Safety Of Buildings Research Articles

INOVAÇÕES NA CORREÇÃO DE MANIFESTAÇÕES PATOLÓGICAS EM EDIFÍCIOS: REVISÃO DE LITERATURA.

Buildings must provide the user with physical and visual comfort, in addition to structural safety. However, it is common for pathological manifestations to appear at any stage of construction or use of the building. In order to indicate appropriate therapy, it is necessary to know the origin of the problem whose study procedures are part of the branch of Building Pathology. The main instructional methods that professionals involved in the area validate are specified in the IBAPE National Building Inspection Standard, which establishes criteria and procedures related to building inspection. In the search to solve such problems, innovative measures found in Brazil. In this sense, this study aimed to present innovative measures aimed at correcting pathological manifestations in buildings. In the methodology used, it is an integrative review of the literature, based on scientific articles, periodicals and current legislation on the respective topic. Data collection was carried out through databases such as Scielo, Google Scholar, among others, from 2019 to 2023. In the results, it was evident that the correction of pathological manifestations in buildings is a vital area in civil engineering and in construction, focusing on the identification, analysis and resolution of structural and functional problems that arise over time. Innovation in this field can significantly improve the durability, safety and functionality of buildings..

Analysis of Vibrational Characteristics of the Bank of China Tower in Hong Kong with ANSYS

Abstract. With the rapid urbanization and technological advancements in construction, modern high-rise buildings have become a symbol of urban development. However, these structures face unprecedented challenges due to their increasing height and the complex dynamic loads they must withstand. This paper presents a comprehensive analysis of the Bank of China Tower in Hong Kong, a prominent architectural landmark, using ANSYS software to perform modal and transient structural analyses. The primary structure of the tower consists of five steel-reinforced concrete columns, designed to resist dynamic responses under extreme conditions such as high wind speeds and seismic activities. The modal analysis identified multiple natural frequencies and vibration modes, providing insights into the dynamic behavior of the structure. The transient structural analysis, conducted under seismic wave loading, demonstrated the tower's admirable performance under specified seismic conditions, highlighting its sufficient safety margins. This study emphasizes the importance of advanced materials and innovative structural designs in ensuring the stability and safety of high-rise buildings. The findings contribute significantly to the field of structural engineering, offering valuable insights for the design and analysis of future high-rise structures.

MANUTENÇÃO PREDIAL: ANÁLISE DAS PRINCIPAIS PATOLOGIAS

This paper addresses the main pathologies in civil construction, focusing on the analysis of causes, effects and solutions for defects that compromise the durability and safety of buildings. The objective of the study is to highlight the importance of preparing detailed projects, executing construction processes in accordance with Brazilian technical standards and using preventive maintenance as essential factors to avoid the emergence of the most recurrent pathologies identified, such as: cracks, fissures, splits, infiltrations, carbonation of concrete and corrosion of reinforcements. As a result, it was observed that the inadequate choice of materials and the lack of qualified labor are indicated as the most significant factors for the emergence of the pathologies listed above. Thus, building maintenance is essential to ensure that structures achieve the planned durability, avoiding high costs with future repairs and ensuring the safety and functionality of buildings.

Optimizing Solar Power Generation and Integration in Automotive Systems Through IoT

