Three-story Building Research Articles

Experimental tests of dynamic effects on nodal connections of a three-story building made of LSTC

The article presents the results of vibration tests of a three-story building made of light gauge steel constructions, a comprehensive analysis of the results of experimental data and calculation results of a representative building, as well as the possibility of using LGSC systems in construction in the Kyrgyz Republic.

Numerical and Experimental Study of Seismically Excited Scaled Structure with Active Mass Damper

In recent years, the development and implementation of artificial intelligence (AI) have attracted tremendous attention. The implementation of active control systems for building structures can be improved by using an AI controller. Non-AI controllers such as the Linear Quadratic Regulator (LQR) controller require full state variables of the structure to be measured, which is rarely feasible. To address this problem, two AI models, namely, artificial neural network (ANN) and fuzzy logic (FL), have been tried as AI-based controller in various studies. In the present study, both AI models were investigated to see their practicality and effectiveness. The AI models were implemented to control an active mass damper (AMD) in a three-story prototype-sized building. The simulation results from the structure with an LQR controller were used as benchmark and training data for the AI models. The results of the study demonstrated that although both AI models could reduce the structure responses, ANN was more practical and effective compared to FL as an AI-based controller for the given structure. Furthermore, the effectiveness of an ANN-based AMD was also shown by the experimental results.

Cost optimization in structural design for reinforced concrete frames using Jaya algorithm

This study analyzes and optimizes the structural design of three-dimensional (3D) reinforced concrete framestructures to minimize the amount of two main materials, including concrete and steel reinforcement, used inreinforced concrete frames. Jaya algorithm was developed based on evolutionary algorithms to build the structural optimization model. The objective function (minimum material cost) with variables being column andbeam cross-sections was set up. In which, the constraints related to the maximum internal force and displacement being in the structural members satisfy limited strength and serviceability requirements. A subroutine written in Python was used to connect the optimization process to the structural analysis software, ETABS. The 3D reinforced concrete frame of a three-story building was selected for cost and design optimization analysis. In which, the optimization problem was solved in cases of three different maximum numbers of iterations, including 20, 35, and 50. In each iteration level, a setting of five independent runs was performed. The subroutine proved to be fast, robust, and convenient manner for solving optimization problems in designing 3D reinforced concrete frames. In which, the best optimal cost might be obtained after twenty iterations and the better convergence behavior occurred at higher numbers of iterations. The study results showed that, for this reinforced concrete frame, applying optimal design led to a materials cost reduction (success rate) by 33.67% compared to that of the conventional design. This success rate would fluctuate according to different nations’ design codes.

Determining Payback Period and Comparing Two Small-Scale Vertical Axis Wind Turbines Installed at the Top of Residential Buildings

In recent years, residential buildings have seen a notable increase in energy consumption. To address this, it is crucial for researchers to invest in renewable energy technologies, aiming to develop highly sustainable and nearly-zero energy buildings. Many countries are started to commit to this goal, seeking to phase out fossil fuels due to their harmful environmental effects. Wind energy stands out as a promising renewable resource, especially in areas with strong wind patterns. This study focuses on a case in Karaburun, Izmir province, Türkiye, where annual wind speeds range from 6 to 8 m/s and evaluates the performance of two types of small-scale Vertical Axis Wind Turbines (VAWTs) in reducing energy consumption in a three-story residential building, along with associated costs. Utilizing advanced simulation tools like ANSYS Fluent and DesignBuilder Software, the study examines Ice-Wind VAWTs and Savonius VAWTs. The findings reveal that installing 15 Ice-Wind VAWTs on the building's roof can reduce energy consumption by approximately 22.5%, with each turbine costing about $2000 and a payback period of around 14.57 years. Conversely, using 15 Savonius VAWTs can reduce energy consumption by 36%, with each turbine costing about $2300 and a payback period of around 8.93 years. These results indicate that the Savonius turbine offers a faster return on investment compared to the Ice-Wind turbine under the specified conditions. Overall, this study highlights the significant benefits and cost implications of integrating renewable energy solutions like VAWTs into residential buildings.

