Number Of Floors Research Articles

A new approach for urban flood risk assessment using coupled SWMM-HEC-RAS-2D model.

Urban stormwater management is crucial for mitigating the impacts of flooding in urban areas, as it plays a vital role in protecting infrastructures, ensuring public safety, and preserving environmental quality. In this study, we develop a new method to quantify urban flood risk (UFR) by integrating the components of hazard magnitude and vulnerability. The hazard is assessed using a coupled SWMM-HEC-RAS 2D model, where the flooding rate calculated by SWMM at each node is automatically fed into HEC-RAS-2D. This integration enables the calculation of the temporal and spatial variations of flood velocity and depth across the urban landscape, allowing for a more accurate representation of flood dynamics. Conversely, vulnerability is evaluated by considering several indicators, including building use, number of floors, construction quality, building age, and materials used in construction. Finally, the UFR as a multi-dimensional factor is calculated by multiplying the standardized hazard and vulnerability components. We successfully examined our proposed method in a part of Karaj City in the north of Iran. According to the findings, although the majority of southern regions of the study area with more deteriorated and aging-drainage-network are experiencing higher hazard magnitudes, the western areas have higher UFR than the others. Our proposed methodology provides a comparative UFR throughout the urban area and can help the managers to take the most effective decisions for flood management. Additionally, the method has the ability to weighting the priorities of hazard and vulnerability components in UFR assessment.

A cross-sectional study characterizing the prevalence of utility service outages across demographic characteristics and health correlates in New York City.

Utility services for electricity, gas, heat, and hot water are necessities for everyday activities (e.g., lighting, cooking, and thermal safety). Utility outages can threaten health; however, information is limited on the prevalence of electricity, gas, heat, and hot water outages in representative studies. We characterized infrastructure-related electricity, gas, heat, and hot water outages in New York City (NYC) and within subgroups. Using a representative 2022 survey of NYC adults (18+), we assessed the prevalence for 6+ hour utility outages and compared across building, demographic, and health subgroups. Building characteristics included age, number of floors, rental type, and owner/rental status. Demographics included household poverty, neighborhood poverty, and race/ethnicity. For health, we focused on cognitive impairment, electricity-dependent medical equipment use, and mental health conditions. Outages impacted 20% of NYC residents. Heat outages were nearly 3× and 2× more common in mid-rise and high-rise buildings respectively, vs. low-rise buildings. Similarly, hot water outages were 5× and over 6× more prevalent in mid-rise and high-rise residences. Renters faced 2× more heat and hot water outages compared with owners. Compared with low-poverty households, high-poverty households faced 2× more hot water outages. Residents with mental health conditions experienced more electricity (11% vs. 5%), heat (15% vs. 7%), and hot water (16% vs. 8%) outages compared with those without. NYC utility outage prevalence varied by type with heat and hot water being most common. Disparities across building, sociodemographic, and health characteristics were also larger and more frequent for heat and hot water outages.

A comparative analysis of LSTM, GRU, and Transformer models for construction cost prediction with multidimensional feature integration

ABSTRACT Construction cost prediction remains a complex challenge due to the multidimensional nature of construction data and external factors. The objective of this study is to identify the most effective deep learning model for accurately predicting construction costs by comparing the performance of LSTM, GRU, and Transformer models. Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer are advanced machine learning regression models widely utilized for data prediction tasks. This study investigates these models’ performance for construction cost prediction using a multidimensional feature framework. Through comprehensive evaluation and comparison, the Transformer model demonstrated superior performance, particularly excelling in handling complex feature interactions and long-sequence data. The LSTM model, while effective in capturing temporal dependencies, shows reliable performance but lags behind the Transformer in accuracy. The GRU model, although faster in training, proved less accurate and is less effective in handling outliers. Key features such as Total Area (TA), Site Area (SA), and Number of Floors (NF) were identified as significant predictors across all models, with the Transformer model proving particularly adept at capturing complex interactions. By integrating these features, this study contributes to improved cost management, thereby enhancing prediction accuracy and reliability.

