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Fire statistics mirror the outcome of fire prevention. Most fire statistics in Germany deal with the loss of life, value, and fire department actions (number of interventions, nozzles used, or alarm category like a false alarm). However, these results also represent the safety level the legislator has set through the prescriptive building regulations. The current statistics cannot evaluate the level of fire safety and the fulfillment or necessity of fire safety precautions. Today, expert judgment from firefighters is necessary to fill this gap. Here, we show the first evaluation of fire prevention and hazard protection measures by evaluating 900 potentially harming fires throughout Germany. In contrast to minor fires, these fires have advanced to the extent that they could potentially violate the protection objectives outlined in building regulations. The fire department association developed a questionnaire to evaluate the fire safety level and possibly reduce unnecessary fire safety regulations. One hundred twenty-three fire departments carried out the questionnaire, which are responsible for 25% of the German population. Fire prevention officers of the fire departments went to the scene after the fire was extinguished, and the fire safety concept of the building could be evaluated. We found a high rate of injuries, smoke spread, need for rescue by firefighters, and higher than expected firefighter response times after arrival at the scene. Surprisingly, smoke spread rates correlated with building height and not with building age. It was even possible to assess the risk of multiple casualties. Overall, the questionnaire results give insight into the current level of fire safety in existing buildings. Ways and rates for smoke and fire spread prove the importance of second escape routes and the influence of human misconduct. According to these results, current building code regulations are sufficient to prevent fire spread. On the other hand, smoke spreading is a severe threat to people's safety. For example, the data shown can be applied in Bayes nets or other risk calculations to optimize individual building designs or even governmental building codes concerning fire safety engineering. Based on our observations, science, and building codes, authorities could in the future establish a performance-based building code instead of the current prescriptive code. This paper presents the first approach in Germany to quantify the expert judgment of fire departments and use it as a source of knowledge for fire prevention. [ABSTRACT FROM AUTHOR]

With the advancement of society, ensuring the safety of personnel involved in municipal construction projects, particularly in the context of pandemic control measures, has become a matter of utmost importance. This paper introduces a security measure for municipal engineering, combining deep learning with object detection technology. It proposes a lightweight artificial intelligence (AI) detection method capable of simultaneously identifying individuals wearing masks and safety helmets. The method primarily incorporates the ShuffleNetv2 feature extraction mechanism within the framework of the YOLOv5s network to reduce computational overhead. Additionally, it employs the ECA attention mechanism and optimized loss functions to generate feature maps with more comprehensive information, thereby enhancing the precision of target detection. Experimental results indicate that this algorithm improves the mean average precision (mAP) value by 4.3%. Furthermore, it reduces parameter and computational loads by 54.8% and 53.8%, respectively, effectively striking a balance between lightweight operation and precision. This study serves as a valuable reference for research pertaining to lightweight target detection in the realm of municipal construction safety measures. [ABSTRACT FROM AUTHOR]

The statistics pertaining to accidents occurring at construction sites underscore the pressing requirement for a substantial and timely reevaluation of safety measures within the construction sector. Accidents do not occur randomly; rather, they arise from the presence of unsafe actions, hazardous conditions, or a combination of both. The majority of accidents stem from a combination of contributing causes, including unsafe acts and conditions. To enhance safety performance on a broader scale, this study undertakes an extensive analysis to identify these causes, evaluate their importance, and determine the countries that are most and least impacted by them. Ten African countries were selected as potential alternatives based on the frequency of infrastructure construction projects. A thorough review of existing literature was conducted to establish a three-level criteria framework. The framework was further refined through the Modified Delphi method to gather expert opinions. The weights assigned to the criteria were determined using the interval-valued Fermatean fuzzy analytical hierarchy process methodology. The Technique for Order Preference by Similarity to Ideal Solution method under the same fuzzy environment was then employed to rank the alternative countries. A sensitivity analysis was carried out to assess the robustness of the proposed methodology. The analysis revealed that the main cause of accidents was attributed to poor management, as it included ineffective project supervision, inadequate safety policies, poor organizational structure, and inappropriate scheduling/planning as the main underlying sub-factors. Additionally, it was observed that the sixth alternative country exhibited the highest susceptibility to accidents occurring at construction sites. [ABSTRACT FROM AUTHOR]