Evaluasi Desain Struktur Gedung Pondok Pesantren At-Tibyan Deli Serdang

The structural design of this three-story Islamic boarding school building, the results obtained after analysis include; risk category II so that the earthquake priority factor is 1. The soil site class is soft soil. In the structural system, it is designed using a special moment resisting frame system (SRPMK). The seismic design category obtained is category D. Seismic inspection starting from checking irregularities, diaphragm design, checking the number of structural vibration variations, determining the fundamental period of the structure, seismic response coefficient values, seismic base shear, checking the deviation between floors is carried out based on SNI 1726-2019. for column elements, beams and also plates in existing conditions after analysis have met the requirements based on SNI 2847-2019. The conclusion in this final project research is based on the results of the analysis that the design of the Attybian Islamic boarding school building still meets the requirements based on SNI 1726-2019 concerning procedures for earthquake resistance planning for buildings and non-buildings and also for beam, column and plate elements have met the requirements based on SNI 2847-2019 concerning reinforced concrete structures. However, for the beam elements for the reinforcement requirements, it is still too wasteful so that in this analysis, the amount of reinforcement is reduced because in the planning principle it must be safe and economical.

The impact of using PCM layers in simultaneously the external and internal walls of building on energy annual consumption

This article conducted a comprehensive study on a three-story residential building in Yazd, Iran, which was meticulously modeled using DesignBuilder. The primary objective was to investigate the impact of BioPCMs (Bio Phase Change Materials), which are environmentally friendly materials, on the building’s thermal performance. For this purpose, BioPCM® M182/Q21 was selected due to its effectiveness in enhancing energy efficiency. The study involved a practical comparison of the building’s energy consumption under three scenarios. The first scenario represented the baseline condition where no PCM was used. The second scenario incorporated PCM into the external walls, while the third scenario extended the use of PCM to both the external and internal walls. In the second scenario, where PCM was applied only to the external walls, there was a 9% reduction in annual energy consumption. The third scenario, which utilized PCM in both the external and internal walls, resulted in a reduction of 15.5% in annual energy consumption. Additionally, when PCM was used in both the external and internal walls, the total energy consumption for cooling and heating the building decreased by 5.4% and 18.9%, respectively.

An inerter-enhanced bistable nonlinear energy sink for seismic response control of building structures

Structural control strategies have attracted great attention in reducing unwanted structural responses. To mitigate the vibrations of seismically excited buildings, this paper proposes a novel vibration control device: an inerter-enhanced bistable nonlinear energy sink (BNESI). A three-story building structure is selected as an example, where the BNESI is attached to the top of the structure and linked to the top second floor of the structure, to explore the control effect of the device under earthquakes. The restoring force of the BNESI and equations of motion for the BNESI-controlled system are derived first. Subsequently, a multi-objective optimization design method of the BNESI is proposed and applied in building structures. The input energy robustness of the BNESI is then accessed by various initial velocities subjected to the structures. Moreover, the seismic vibration mitigation capacity of the BNESI is systematically investigated under a group of near-field and far-field excitations. Analysis results show that the proposed design algorithm for BNESI can be an attractive alternative to effectively obtain optimal parameters of the control device. The BNESI is less sensitive to the input energy levels, and can stably capture and dissipate energy from the primary structure. Also, the BNESI system provides good control effectiveness with less sensitivity to the uncertainty of seismic excitations. In addition, with less standard deviation, the damper stroke of the BNESI is only about a quarter of that of the TMD under the same excitation. Moreover, the damage to the primary structure has a negligible effect on the control effect of the BNESI system. Meanwhile, the BNESI can decrease the energy dissipation demand on the structure, and the energy distribution mode of the BNESI system shows less sensitivity to the frequency decrease of the structure.