Objective: This study presents an Internet of Things (IoT)- based system with edge intelligence that predicts power production with over 98% accuracy and monitors substations and smart solar installations, ensuring reliable power distribution in industrial IoT environments. This system enhances sustainability, safety, and energy management in smart buildings by reducing power fluctuations by 30% and improving decision-making, leading to a 95% reduction in energy management costs. Method: An IoT-enabled power monitoring system was implemented for smart solar panels and substations, incorporating edge intelligence for instantaneous prediction and decision-making. An IoT-enabled solar charging station was deployed for smart homes and Industry 4.0 applications. The cloud was used for analyzing sensor data, with a response time of less than 1 second. Findings: The proposed framework increased the efficiency and reliability of power production and distribution by 25% across commercial, residential, and industrial contexts. It significantly mitigated power fluctuations, reducing downtime by 40% and achieving a 95% cost reduction in energy management compared to traditional systems. IoT integration improved safety and sustainability metrics by 20% in smart buildings. Novelty: The framework integrates edge intelligence with IoT in smart solar systems and substations, providing a sophisticated control system that enhances power distribution decision-making. It facilitates real-time monitoring and prediction of power production with over 98% accuracy, emphasizing sustainability, safety, and energy management improvements of up to 30% in smart buildings. Keywords: IoT-based control system, Smart solar systems, Edge intelligence, Power substations, Load management, Energy sustainability

Optimized bp Neural Network Based on Improved Dung Beetle Optimization Algorithm to Predict High-Performance Concrete Compressive Strength

In modern architecture, the structural safety of buildings largely depends on the compressive strength of high-performance concrete (HPC), which is determined by the complex nonlinear relationships between its components. In order to more accurately forecast HPC’s compressive strength, this paper proposes a prediction model based on an improved dung beetle optimization algorithm (OTDBO)-optimized backpropagation neural network (BPNN). Extreme Gradient Boosting (XGBoost) is employed to determine the inputs for the BPNN, enhancing the computational efficiency under high-dimensional data feature conditions. To address the issues of local optima entrapment and slow convergence in the dung beetle optimization algorithm (DBO), four improvements were made to enhance its performance. In the initial population generation stage, the optimal Latin hypercube method was used to increase the population diversity. In the rolling stage, the osprey optimization algorithm’s global exploration strategy was introduced to improve the global search capability. The variable spiral search strategy was employed in the reproduction stage, and an adaptive t-distribution perturbation strategy was combined in the foraging stage to enhance the algorithm’s adaptability and search efficiency. The improved dung beetle optimization algorithm (OTDBO) outperformed other algorithms in performance tests on the CEC2017 benchmark functions. In terms of predicting the compressive strength of HPC, the XG-OTDBO-BP model developed in this study outperformed models optimized by other algorithms in terms of fitting outcomes and prediction accuracy. These findings support the XG-OTDBO-BP model’s superiority in the compressive strength of HPC prediction.

A study of staff pre‐evacuation behaviors in a Malaysian hotel

AbstractSimulating fire and evacuation scenarios is crucial for engineers to assess building safety during fire incidents. Accurate simulations require data on occupants' behaviors, particularly during the pre‐evacuation phase as these decisions significantly impact evacuation duration. Gathering comprehensive data from diverse regions while considering cultural and regional variations is necessary to understand how occupants' behavior is influenced. Thus, this study focuses on examining the behavior of Malaysian hotel staff during unannounced fire drill to gain insights into factors affecting their behavior during pre‐evacuation stage, such as fire experience, fire alarm, drill participation, fire training, and awareness. The study categorizes the actions performed by the hotel staff into sequences and analyses them based on influencing factors. The findings indicate that instead of immediately evacuating in response to emergency notification, the hotel staff engage in various actions. Most staff members initially investigate or ignore the emergency, resulting in longer pre‐evacuation times. Moreover, the results suggest that previous drill participation and high awareness levels contribute to shorter pre‐evacuation times. Conversely, previous fire experience, fire training, and fire alarm familiarity have no effect on pre‐evacuation time.

