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A building fire is dynamic. A continually changing hostile fire environment influences time relationships that affect fire defenses and risks to people and building functions. The fire and fire defenses in each building interact with different sequences and distinct ways. Risks are characterized by the building's performance. This book organizes the complex interactions into an analytical framework to evaluate any building - at any location - built under any regulatory jurisdiction or era. Systematic, logical procedures evaluate individual component behavior and integrate results to understand holistic performance. The Interactive Performance Information (IPI) chart structures complex time-related interactions among the fire, fire defenses, and associated risks. Quantification uses state-of-the-art deterministic methods of fire safety engineering and fire science. Managing uncertainty is specifically addressed.

The Moderate Resolution Imaging Spectroradiometer (MODIS) has been widely used for wildfire occurrence and distribution detecting and fire risk assessments. Compared with its commission error, the omission error of MODIS wildfire detection has been revealed as a much more challenging, unsolved issue, and ground-level environmental factors influencing the detection capacity are also variable. This study compared the multiple MODIS fire products and the records of ground wildfire investigations during December 2002–November 2015 in Yunnan Province, Southwest China, in an attempt to reveal the difference in the spatiotemporal patterns of regional wildfire detected by the two approaches, to estimate the omission error of MODIS fire products based on confirmed ground wildfire records, and to explore how instantaneous and local environmental factors influenced the wildfire detection probability of MODIS. The results indicated that across the province, the total number of wildfire events recorded by MODIS was at least twice as many as that in the ground records, while the wildfire distribution patterns revealed by the two approaches were inconsistent. For the 5145 confirmed ground records, however, only 11.10% of them could be detected using multiple MODIS fire products (i.e., MOD14A1, MYD14A1, and MCD64A1). Opposing trends during the studied period were found between the yearly occurrence of ground-based wildfire records and the corresponding proportion detected by MODIS. Moreover, the wildfire detection proportion by MODIS was 11.36% in forest, 9.58% in shrubs, and 5.56% in grassland, respectively. Random forest modeling suggested that fire size was a primary limiting factor for MODIS fire detecting capacity, where a small fire size could likely result in MODIS omission errors at a threshold of 1 ha, while MODIS had a 50% probability of detecting a wildfire whose size was at least 18 ha. Aside from fire size, the wildfire detection probability of MODIS was also markedly influenced by weather factors, especially the daily relative humidity and the daily wind speed, and the altitude of wildfire occurrence. Considering the environmental factors’ contribution to the omission error in MODIS wildfire detection, we emphasized the importance of attention to the local conditions as well as ground inspection in practical wildfire monitoring and management and global wildfire simulations.

This study assessed the severity of building fires in 17 villages that comprise Taishan District, New Taipei City, Taiwan. A literature review was performed to discuss the impact of fire severity assessment criteria in order to develop items and factors for the analytic hierarchy process (AHP). We identified six items for the building fire severity assessment: rescue response time, narrow road density, water-sacristy area density, building risk, hazardous materials’ place density, and fire safety inspection-regulated premises density. Big data analysis and remote sensing were employed to facilitate devising the AHP structure with items and factors. We also compared the annual average burned area from 2005 to 2015 through the building fire severity assessment to validate assessment accuracy. The actual yearly average burned area in each village of Taishan District was used to verify the building fire severity assessment, and the compliance rate of the rating (i.e., high, moderate, and low) was, respectively, 60%, 67%, and 67%. The proposed assessment is evidently feasible and can act as a reference for quantitative analyses for assessments of building fire severity.

Fiber-reinforced polymers (FRPs) are materials that comprise high-strength continuous fibers and resin polymer, and the resins comprise a matrix in which the fibers are embedded. As the technique of FRP production has advanced, FRPs have attained many incomparable advantages over traditional building materials such as concrete and steel, and thus they play a significant role in the strengthening and retrofitting of concrete structures. Bridges that are built out of FRPs have been widely used in overpasses of highways, railways and streets. However, damages in FRP bridges are inevitable due to long-term static and dynamic loads. The health of these bridges is important. Here, we review the maintenance and inspection methods for FRP structures of bridges and analyze the advantages, shortcomings and costs of these methods. The results show that two categories of methods should be used sequentially. First, simple methods such as visual inspection, knock and dragging-chain methods are used to determine the potential damage, and then radiation, modal analysis and load experiments are used to determine the damage mode and degree. The application of FRP is far beyond the refurbishment, consolidation and construction of bridges, and these methods should be effective to maintain and inspect the other FRP structures.

This study presents a validated drone-based methodology for inspecting fire protection belts in Galicia, Spain, with a focus on secondary protection belts surrounding settlements. Current manual inspection methods are limited by resource constraints and inefficiency, especially given Galicia’s steep slopes and fragmented, vegetated terrain. Our integrated approach combines high-resolution drone imagery, RTK positioning, GIS tools, and the Time2Parcel algorithm, enabling synchronized, parcel-level documentation at cadastral scale and allowing office-based technicians to directly review automatically generated video segments specific to each parcel for inspection verification. The methodology employs a hybrid classification system: automated assessments via orthophoto and LiDAR analysis and manual verification for cases with low confidence scores. Government technicians can perform office-based reviews without GIS expertise; the system automatically matches video to cadastral records, eliminating manual video review. Key results include the Time2Parcel algorithm for automatic video-to-parcel correlation, completion of inspections for 4934 parcels, and an operational efficiency increase of 68–70% reduction in inspection time compared with traditional methods. This workflow enables faster, safer, and more accurate inspections in highly fragmented rural contexts, improving legal compliance and environmental management.