Effectiveness of Suspended Lead Dampers in Steel Building Structural Model Subjected to Impact Load

This paper delves into an in-depth experimental investigation focusing on the dynamic behavior of steel frame buildings employing passive damping through suspended lead dampers. The primary objective revolves around scrutinizing a three-story steel frame building model to elucidate the effects of integrating lead dampers into the outer tubular square-section columns. By strategically embedding these dampers, the study aims to showcase the resultant reductions in both acceleration and displacement. To execute this analysis, an impact load is precisely applied to the mid-center of the middle column along the x-axis of the first story of the steel frame. The experimental setup employs six wireless accelerometers strategically positioned across the frame to capture comprehensive data on its response at diverse locations. Various quantities of lead dampers are systematically incorporated into each testing scenario to gauge the extent of passive damping’s influence on the structural response of steel buildings to impact loads. Throughout the experiments, acceleration-time relations are meticulously recorded at each story, facilitating a comparative assessment of outcomes with and without the presence of lead dampers. The findings of the study underscore a marked decrease in vibration levels at higher floors of the steel model subsequent to the installation of lead dampers within the structure. Furthermore, a noteworthy trend emerges indicating that an augmented number of lead dampers within the building yields a commensurate decrease in vibration amplitude. This detailed investigation offers valuable insights into the efficacy of passive damping mechanisms, thereby contributing to the advancement of seismic-resistant structural design practices.

Analytical solution to 2D dynamic soil-tunnel-building interaction under seismic SH-wave excitation considering the influence of the stiffness of tunnel and building's foundation

This investigation examines the structure-soil-structure interaction (SSSI) problem between a tunnel and nearby aboveground building, considering the impact of the stiffness of tunnel and building's foundation. A two-dimensional (2D) model is utilized, which includes a flexible circular tunnel and a three-story building anchored by a flexible semi-circular foundation embedded in a homogeneous half-space. The response of the system to incident plane SH-wave excitation is solved analytically using the wave function expansion method. The analysis focuses on how the system response is affected by the stiffness of the tunnel and the building's foundation. Tunnel stiffness plays a leading role in tunnel response analysis. The model with a rigid tunnel and rigid foundation can offer a reasonable system response overall, but it fails to accurately represent tunnel deformation and stress distribution. Depending on the stiffness of the building's foundation, the maximum value of the inter-story displacement of the first floor can be overestimated by around 70 %, with even greater overestimations for higher floors. The results help understand observed seismic behaviors and provide insight into the effects of foundation and tunnel stiffness on the tunnel-building interaction system.

Mapping the Potential of Zero-Energy Building in Greece Using Roof Photovoltaics

The present study investigates the incorporation of renewable rooftop photovoltaic systems in fully electrified residential buildings and estimates the zero-energy demand building potential in relation to the climatic data of Greece. Specifically, the aim of the analysis is to calculate the maximum possible number of stories and therefore the total building height for a complete transformation to zero-net-energy building. The energy analysis, which is conducted using the DesignBuilder software, focuses on single-floor up to seven-story buildings. The importance of the present work lies in the acknowledgment of the diversity of the Greek residential sector, the adherence to national energy policies, and the European goal of fully electrified buildings. The examined case studies are equipped with electrically driven air-to-air heat pumps serving the space heating and cooling demands and with an air-to-water heat pump covering the domestic hot water requirements. The investigated locations are the four main cities of Greece, Athens, Thessaloniki, Chania, and Kastoria, which represent the country’s four climatic categories. The conducted analysis allows for the mapping of the zero-energy building potential for the climatic data of Greece, demonstrating the possibility of striking a positive building energy balance through the integration of on-site renewable energy sources and the production of necessary electrical energy. The novelty of the present work lies in the identification of a key factor, namely, the building height, which determines the feasibility of transforming multifamily buildings into zero-energy buildings. According to the analysis results, the critical number of stories is calculated at six for Chania, five for Athens, four for Thessaloniki, and two for Kastoria. Regarding a three-story residential building, the incorporation of a renewable photovoltaic system can result in an annual surplus electricity production of 13,741 kWh (Chania), 10,424 kWh (Athens), and 6931 kWh (Thessaloniki), and a corresponding coverage of 100% (Chania), 69.0% (Athens), 38.9% (Thessaloniki) and 0% (Kastoria).