Structural evaluation of selected reinforced concrete buildings within the scope of urban renewal in Izmir province

In this study, 160 reinforced concrete buildings located in Izmir, with different numbers of floors and construction years, within the scope of urban renewal between 2013 and 2018, were examined, and their risk status was determined according to the Principles for Identification of Risky Buildings 2013 (RYTEIE 2013). Reinforced concrete buildings were evaluated in detail considering characteristic properties such as concrete compressive strengths, column longitudinal reinforcement ratios, floor weights, ratio of column sectional areas to floor areas, natural vibration periods, ratio of column axial stresses to existing concrete compressive strength, and failure ratios. The structures examined within the scope of the paper were also grouped into two main categories: before and after 1998. In addition, all these buildings were evaluated in terms of Life Safety (LS) performance level with the linear elastic calculation method given in Specification for Structures to be Built in Disaster Areas 2007 (DBYBHY 2007), which was also in force between 2013 and 2018. It is observed that 72% of reinforced concrete buildings were determined as risky buildings according to RYTEIE 2013 and couldn’t provide the LS performance level according to DBYBHY 2007.

Modeling of Anthropogenic Heat Fluxes during the Heating Period in Major Russian Cities

Estimates of the anthropogenic heat flux (AHF) generated by megacities of the Russian Federation during the heating period are obtained. To calculate the AHF value, two-dimensional models were created taking into account the height, number of floors and the type of buildings for sixteen cities with a population of at least one million people. The source data is obtained from the OpenStreetMap open web mapping platform and the Yandex Maps website. Two algorithms for calculating AHF using building codes, thermophysical properties of enclosing structures and the difference between internal and external air temperatures are considered. The first algorithm uses the basic value of the required heat transfer resistance of the enclosing structure, the second – the calculated value of the specific characteristic of the consumption of thermal energy for heating and ventilation of the building. The AHF is assessed from the territory of the city within the administrative boundaries and from the urbanized territory, which is defined by multi-store buildings. Maps of the spatial distribution of AHF density are provided for the four largest megacities: Moscow, St. Petersburg, Novosibirsk and Yekaterinburg.

APPLICATION OF HEAT ENERGY ACCOUNTING DEVICES: PRIMARY ANALYSIS OF DATA

This article presents the primary analysis of data from the distribution accounting system of an apartment building, displays of individual groups of distributors, made a comparison between groups of consumers to identify factors affecting the accuracy of distribution, and formulated the main theses of further research. As part of the analysis, data from the distribution devices of the thermal energy distribution accounting system for heating an apartment building in Bila Tserkva were used. The distributed consumption of apartments and the indirect effect of the full-scale invasion of the Russian Federation on the behavior of heating consumers regarding the individual regulation of heat energy consumption for heating were analyzed. The possible influence of the location of the premises in relation to the number of floors on the level of heating consumption was also analyzed. For the analysis, the collected readings from the distributors were used in the primary form, and interpreted in the distributed form as the distributed energy consumption for space heating, as well as the specific distributed consumption for comparison between apartments without the influence of the difference in area. The grouping of premises was carried out by apartment, by floor, by apartment risers, and the general household energy consumption for heating. During the analysis of the process of interpretation of the displays, it was found that the consumption of apartments with individual heating systems was not taken into account, as a result of which it is impossible to sufficiently draw conclusions about the behavior of consumers regarding heating regulation. Further research is needed to clarify the values ​​of the correction coefficients when interpreting the readings, in general, to identify the feasibility of using some correction coefficients, to develop changes to the method of distribution accounting of heat energy for heating, to study the behavior of consumers at a given level of individual control of energy consumption.