BackgroundThe Bureau of Alcohol, Tobacco, Firearms and Explosives (ATF) conducts inspections of Federal Firearms Licensees (FFLs) to ensure compliance with federal laws in an effort to keep the public safe from firearm violence. However, the criteria for which FFLs are inspected lacks transparency, potentially signalling inefficiencies or biases.MethodsWe extracted data on FFL inspections and violations (2016–2017) requiring remedial action from the Gun Store Transparency Project database and the ATF’s FFLs listings. Using a generalised linear mixed model with a random effect for states and robust SE, we estimated the census tract and state level predictors of 1) the likelihood of the ATF inspecting an FFL and 2) among those inspected, the likelihood of a violation.ResultsOf the 93,420 FFLs, 22 921 were inspected and 2392 had violations requiring remedial action. Increased likelihood of inspection was statistically significantly associated with higher levels of poverty, lower median income, greater non-White population percentages, and older census tract median ages. The sole predictor of an FFL receiving a violation requiring remedial action was the state’s firearm law permissiveness.DiscussionATF inspections disproportionately targeted communities with higher poverty, lower median income, larger non-White populations, and older demographics. This indicates potential racial and socioeconomic biases, diverting attention from jurisdictions with more lenient firearm laws where violations were more likely to occur.ConclusionThe ATF needs to reassess the influence of potential biases on its strategies for selecting FFLs for inspections and instead focus on risk-based assessments in order to promote public safety.

This paper focusses on real-time detection and abnormal state prediction through a set of wireless equipment of fire water system to provide a better solution for fire inspection. The main goal of this research is to improve the fire extinguishing efficiency. Firefighting and rescue operations are urgent, and concealed or dry fire hydrants may cause failure cases of firefighting. To prevent this situation, this paper proposed the wireless equipment of fire water system to monitor the multiple indicators of outdoor hydrant, indoor hydrant, and sprinkler network. The objective of the experiment is to realize the real-time monitoring and obtain the data information of the state of the fire water system. Also, the multiple linear regression analysis method is adopted to analyze the monitored water pressure data to achieve rapid identification of abnormal water pressure value. The results indicate that the data transmission of the wireless equipment of fire water system is reliable, the pressure is accurately monitored below 0.40 MPa, and the average relative error of abnormal water pressure monitoring results is less than 6%. The new equipment and algorithm are used not only to monitor the state of water supply system, but also to depict the change of water consumption in the monitoring environment and predict the unconventional state, which provides directional help for fire patrol and inspection.

With the increasing frequency and intensity of forest fires driven by climate change and human activities, efficient detection and rapid response have become critical for forest fire prevention. Effective fire detection, swift response, and timely rescue are vital for forest firefighting efforts. This paper proposes an unmanned aerial vehicle (UAV)-based forest fire inspection system that integrates a ground support system (GSS), aiming to enhance automation and flexibility in inspection tasks. A three-layer mixed-integer linear programming model is developed: the first layer focuses on the site selection and capacity planning of the GSS; the second layer defines the coverage scope of different GSS units; and the third layer plans the inspection routes of UAVs and coordinates multi-UAV collaborative tasks. For planning UAV patrol routes and collaborative tasks, a goal-driven greedy algorithm (GDGA) based on traditional greedy methods is proposed. Simulation experiments based on a real forest fire case in Turkey demonstrate that the proposed model reduces the total annual costs by 28.1% and 16.1% compared to task-only and renewable-only models, respectively, with a renewable energy penetration rate of 68.71%. The goal-driven greedy algorithm also shortens UAV patrol distances by 7.0% to 12.5% across different rotation angles. These results validate the effectiveness of the integrated model in improving inspection efficiency and economic benefits, thereby providing critical support for forest fire prevention.

To reduce the danger for firefighters and ensure the safety of firefighters as much as possible, based on the back propagation neural network (BPNN) the fire sensor multi-sensor information fusion detection system is investigated. According to previous studies, the information sources and information processing methods for the design of this study are first explained. Then, the basic structure and flowchart of the research object in this study are designed. Based on the structure diagram and flowchart, the BPNN is selected to fuse the feature layers in this study, and the fuzzy control is selected to fuse the decision layers in this study. The multi-sensor information fusion detection system collects information for the sensors first, processes the collected information, and sends it to the processor of the robot. The processor analyzes and processes the received signal, and transmits the obtained information to the control terminal through the wireless communication system. Through the tests in this study, it is found that when the number of hidden layer nodes of the BPNN is 7, the optimal training result is obtained. On this basis, the test of BPNN in this study is performed. The test results show that after 127 iterations, the error of the BPNN reaches the lowest target value, indicating that the BPNN achieves an excellent level of accuracy. The trained BPNN has a running time of 0.0276 s and a mean square error of 0.0013. The smaller the mean square error value is, the higher the accuracy of the BPNN is, which shows that the BPNN meets the high precision requirements of this study. The research on the multi-sensor information fusion detection system of fire robots in this study can provide theoretical support for the research on forest fire detection in China. Since the proposed BPNN-based robot is applied to the inspection and processing of forest remaining fire, the results are applicable to the forests of various countries, with a wide range of applications.