In this research, a groundbreaking intelligent model named the GWO-RF model is introduced for the prediction of water inflow (WI) during tunnel construction. WI is a prevalent and intricate occurrence that significantly jeopardizes the safety of workers and equipment. The proposed model aims to address this issue by leveraging the capabilities of Grey Wolf Optimization (GWO) combined with the Random Forest (RF) algorithm. Through this innovative approach, the study strives to enhance the accuracy and effectiveness of WI prediction, ultimately contributing to improved safety measures in tunnel construction projects. The GWO-RF model utilizes 600 sets of data comprising tunnel depth (H), groundwater level (h), rock quality designation (RQD), and water yield property (W) as input independent variables. To evaluate the predictive performance of the GWO-RF model, seven ensemble regression models, namely RF, Bagging, AdaBoost, HistGradientBoosting (HGBoosting), GradientBoostingRegressorTree (GBRT), Voting, and Stacking are compared. Four prediction accuracy criteria were used to evaluate the performance of the developed models. The significance of variables and their contribution to WI prediction are analyzed using the Shapley Additive Explanations (SHAP) approach. The evaluation results demonstrate the superior accuracy of the proposed GWO-RF model over other conventional ensemble models in both the training and testing phases. With R2 values of 0.9483 and 0.9289, MAE values of 4.6042 and 8.3862, MSE values of 68.5806 and 144.1149, and RMSE values of 8.2813 and 12.0047, respectively, the GWO-RF model outperforms the others. As a result, the proposed GWO-RF model proves highly effective in predicting WI and determining the corresponding pumping requirements, making it invaluable for tunneling projects. [ABSTRACT FROM AUTHOR]

Current studies have focused on the exploration of the unmanned aircraft system (UAS) applications in the construction and infrastructure domains, such as progress monitoring, condition inspections, and particularly safety inspection. While their works have had significant contributions to highlighting the potential of the UAS technology, there are still gaps in the lack of operational considerations and workflows established to integrate the UAS into the current safety management program. To fill this gap, this study conducted field tests at commercial building construction worksite by adopting conceptually developed UAS flight protocol. A total of eight flights were performed to collect visual assets and provide for safety professionals at the jobsite with the insight of how this technology can be utilized in accordance with the developed workflow. A total of eight project personnel participated in the UAS flight test at their jobsite as well as post-flight interview to discuss how this technology can meet their safety management task goals. As the qualitative study, this paper narratively described the conceptual procedures consist of three different decision-making processes for ensuring the safe flight of the UAS: pre-flight, flight, and post-flight decision makings. Based on the potential users' perceptions obtained during the field testing, this presented study focused on three different operational considerations: involving project-based, UAS's capability and hardware specifications and UAS and project team-based aspects. The main goal of this study proposes a decision-driven workflow for UAS-integrated safety management that encourages sustainable and continuous improvement and documents the operational considerations related to this application. As the qualitative exploratory study, this research is limited to employ only one test-bed environment and the small number of interviewees. However, this study can contribute to a better understanding of the concerns, processes, and further recommendations to safely and effectively implement the UAS into the construction safety management program. [ABSTRACT FROM AUTHOR]

Evacuation plans have been historically used as a safety measure for the construction of buildings. The existing crowd simulators require fully modeled 3D environments and enough time to prepare and simulate scenarios, where the distribution and behavior of the crowd needs to be controlled. In addition, its population, routes or even doors and passages may change, so the 3D model and configurations have to be updated accordingly. This is a time-consuming task that commonly has to be addressed within the crowd simulators. With that in mind, we present a novel approach to estimate the resulting data of a given evacuation scenario without actually simulating it. For such, we divide the environment into smaller modular rooms with different configurations, in a divide-and-conquer fashion. Next, we train an artificial neural network to estimate all required data regarding the evacuation of a single room. After collecting the estimated data from each room, we develop a heuristic capable of aggregating per-room information so the full environment can be properly evaluated. Our method presents an average error of 5 % when compared to evacuation time in a real-life environment. Our crowd estimator approach has several advantages, such as not requiring to model the 3D environment, nor learning how to use and configure a crowd simulator, which means any user can easily use it. Furthermore, the computational time to estimate evacuation data (inference time) is virtually zero, which is much better even when compared to the best-case scenario in a real-time crowd simulator. [ABSTRACT FROM AUTHOR]

This study presents the fragility curve for steel and concrete frames based on near-field (NF) and far-field (FF) ground motion records. The fragility curve is calibrated to the incremental dynamic analysis (IDA) curve based on building materials and frame heights. The frames are designed according to Eurocodes. This study uses seven ground motion records for NF and another seven records for FF. The five performance levels prescribed by FEMA-273 are used as structure performance benchmarks in generating the IDA curve. Results based on the NF and FF records indicate that a three-storey steel frame is stiffer than a concrete frame. By contrast, the results for a six-storey structure show that a steel frame is more suitable than low-rise and mid-rise frames. The fragility curve results show that the probability of reaching or exceeding a specified damage state can be determined. [ABSTRACT FROM AUTHOR]

When a building is designed it possesses a spatial layout of routes and passageways all of which contribute to the building’s egress capability—a key consideration in the context of health and safety for occupants. In this paper we examine how such a spatial layout can be interpreted as a vital system of egress, which if processed exhaustively by a naïve agent will identify with a measure of egress complexity. We further examine how the established rule-based mechanism for the determination of egress complexity satisfies the generically accepted axioms of complexity and we illustrate some of the algorithms of egress complexity with examples. [ABSTRACT FROM AUTHOR]