Comprehensive analysis on building performance enhancement based on selective split-band modulated adaptive thermochromic windows

Thermochromic technology has exciting potential for building applications as it passively modulates its solar transmittance. Existing smart window technologies lead to unnecessary increases in lighting energy consumption during spectral modulation and limited application scenarios. Therefore, spectrally selective modulation is a key direction to improve the performance of smart windows. In this paper, a three-story office building is established in EnergyPlus, and how the characteristic parameters and spectral modulation properties affect the effectiveness of thermochromic windows are thoroughly investigated through actual spectra and hypothetical split-band modulation spectra. A comprehensive human-centered evaluation system is proposed, multiple light zones are divided to investigate in detail the light and thermal environment and comfort performance of office spaces with different orientations of near-window areas and verify the energy-saving effect. The impact of thermochromic adaptive windows on building part loads and equipment efficiency is discussed and the matching of this passive technology with renewable photovoltaics is briefly mentioned. Based on the two-way synergistic perspective of materials and buildings, the results inform the application of smart windows in specific scenarios, throwing light on more cross-cutting research.

Adaptive Real Time Efficient Control Algorithm for Buildings Under Wind and Earthquake Forces

The study aims to tackle two key issues: the dependency of optimal controllers on available training data and their limited capability in managing multi-component structural responses. Real Time Fuzzy Logic Efficient Control system (RTFLEC) was developed to address these challenges. This system integrates a fuzzy logic inference system with a multi-verse optimization algorithm and utilizes real time training for real-time optimal controller derivation. It incorporates decentralized systems and optimal controller memory concepts and employs a magnetorheological damper (MR) for adjustable vibration control. Two case studies were conducted to evaluate the RTFLEC system's effectiveness. The first study focused on a three-story shear building under seismic loads, while the second analyzed a 76-story building facing multi-directional wind loads. The results showed that RTFLEC system reduced structural drifts by 28.33% and 51.5% during near-field and far-field earthquakes, respectively. It also decreased structural acceleration by 27.66% and 53% during near-field and far-field earthquakes, respectively. For wind-induced structures, it reduced story displacement by 37.5%, 14.5%, and 12% against across-wind, along-wind, and rotational forces, respectively. RTFLEC system demonstrated robust performance against uncertainties in external excitations, providing exceptional structural responses. Its key advantage lies in real-time adaptability, requiring minimal training data and computational processing. These findings highlight its significant potential in enhancing the performance of adaptive vibration control systems under unpredictable real-world loads.

Dynamic Characterization of a Full-Scale Three-Story Mass Timber Building Structure

Dynamic Characterization of a Full-Scale Three-Story Mass Timber Building Structure

Transfer Learning-Based Structural Damage Identification for Building Structures with Limited Measurement Data

Structural damage detection is crucial for ensuring the safety of civil building structures in operational environments. Recently, deep learning-based methods have gained increasing attention from engineers and researchers. The performance of conventional deep learning methods for structural damage detection relies on a large number of labeled training datasets. However, it is difficult or/and impossible to obtain sufficient datasets to cover various damage scenarios for in-service structures. A little research has been conducted to identify both the damage severity and location with limited labeled measurement data. A novel transfer learning-based method for structural damage identification with limited measurements has been proposed utilizing frequency response functions (FRFs) as the input. The real structure is regarded as the target domain and its numerical model is as the source domain. The samples for various damage scenarios are generated using the numerical model, and a designed deep convolutional neural network (CNN) is pre-trained. The knowledge of the pre-trained network is transferred to identify the damage location and severity of the real structure using limited measurement data. Numerical and experimental studies have been conducted on a three-story building structure to verify the performance of the proposed method. To understand transfer learning and model interpretability, the t-SNE feature visualization is adopted to show the feature distribution changes during transfer learning. Numerical and experimental results show that the proposed approach outperforms conventional CNN models, and it is effective and accurate in identifying structural damage location and severity in real structures with limited measurement data.