Seismic Response of Open Ground Story Reinforced Concrete Buildings, Considering with Soil-Structure Interaction

This study investigates the seismic behavior of open-ground story (OGS) reinforced concrete buildings with soil-structure interaction (SSI) using SAP2000. The study mainly focused on mid-rise (8-storey) and low-rise (4-storey) OGS buildings, incorporating different masonry infill materials such as brick, concrete blocks, and autoclaved aerated concrete (AAC) blocks. Selected ground motions from the Bhuj, El Centro, and Chi-Chi earthquakes were utilized to simulate varying seismic intensities and frequency content. Critical parameters such as top-story displacement and inter-story drift are analyzed to assess the structural safety and serviceability of RC buildings. The results demonstrate that both OGS coupled with SSI and masonry infill type significantly affect the displacement and drift, with mid-rise buildings showing more pronounced impacts. Furthermore, ground motion characteristics are critical in producing different response patterns. The study highlights the importance of considering SSI, infill materials, and ground motion characteristics in the design of OGS buildings to enhance earthquake resilience and reduce seismic damage.

A Review of Seismic Optimization Design of Building Structures with Uniform Response

Earthquakes threaten human survival with powerful destructive power, and the performance of buildings in earthquakes is crucial. The seismic design of traditional building structures has the limitations of non-uniform response to displacement and acceleration, which can easily lead to partial damage to the building, unreasonable structure and difficulty in post-earthquake repair, which seriously affects the comfort and safety of the building. It is emphasized that improving the seismic safety of buildings is not only an inevitable requirement to ensure the safety of people's lives, but also to meet people's needs for continuous improvement of building comfort. The review shows that it is of great practical significance to study the seismic optimization design method of building structures with uniform response, which provides an important reference direction for the future design of building structures.

Intelligent Monitoring and Visualization System for High Building Nighttime Utilization Based on Image Processing

The rise of complex high-rise buildings has made building management increasingly challenging, especially the nighttime supervision of university laboratories. Idle occupation increases the risk of accidents and undermines campus sustainability. Effective occupancy detection is essential for optimizing campus building safety and energy efficiency. Environmental sensors for occupancy detection offer limited coverage and are costly, making them unsuitable for campuses. Surveillance cameras, as part of campus infrastructure, provide wide coverage. On this basis, we designed a detection algorithm that uses light brightness to assess nighttime building use. Experimental results showed that the algorithm achieves an average accuracy of 98.67%, enabling large-scale nighttime occupancy detection without the need for installing additional sensors, significantly improving the efficiency of campus building management. In addition, to address the limitations of indoor space representation in geographic information system (GIS) management models, this paper developed a comprehensive 3D GIS model based on a “building–floor–room” hierarchical structure, utilizing oblique photogrammetry and laser scanning technology. This study combined the detection results with real-world 3D data for visualization, providing a new perspective for the 3D spatiotemporal refinement of complex high-rise buildings, and providing a reference framework for the detection and analysis of other types of building environments.

Smart composite structural insulated panels (CSIPs) with embedded piezoelectric sensors for damage localization

Coastal communities face increasing vulnerability to natural disasters, necessitating resilient construction solutions. This study presents the development of smart composite structural insulated panels (CSIPs) embedded with piezoelectric sensors for damage localization in hurricane and tornado-prone areas. Experimental studies, such as four-point bending and in-panel compression tests, were conducted to analyse the shock response and failure modes of these smart CSIPs. Material characterization informs the development of finite element models for simulation, enhancing structural design. By integrating structural health monitoring capabilities, these smart CSIPs enable continuous tracking of their response to environmental factors, improving longevity and durability. Shock identification and localization in smart CSIPs are successfully demonstrated through numerical simulations and experimental validation, showcasing the potential step towards self-resilient structures in coastal regions. This novel approach provides real-time information on structural integrity, paving the way for improved safety and predictive maintenance of buildings and infrastructure utilizing CSIPs.

How to address the barriers to meaningful adolescent involvement in health research: A qualitative study.