Economic Viability of Photovoltaic Systems Based on the Number of Floors in Modular Multi-Family Housing in Korea

Economic Viability of Photovoltaic Systems Based on the Number of Floors in Modular Multi-Family Housing in Korea

Macro joint model for progressive collapse analysis of L-shaped CFST column frames

In this study, a macro joint model was developed to evaluate the progressive collapse resistance of L-shaped columns composed of concrete-filled steel tube (L-CFST) frames, accounting for joint performance. The methods used to calculate the spring stiffness in each part of the macro joint model were derived using the component method and the deformation coordination principle. Additionally, an L-CFST frame model consisting of fibre beam elements and macro joint models was created. For frame models with different parameters, the dynamic response under column removal was analysed; these parameters included the loading condition, connection type, the location of failed columns, the height and number of floors and the span–depth ratio of the steel beams. The macro joint model was found to have high accuracy in predicting progressive collapse resistance. The L-CFST column frames with novel side-plate reinforced connections fully exploited the catenary mechanism and Vierendeel action. All the variables affected the vertical displacement at the position of the failed columns, with the span–depth ratio having the most significant impact.

Seismic Vulnerability Assessment Mobile Application for Rapid Visual Screening

Similar to the Federal Emergency Management Agency's (FEMA) FEMA 154 method in the United States, which provides a rapid on-site survey method for building data to assess vulnerability indices and offers the RSV App mobile electronic form, this paper proposes the development of a mobile application for the visual rapid screening method for the Brazilian community of engineers and inspectors to assess the seismic vulnerability of existing structures. The simplified qualitative method adopted in the application originates in Japan, and a similar initiative was observed in that country at the symposium "Future of post-disaster assessment for buildings"; however, such an application was not found. Considering that the evaluation is carried out on-site, the application aims to facilitate the rapid screening process and disseminate the use of the method. Seismic-V was developed based on the Dart programming language and the Flutter framework. The Android Studio Giraffe IDE (Integrated Development Environment) | 2022.3.1 Patch 3 was chosen. Among the functionalities, it allows the structural verification for the five seismic zones with soil classes recommended in NBR 15.421. The user is required to provide real structural deterioration conditions, data, and structural characteristics such as number of floors, number of pillars, and respective cross-section that will support the seismic performance sub-index. Other features will be discussed in the paper and, at the end, a comparison will be developed between the results issued by the application and those calculated manually, with a discussion of the results. The proposal is relevant given the speed of application of the method and the initial screening that helps to prioritize existing buildings in future quantitative analytical approaches.

Seismic Resistance of Reinforced Concrete Building Frames Based on Interval Assessment of the Coefficient of Permissible Damage

The main method for assessing the seismic resistance of buildings in the standards of most countries is the linear-spectral method. This method allows for the calculation of the spatial model of a building for seismic load in the elastic range without resorting to direct integration of the equations of motion. Nonlinear characteristics of reinforced concrete structure materials are usually considered integrally using the reduction factor. However, the values of this factor in the Russian standards are not sufficiently substantiated, as the later studies show. To determine the coefficient of permissible damage (reduction factor), six reinforced concrete frames were considered, with different parameters such as span length, number of spans, and number of floors. The design parameters of beams and columns (section sizes, reinforcement, etc.) were preliminarily selected based on the calculation using the linear-spectral method. In the second stage, numerical modeling was carried out in the OpenSEES PC to implement the pushover analysis procedure. Then, the coefficient of permissible damage was estimated by processing the capacity curves obtained on the basis of nonlinear static calculation. The value of the sought coefficients is practically not affected by the number of stores of the frame; however, with an increase in the number of spans, the coefficient K1 increases, which is explained by a decrease in the plasticity of the system. On average, for the frames under consideration, the coefficient K1 was 0.526, which is 1.5 times greater than the coefficient proposed in modern Russian standards, K1 = 0.35. The results obtained on the basis of pushover analysis are compared with the coefficients K1 determined through the values of the average degree of damage (d) of the buildings according to the modified seismic scale MMSK-86. For various types of reinforced concrete frame buildings, K1 = 0.51 was obtained. It is recommended that the coefficient K1 for reinforced concrete frame buildings should be increased to a value of at least K1 = 0.5 in the Russian standard.