Optimization of Elevator Usage by Image Processing for Optimal Energy Conservation

An efficient vertical transit system is a crucial feature of contemporary office buildings. Modern machine learning (ML) algorithms make it simple to optimise lift control strategies. This study proposes a revolutionary way to use Raspberry Pi camera module data for the best possible dispatching of traditional passenger lifts. It is assumed that an image processing system processes a real-time video to ascertain the quantity of people and objects utilising the lifts and waiting for a lift vehicle in the halls. These numbers are assumed to be connected to a specific uncertain probability. The efficiency of our unique lift control algorithm is derived from the need to serve a crowded floor completely, sending as many lifts as possible there and filling them to the maximum weight permitted, in addition to the probabilistic utilisation of the number of people and/or items waiting. The suggested technique introduces the idea of the effective number of persons and items to account for the uncertainty that may arise from the image processing system's imperfection. Reducing wait time, energy conservation and optimization are main the goals of this research. The proposed approach was implemented, and the simulation results showed that the passenger journey time was reduced. A three-story office building was the intended application for the prototype model. Key Words: Elevator, Raspberry Pi, Machine Learning, Optimization, Image Processing

Anomaly detection in structural dynamic systems via nonlinearity occurrence analysis using video data

Dynamic loads in disaster events, such as earthquakes, often cause structural damages that severely affect serviceability and even reduce the safety of civil structures or render them unsafe. Most of these damage scenarios can cause the transition from a linear to nonlinear behavior in structural dynamics. Therefore, extracting such nonlinear behaviors is an effective strategy for structural anomaly detection. In recent years, the rapid development of media devices has led to the widespread use of video data, primarily because of the ease of acquisition and lack of contact measurement. This study developed a novel method for detecting anomalies due to structural nonlinearity from video data that could capture target dynamic behaviors via non-contact measurement. The method is based on optical flow, extended repulsive force networks, and morphological operations for extracting singularities due to nonlinear events in the estimated motion vector field in video data. Subsequently, shaking table tests on a three-story shear building model containing a novel controllable hinge bearing verified the effectiveness of the developed method. The results of the feature enhancement process demonstrate the accurate localization of anomalous regions and effective mitigation of unwanted interference using the proposed method. The proposed method facilitates the use of video-based technology to evaluate rapid damage conditions post-earthquake.

Optimizing the dimensional ratio and orientation of residential buildings in the humid temperate climate to reduce energy consumption (Case: Rasht Iran)

Today, the increase in the number of cities and greenhouse gas increases has led to the intensification of global warming. The construction industry has had a significant impact on this crisis. Therefore, using passive solutions such as optimizing building proportions and proper orientation in any climate can significantly help reduce building energy consumption and greenhouse gases. The research aims to optimize the proportions of residential buildings and their orientation to minimize the building's energy consumption in the city of Rasht with a moderate climate. For this purpose, a three-story building with dimensions of 1:1 was selected, and the energy consumption of the building, including the load of cooling, heating, and lighting, was calculated in the north-south and east-west dimensions up to the ratio of 1–10. This research has used validated software to calculate energy consumption. Then, the most optimal mode was selected in terms of proportionality; the energy consumption of the building with a different orientation compared to the geographical north was calculated for each degree up to 360°. The results showed that the energy consumption of the building with a ratio of 1:4 with east-west elongation was 155.29 kWh/m2, which has the lowest energy consumption compared to other modes and is also compatible with the construction and building industry. Also, the building with zero-degree orientation to the geographic north and east-west elongation has the lowest carbon dioxide production and annual energy consumption, which are 2107 kg/m2 and 146.28 kWh/m2, respectively. By using two primary parameters, i.e., building orientation and aspect ratio, this research presents a valid innovation in optimizing the energy consumption of buildings. This research is focused on the detailed analysis of the environmental performance of a building in Rasht climate and determining the best form and orientation to reduce energy consumption and environmental efficiency.

Damage Detection with Data-Driven Machine Learning Models on an Experimental Structure

