters’ practice systems carry a lot of uncertainties, utilizing the proposed 3D indoor situational awareness model can present a much more certain, robust, and reliable solution. Availability of such information could help decision makers interact with building information and thus make more efficient planning prior to entering the disaster site.