Relationship Between Spatial Form, Functional Distribution, and Vitality of Railway Station Areas Under Station-City Synergetic Development: A Case Study of Four Special-Grade Stations in Beijing

The integration of railway stations into urban environments necessitates a detailed examination of their vitality and influencing factors. This study assesses urban vitality around four major railway stations in Beijing utilizing a variety of analytical models including Ordinary Least Squares, Geographically Weighted Regression, Multi-Scale Geographically Weighted Regression, and machine learning approaches such as XGBoost 2.0.3, Random Forest 1.4.1.post1, and LightGBM 4.3.0. These analyses are grounded in Baidu heatmaps and examine relationships with spatial form, functional distribution, and spatial configuration. The results indicate significant associations between urban vitality and variables such as commercial density, average number of floors, integration, residential density, and housing prices, particularly in predicting weekday vitality. The MGWR model demonstrates enhanced fit and robustness, explaining 84.8% of the variability in vitality, while the Random Forest model displays the highest stability among the machine learning options, accounting for 76.9% of vitality variation. The integration of SHAP values with MGWR coefficients identifies commercial density as the most critical predictor, with the average number of floors and residential density also being key. These findings offer important insights for spatial planning in areas surrounding railway stations.

Intelligent Forecast Model for Project Cost in Guangdong Province Based on GA-BP Neural Network

Project cost forecasting is a complex and critical process, and it is of paramount importance for the successful implementation of engineering projects. Accurately forecasting project costs can help project managers and relevant decision-makers make informed decisions, thereby avoiding unnecessary cost overruns and time delays. Furthermore, accurately forecasting project costs can make important contributions to better controlling engineering costs, optimizing resource allocation, and reducing project risks. To establish a high-precision cost forecasting model for construction projects in Guangdong Province, based on case data of construction projects in Guangdong Province, this paper first uses the Analytic Hierarchy Process (AHP) to obtain the characteristic parameters that affect project costs. Then, a neural network training and testing dataset is constructed, and a genetic algorithm (GA) is used to optimize the initial weights and biases of the neural network. The GA-BP neural network is used to establish a cost forecasting model for construction projects in Guangdong Province. Finally, by using parameter sensitivity analysis theory, the importance of the characteristic values that affect the project cost is ranked, and the optimal direction for controlling the project cost is obtained. The results showed: (1) The determination coefficient between the forecasting and actual values of the project cost forecasting model based on the BP neural network testing set is 0.87. After GA optimization, the determination coefficient between the forecasting and actual values of the GA-BP neural network testing set is 0.94. The accuracy of the intelligent forecast model for construction project cost in Guangdong Province has been significantly improved after optimization through GA. (2) Based on sensitivity analysis of neural network parameters, the most significant factor affecting the cost of construction projects in Guangdong Province is the number of above-ground floors, followed by the main structure type, foundation structure, above-ground building area, total building area, underground building area, fortification intensity, and building height. The results of parameter sensitivity analysis indicate the direction for cost control in construction projects. The research results of this paper provide theoretical guidance for cost control in construction projects.

Prediction models for modal parameters of high-rise buildings based on the latest statistics of field measurements

The values of natural frequencies and damping ratios are usually involved as prerequisite information for the wind-resistant and anti-earthquake design of high-rise buildings. Although many related prediction models have been proposed, a majority of them were derived based on field measurements for buildings with a height usually lower than 200 m, or occasionally between 200 m and 300 m. Over the last two decades, continuously increasing field studies have been conducted on ever-developing high-rise buildings, which provides a good opportunity to further examine the dynamic properties of such skyscrapers from a statistical perspective. In this study, a database of the natural frequencies and damping ratios for 293 high-rise buildings is established, consisting of public data sources that cover 60 super-tall buildings whose heights exceed 300 m and another 61 buildings whose heights exceed 200 m. Based on the database, the dependence of modal parameters (natural period, natural frequency and damping ratio) on various influencing factors like building height, number of floors, structural system, construction material, storey height, etc. is investigated comprehensively. Subsequently, statistical prediction models for modal parameters are proposed and compared with those documented in literature or codes/standards. It is expected that the results presented in this study would provide useful insights to better understand the dynamic properties of low-to high-rise buildings.