Various techniques have been employed to detect damage in civil engineering structures. Apart from the model-based approach, which demands the frequent updating of its corresponding finite element method (FEM)-built model, data-driven methods have gained prominence. Environmental and operational effects significantly affect damage detection due to the presence of damage-related trends in their analyses. Time-domain approaches such as autoregression and metrics such as the Mahalanobis squared distance have been utilized to mitigate these effects. In the realm of machine learning (ML) models, their effectiveness relies heavily on the type and quality of the extracted features, making this aspect a focal point of attention. The objective of this work is therefore to deploy and observe potential feature extraction approaches used as input in training fully data-driven damage detection machine learning models. The most damage-sensitive segment (MDSS) feature extraction technique, which potentially treats signals under multiple conditions, is also proposed and deployed. It identifies potential segments for each feature coefficient under a defined criterion. Therefore, 680 signals, each consisting of 8192 data points, are recorded using accelerometer sensors at the Los Alamos National Laboratory in the USA. The data are obtained from a three-story 3D building frame and are utilized in this research for a mainly data-driven damage detection task. Three approaches are implemented to replace four missing signals with the generated ones. In this paper, multiple fast Fourier and wavelet-transformed features are employed to evaluate their performance. Most importantly, a power spectral density (PSD)-based feature extraction approach that considers the maximum variability criterion to identify the most sensitive segments is developed and implemented. The performance of the MDSS selection technique, proposed in this work, surpasses that of all 18 trained neural networks (NN) and recurrent neural network (RNN) models, achieving more than 80% prediction accuracy on an unseen prediction dataset. It also significantly reduces the feature dimension. Furthermore, a sensitivity analysis is conducted on signal segmentation, overlapping, the treatment of a training dataset imbalance, and principal component analysis (PCA) implementation across various combinations of features. Binary and multiclass classification models are employed to primarily detect and additionally locate and identify the severity class of the damage. The collaborative approach of feature extraction and machine learning models effectively addresses the impact of environmental and operational effects (EOFs), suppressing their influences on the damage detection process.

CFD simulation of pumping ventilation with two asymmetrically arranged openings on the rear wall in a three-story isolated building

Pumping ventilation, driven by periodic vortex shedding, can effectively improve the ventilation rate of single-sided ventilation. Previous studies on pumping ventilation mainly considered symmetric opening conditions, while the asymmetric opening distribution has not been well studied. Therefore, this study investigates the pumping ventilation of an isolated three-story building with asymmetric opening conditions using computational fluid dynamics (CFD) simulations. Unsteady Reynolds-averaged Navier-Stokes (URANS) method with SST k-ω turbulent model is adopted to conduct the CFD simulations. The effects of horizontal translation distances, internal partitions, and inter-floor interference on time-averaged and fluctuating ventilation rates are investigated. The simulation results show that the mean ventilation rate is larger than the fluctuating ventilation rate. The mean ventilation rate increases while the fluctuating ventilation rate decreases with the increase of the horizontal translation distance. In addition, the internal partition decreases the pumping ventilation rate primarily by reducing the fluctuating ventilation rate rather than the mean ventilation rate. Furthermore, the ventilation rates of the 2nd floor are promoted by simultaneous pumping ventilation of the 1st and 3rd floors compared to the situation that only the 2nd floor is ventilated. The ventilation rates of the 1st floor are the largest, and of the 3rd floor are the lowest. The findings of this study benefit the deeper understanding of pumping ventilation and provide theoretical guidance on the opening designs of multi-story buildings for better ventilation efficiency.

H hybrid control and MRD in a steel frame building subjected to excessive vibrations caused by the dynamic action of wind and earthquake

The dynamic loads from earthquakes and winds can destroy lives, cause collapse in civil structures, and interrupt basic services provided to the population. In this scenario, structural designs must be developed to decrease the damage induced by these actions. The objective of this work is to design a hybrid controller based on the H¥ optimization via state feedback and the magneto-rheological damper (MRD) to mitigate the excessive vibrations of a three-story steel frame building, represented through the shear building model, subjected to the simultaneous dynamic action of wind and earthquake. All research is based on computational simulation, experimental research and results will not be addressed. In the numerical analysis, digital computer and MATLAB® software are used, and implemented codes generate the expected results based on the mathematical modeling. With the application of the H¥ control technique via state feedback, the displacements were reduced by 77%. With MRD this reduction was 79%. With the hybrid controller, this reduction was 100%. Thus, the verifications in relation to maximum displacements were met for NBR 15421:2006, NBR 8800:2008 and NBR 6118:2014. From the results, it is concluded that the hybrid controller proved to be more efficient and achieved the proposed objective. The exogenous inputs had zero influence on the behavior of the system output.