The under-involvement of adolescents in health research has been attributed to multiple barriers faced by both researchers and adolescents. Despite identifying these barriers, the literature offers few solutions, mostly from the perspective of researchers. To address this, we conducted a qualitative study to explore effective strategies to address these barriers from the perspective of both researchers and adolescents. We conducted semi-structured interviews with adolescents (n = 25) and researchers (n = 25) from 14 countries. We included adolescents aged 10-24 years with experience of contributing to health research studies and health researchers with experience of engaging adolescents in health research. The interviews explored the mitigation strategies to commonly reported barriers to meaningful adolescent involvement for researchers and adolescents. Data were analyzed using reflexive thematic analysis. We identified three overarching strategies to address the commonly experienced barriers to adolescent involvement. First, participants suggested the need to plan for adequate resources, organizational support, capacity building, accessibility, compensation, and adolescents' safety. Second, they recommended building relationships by engaging the community, fostering trust and respect with adolescents, promoting teamwork, and maintaining transparent communication. Third, they proposed making involvement engaging for adolescents by creating a conducive environment, increasing their representation, using interesting methods, and addressing power dynamics. These findings build on the current best practices for adolescent involvement in health research by highlighting which strategies should be incorporated early on to plan for and prevent potential challenges to adolescent involvement.

Understanding human-obstacle interaction dynamics on staircases: Implications for emergency evacuation and fire safety in high-rise buildings

Interactions between system components, including human-obstacle, human-building, human-robot, and human-human, significantly impact public safety, system efficiency, and functionality. A deeper understanding of these systems is essential to enhance their resilience and effectiveness. Despite the significance of these interactions, empirical research is limited, particularly regarding behavioral mechanisms when interacting with obstacles on stairways. Our study examines the evacuation behavior of 90 evacuees on the staircase of a high-rise building under different temporary obstacle conditions to understand how key factors affect decision-making and motion behavior during stairway descent. The experiments considered obstacle shapes as control variables and analyzed evacuation time, inner and outer stair route choices, regional speed fluctuations, velocity-relative displacement, evacuation efficiency, and biomechanical analysis of occupants’ comfort. Results show that larger obstacle shapes, increased evacuation urgency time, and closer proximity to the wall lead to a higher proportion of the splitting to merging state before and after obstacle interaction. A strong inner stair route preference emerges in no-obstacle conditions, with significant mean speed differences in downward movements when encountering temporary obstacle. Three phases of average speed variations are observed: deceleration, acceleration, and continued deceleration for the outer route; and deceleration followed by uniform speed for the inner route. A method was developed to calculate regional speed fluctuation using average speeds from trajectories. Comfort analysis revealed that temporary obstacle on building stairways significantly impact safe evacuation. Overall, these findings enhance the understanding of crowd dynamics in high-rise building emergency evacuations, with implications for model development, architectural design, building operations, emergency preparedness, behavioral cognition, and fire safety.

Dynamic Analysis of a 600-m-High Supertall Building Subjected to Long-Period Ground Motions, Ordinary Ground Motions and a Super Typhoon: A Comparative Study

Long-period ground motions pose potential risks to the safety and serviceability of supertall buildings, which was not paid sufficient attention in the past of structural seismic design. To address this issue, the comparative study presented in this paper systematically investigates the dynamic responses of a 600-m-high supertall building subjected to natural hazards including long-period ground motions, ordinary ground motions and a super typhoon with the same return periods of 50 years. The seismic responses and wind-induced responses of the supertall building are obtained based on the building’s finite element model under earthquake excitation records and structural health monitoring system installed in the skyscraper, respectively. The long-period ground motions cause larger dynamic responses of the supertall building than those resulted by the ordinary ground motions or the super typhoon. This combined study of numerical analysis and field measurement provides valuable insights into the dynamic responses of a supertall building under different natural hazards, especially the long-period ground motions. The findings are expected to be of interest and practical use to the design of supertall buildings.