Occupancy and equipment usage prototype schedules for building energy simulations of office building types in China

The accuracy of occupancy and energy-consuming equipment schedules significantly influences building energy simulations. Existing standards provide generalized schedules that do not fully capture variations across different office building types and periods. This study utilizes questionnaire data from office buildings across four Chinese cities to extract refined prototypes for occupancy and equipment usage schedules using hierarchical clustering analysis. Specific schedules are developed for summer workdays, winter workdays, summer weekends, and winter weekends, covering HVAC, lighting, and office equipment. For instance, the extracted lighting schedule increases annual energy consumption intensity by 25.35% compared to standard schedules. Additionally, XGBoost models identify key factors influencing equipment usage; for HVAC, floor number emerges as most significant. The study's prototypes offer more realistic inputs for building energy simulations, enhancing accuracy and guiding energy-efficient building design and management strategies in China.

Nonstructure–structure interaction with respect to mass ratio: Numerical analysis and free vibration tests

The dynamic interactions between nonstructural components (NCs) and the main building structure cannot be neglected because the dynamic properties of the main structure are modified, particularly for heavy NCs. The mass ratio (MR) is a parameter that incorporates the nonstructure–structure interaction (NSI) between the NCs and main structure. In this study, to investigate the seismic interaction in terms of the MR, a four-story steel moment frame was constructed and free vibration tests were carried out. The equivalent mass of the NCs was connected to various floor levels, neglecting their stiffness and damping. The vibration mode and period of the steel frame were obtained numerically. At MR = 25 %, the period generally increased with increasing MR. The modal contribution factors in terms of the roof displacement and base shear were analyzed. The findings show that it would be safe to consider the 1st mode only when using the roof displacement, whereas more modes should be considered when the base shear is used. The experimental damping ratios correlated with the magnitude of the MR without a representative trend. The damping ratio generally increases with an increase in the number of floors hosting additional masses. The experimental period increased almost linearly with the MR irrespective of the number of floor-hosting masses. An equivalent single-degree-of-freedom model is developed to predict the frame period. The predicted periods generally matched the experimental results. The normalized height of the equivalent model and period ratio vary with the MR and are correlated with the number of floors hosting the mass. The results of this study will be beneficial for the seismic design of building structures with heavy NCs.

Analysis of carbon emission characteristics and establishment of prediction models for residential and office buildings in China

Intensifying environmental issues make carbon reduction crucial, especially in the construction industry. Embodied carbon emissions (ECE) in buildings characteristically involve concentrated, intensive emissions over a short period. Implementing energy-saving and carbon-reducing technologies has raised ECE rates. Therefore, analyzing carbon emission (CE) characteristics during the production and construction phases is necessary. Currently, essential data on ECE is lacking, the calculation methods are complex, and ECE's characteristics and influential factors in residential and office buildings remain unclear. Moreover, there is a lack of prediction models for ECE in the construction phase that apply to current construction methods and CE factors. The research on prediction models for ECE has primarily focused on the production phase but has paid little attention to the contribution of non-structural materials to embodied carbon (EC). In this study, reinforced concrete residential and office buildings in hot summer and cold winter regions (located in the southeast part of mainland China, with a subtropical monsoon climate) were considered typical types, and the CE characteristics of two main phases, the production and construction phases, were analyzed. Based on critical features, such as the number of floors and the basement, prediction models for ECE in residential and office buildings were established at the design stage. The models were validated with mean absolute percentage error (MAPE) values below 4 %, and coefficient of variation (root mean square error) (CV [RMSE]) values less than 0.02. The prediction models enabled a rapid estimation of ECE, encouraging designers to consider EC in the early design stages.

