Fire protection durability of intumescent coatings after accelerated aging

The most common method of achieve the required fire resistance is by the use of passive fire protection systems, being intumescent coatings the fire protection material frequently used. These are usually considered thin film coatings as they are applied with a dry film thickness (DFT) between 0.3-3 [mm]. The required DFT is obtained by experimental fire resistance tests performed to assess the contribution of this reactive fire protection material to the steel member fire resistance. This tests are done after dry coating and a short time period of atmospheric conditioning, at constant temperature and humidity. As the coatings formulation is mainly made from polymeric basis compounds, it is expected that the environmental factors, such temperature, humidity and UV radiation (UVA and UVB) significantly affect the intumescent coating fire protection performance and its durability. This work presents a research study about the effects of aging on the fire protection performance of intumescent coatings. A commercial water based coating is submitted to an accelerated aging cycle, using a QUV Accelerated Weathering Tester. This tests aim to simulate 10 years of the coating natural aging. The coating durability is tested comparing the fire protection of small steel samples submitted to a radiant heat flux exposure from a cone calorimeter. In total, 28 tests were performed on intumescent coating protected steel specimens, of which 14 specimens were tested before the hydrothermal aging test and other 14 after accelerated aging. The experimental tests results of the steel temperature evolution shows that increasing the intumescent dry coating film thickness, the fire resistance time increases. After the accelerated aging cycles, the coating lose their ability to expand, resulting in an increase of the steel temperature of approximately 200 [ºC], compared to the samples without aging.

Fire in relation to primitive agriculture and grazing in the Tropics ; annotated bibliography.

Vol. 2 was presented at the Ninth Pacific Science Congress, Bangkok, Thailand, Nov. 18-30, 1957.

Global interactions between fire and vegetation, human activities and climate

Doutoramento em Engenharia Florestal - Instituto Superior de Agronomia - UL

Wildfire Assessment Using FARSITE Fire Modeling: A Case Study in the Chihuahua Desert of Mexico

The Chihuahua desert is one of the most biologically diverse ecosystems in the world, but suffers serious degradation because of changes in fire regimes resulting in large catastrophic fires. My study was conducted in the Sierra La Mojonera (SLM) natural protected area in Mexico. The purpose of this study was to implement the use of FARSITE fire modeling as a fire management tool to develop an integrated fire management plan at SLM. Firebreaks proved to detain 100% of wildfire outbreaks. The rosetophilous scrub experienced the fastest rate of fire spread and lowland creosote bush scrub experienced the slowest rate of fire spread. March experienced the fastest rate of fire spread, while September experienced the slowest rate of fire spread. The results of my study provide a tool for wildfire management through the use geospatial technologies and, in particular, FARSITE fire modeling in SLM and Mexico.

Improving understandability in consumer health information search: Uevora @ 2016 fire chis

This paper presents our work at 2016 FIRE CHIS. Given a CHIS query and a document associated with that query, the task is to classify the sentences in the document as relevant to the query or not; and further classify the relevant sentences to be supporting, neutral or opposing to the claim made in the query. In this paper, we present two different approaches to do the classification. With the first approach, we implement two models to satisfy the task. We first implement an information retrieval model to retrieve the sentences that are relevant to the query; and then we use supervised learning method to train a classification model to classify the relevant sentences into support, oppose or neutral. With the second approach, we only use machine learning techniques to learn a model and classify the sentences into four classes (relevant & support, relevant & neutral, relevant & oppose, irrelevant & neutral). Our submission for CHIS uses the first approach.