Comparisons of experimentally and numerically determined statistics for predicting low-occurrence wind speeds around a 1:1:2 block model

Accurate prediction of low-occurrence wind speeds around urban structures is crucial for safe building design. Although Large-eddy simulation (LES) is commonly used as a high-fidelity model as compared with the Reynolds-Averaged Navier–Stokes (RANS) simulations, the present validation process relies on the comparison of fundamental statistics of the mean and standard deviations. The discrepancies in LESs and wind-tunnel experiments (WTEs) are unclear in terms of physical quantities characterizing the unsteadiness of the simulated turbulent flow such as probability density and power spectral densities, and low-occurrence winds speeds. Therefore, this study aims to evaluate the effectiveness of LES in predicting unsteady wind behavior around a 1:1:2 block model. The study identifies prominent differences to improve the accuracy of unsteady numerical simulations especially for the purpose of predicting low-occurrence wind speeds. Various advection schemes in LESs were investigated, including first-order upwind, second-order linear, and dynamic interpolation schemes. The results show significant discrepancies, particularly in higher-order statistics and low-occurrence wind speeds, with WTE consistently exhibiting higher energy levels across all frequencies. These findings highlight the need to refine advection schemes to enhance their predictive accuracy. LESs with minimal numerical errors from discretization schemes can substantially improve urban wind assessments and contribute to the design of safer structures.

Advanced Predictive Structural Health Monitoring in High-Rise Buildings Using Recurrent Neural Networks

This study proposes a machine learning (ML) model to predict the displacement response of high-rise structures under various vertical and lateral loading conditions. The study combined finite element analysis (FEA), parametric modeling, and a multi-objective genetic algorithm to create a robust and diverse dataset of loading scenarios for developing a predictive ML model. The ML model was trained using a recurrent neural network (RNN) with Long Short-Term Memory (LSTM) layers. The developed model demonstrated high accuracy in predicting time series of vertical, lateral (X), and lateral (Y) displacements. The training and testing results showed Mean Squared Errors (MSE) of 0.1796 and 0.0033, respectively, with R2 values of 0.8416 and 0.9939. The model’s predictions differed by only 0.93% from the actual vertical displacement values and by 4.55% and 7.35% for lateral displacements in the Y and X directions, respectively. The results demonstrate the model’s high accuracy and generalization ability, making it a valuable tool for structural health monitoring (SHM) in high-rise buildings. This research highlights the potential of ML to provide real-time displacement predictions under various load conditions, offering practical applications for ensuring the structural integrity and safety of high-rise buildings, particularly in high-risk seismic areas.

Attention-based hybrid network for structural nonlinear response prediction under long-period earthquake

To develop structures that can respond to severe seismic events, models and tools are essential for quickly and precisely assessing and controlling the structure's performance. Long-period ground motions may pose potentially significant hazards to the safety of high-rise buildings located in basins or alluvial plains. However, traditional regression-based methods and pure data-driven machine learning methods are insufficient to address this problem of response prediction, which is characterized by non-stationarity, nonlinearity, and scarcity of samples. To tackle this issue, we propose a novel semi-explicit physics-informed deep learning method. The method incorporates the physical information into the model, thereby achieving better prediction accuracy. Through numerical and experimental validations, including hysteresis experimental data, the actual seismic response record of a six-story hotel, as well as data from single-degree-of-freedom structures across various periods and multi-story buildings in Los Angeles, Seattle, and Boston, we verified the proposed method significantly outperforms traditional regression-based methods and data-driven machine learning methods in terms of accuracy, efficiency, and robustness.

Cost-oriented fire safety design of urban structures

Providing adequate fire safety for urban buildings is a life-saving goal, and can be addressed in different ways. Reported studies integrating fire safety and life cycle cost (LCC) of conventional urban structures are rare. To address this gap, a cost–performance framework was developed. Four- and seven-storey steel (with and without fireproofing coatings) and reinforced concrete (RC) structures were designed, first for conventional loads and then fireproofed to meet their required fire-resistance rating. A range of sprinkler spacings, from 3200 mm (based on NFPA 13) to 12 000 mm, was configured. Fire was simulated using a fire dynamic simulation tool and the results were used to monitor evacuation plans and structural responses. The steel structures were additionally analysed under the assumption that they were not fireproofed. Human and financial damage costs were then estimated and the LCC for every case/scenario was determined. Interestingly, the results showed that the optimum LCC was not achieved for cases designed based on NFPA 13; instead, a sprinkler spacing of 6000 mm led to the optimum LCC. It was also found that steel structures without fireproofing do not have a higher LCC if the sprinklers are placed over a shorter distance. This result could be particularly beneficial for existing structures that are not fireproofed.