Urban Morphology Classification and Organizational Patterns: A Multidimensional Numerical Analysis of Heping District, Shenyang City

Prior studies have failed to adequately address intangible characteristics and lacked a comprehensive quantification of cultural dimensions. Additionally, such works have not merged supervised and unsupervised classification methodologies. To address these gaps, this study employed multidimensional numerical techniques for precise spatial pattern recognition and urban morphology classification at the block scale. By examining building density, mean floor numbers, functional compositions, and street block mixed-use intensities, alongside historical and contemporary cultural assets within blocks—with assigned weights and entropy calculations from road networks, building vectors, and POI data—a hierarchical categorization of high, medium, and low groups was established. As a consequence, cluster analysis revealed seven distinctive morphology classifications within the studied area, each with unique spatial configurations and evolutionary tendencies. Key findings include the dominance of high-density, mixed-use blocks in the urban core, the persistence of historical morphologies in certain areas, and the emergence of new, high-rise clusters in recently developed zones. The investigation further elucidated the spatial configurations and evolutionary tendencies of each morphology category. These insights lay the groundwork for forthcoming studies to devise morphology-specific management strategies, thereby advancing towards a more scientifically grounded, rational, and precision-focused approach to urban morphology governance.

딥러닝 기반의 주차 층수 탐지 애플리케이션의 개발

In this study, we develop an application to detect the number of indoor parking floors using deep-learning techniques. Previously, floor-number predictions relied solely on simple atmospheric pressure data. In this study, we expand this solution to create applications for both Android and iPhone platforms. This ensures that user experience is optimized for the unique characteristics of mobile applications on each platform. Our approach renders parking-space navigation more efficient and intuitive, thus significantly enhancing user convenience. Ultimately, this development contributes positively to environmental, social, and governance fields.

Stern, Gamow, and the Shabbat Elevator Effect

Abstract In addition to being known for their other work, physicists George Gamow and Marvin Stern also published an analysis of an intriguing problem regarding their perception that although both worked on different floors, it seemed that the first elevator to arrive on their respective floors was traveling in the opposite direction of their destination. In this manuscript, a fictitious conversation occurs between the two in regards to the probability of the elevator stopping on every floor (which they term the Shabbat Effect). One claims that it is the number of elevator occupants that predominates whereas the other claims it is the number of floors in the building that predominates in giving rise to this effect. Equations for estimating the probability of the Shabbat Effect are developed and analyzed. The resulting equations show that the relationship between the number of elevator occupants and floors is surprisingly complicated. Consequently, no general rule can be given for whether the probability of the Shabbat Effect increases or decreases for an arbitrary change in the number of occupants and floors. However, it discovered that for similar changes in the number of occupants and floors, it is the number of floors that predominates in determining the probability of the elevator stopping on every floor.

Seismic vulnerability analysis of hospitals and school buildings considering the Gaussian regression algorithm

This study combines the Gaussian regression algorithm to propose a nonlinear regression model that can be used to evaluate the seismic vulnerability of regional hospitals and school buildings. The failure features and disaster mechanisms of hospital and school buildings affected by the Jishishan (JSS) earthquake on December 18, 2023, and the Wenchuan (WC) earthquake on May 12, 2008, were reported. The samples of 252 damaged buildings (37 hospitals and 215 schools) in Dujiangyan city affected by the Wenchuan earthquake for which the author participated in the survey were collected and counted. Hospitals and school buildings were classified according to the types of reinforced concrete (RC) and masonry structures, and earthquake vulnerability probability matrices for hospital and school building clusters were established considering the actual failure probabilities. A total of 2103,213 acceleration records (from the Wenchuan earthquake) monitored by 12 real stations were selected. Using dynamic time history and response spectrum analysis methods, time history and spectrum curves were generated, considering the influence of different seismic directions. Using the developed Gaussian regression model, vulnerability comparison curves and parameter matrices considering multiple regression algorithms were generated. This paper considers the effects of the construction year and number of floors on the seismic vulnerability of hospital and school buildings. Seismic vulnerability curves and failure probability matrices were generated and established on the basis of actual structural failure probabilities. According to regression and vulnerability surface analysis, the evaluation results of the proposed model are highly consistent with actual field observations.