Integrating Web-Based Weather Data into Building Information Modeling Models through Robot Process Automation

The rapid evolution of digital technologies has revolutionized the architecture, engineering, and construction (AEC) industry, driving the wide-spread adoption of digital twins for structures. These virtual replicas, developed using Building Information Modeling (BIM) methodology, incorporate extensive information databases, proving indispensable for enhancing project management throughout a structure’s entire lifecycle and towards smart city development. As the impact of climate change continues to grow, hazardous weather alerts play a critical role as an early-warning system that notifies stakeholders of imminent threats, thereby influencing decision-making processes in construction projects. Surprisingly, despite its evident value, the integration of alert systems for hazardous weather conditions into BIM is often overlooked. To fill this gap, this paper proposes Robot Process Automation (RPA) protocols to automate the integration of real-time weather parameters into a structure’s BIM models. These very protocols are also used as alert systems, enabling the timely notification of stakeholders in the event of detected hazardous weather conditions. The effectiveness of the proposed methodology is demonstrated through its practical application in enhancing the safety of an actual building in Viña del Mar, Chile.

Preservation of Cultural Heritage Buildings in Central Plains via Novel Detection Approach

Modern nations recognize cultural heritage as an expression of culture and variety. Conserving and repurposing historic buildings has only been more popular in the last decades. Nonetheless, a considerable portion of cultural legacy is afflicted by structural concerns that endanger the safety of buildings and people. Challenges include a scarcity of resources, whether financial, human, or material. This initiative aims to employ deep learning (DL) approaches to preserve cultural heritage buildings, particularly in poor nations where these buildings are still being maintained. To overcome these issues this study proposed a novel Flower Pollination improved Resnet (FP-IResNet) to detect preservation of cultural heritage buildings. The image data were collected from China's cultural heritage buildings. The data is preprocessed using normalization. Histogram of Oriented Gradients (HOG) using extract the features for preprocessed data. The proposed method is implemented using Python software. The findings reveal that the suggested obtained greater performance in the detection of cultural heritage buildings than other traditional algorithms. The suggested concept enables the computerized preservation of cultural heritage buildings, leading to improved accuracy and reduced individual fault. Performance measures show that the model is successful in correctly categorizing and detecting heritage buildings that require preservation, with high accuracy (96.82%), precision (97.21%), recall (97.58%), and an F1 score (93.58%). The research emphasizes how computerized techniques could enhance the precision and effectiveness of CH conservation initiatives

Case study: structural feasibility analysis for a reinforced concrete building

Currently, the residential building sector in Brazil is experiencing continuous growth, despite the adverse impacts caused by 2 years of the Covid-19 pandemic. Therefore, there is a demand for structural projects that enable the execution of these works from a structure that is viable from the point of view of safety, good conditions of use, comfort and economy. Technical and practical knowledge is a fundamental requirement for a good project, combined with skill in a technological analysis and sizing tool. Having said that, the present work proposed to develop the analysis and structural design, with the help of the Brazilian software TQS®, of a multi-family residential building in reinforced concrete, using both linear and nonlinear static analyses. The central motivation was to address structural challenges and ensure that the design complies with Brazilian standards, thereby improving the quality and safety of residential buildings. The work sought to follow all the design steps of a structure in order to meet the requirements recommended by Brazilian standards. Based on the analyses, it was possible to adopt solutions for the various structural problems that arose during the design and, finally, obtain the final document with all the details of the structure.