Real Building Research Articles

Reconstruct the 3D load redistribution paths of framed structures subjected to local column failure and multi-floor fire

Considering the slabs in the global response of steel framed buildings in fires is important, as deflected floor slabs could redistribute the load to columns leading to column failure and even progressive collapses. Floor slab systems also experience tensile membrane action after deflection, which differs from the behaviour observed using 2D structural fire models. This paper focuses on unfolding the load redistribution mechanisms using 3D steel-concrete composite structure models, while multi-floor spreading fires and local failure of columns are considered. A framed structural model based on the Cardington building is designed and modelled, in which the composite slabs at different floors are simulated using the recently developed high-efficiency slab model to address the slab effect to the global load redistribution. Four hazard scenarios representing different states of the central column (i.e., fully protected, partially protected or failed) are given as the initiating local failure. The analyses with the 3D structural models show that the slabs can effectively pull up the failed column and the vertical loads are transferred to the adjacent columns. The worst scenario has been identified as the case experiencing column failure both at the upper floor and the lower floor, which leads to the middle floor without pulling action and causes significantly large deflection. This may be a scenario that local column failure triggers progressive collapse of a real building and should be prevented.

Direction-of-arrival estimation of impulse noise using MUSIC algorithm

Spatial information about the source of sounds can be obtained by measuring amplitude and phase differences among multiple microphones in a microphone array. Various methods have been proposed for estimating the direction of arrival, including beam-forming methods, but generally their spatial spectral resolution degrades with a low-frequency source in the far-field. In architectural acoustics, it is important to obtain high spatial resolution in the low-frequency band, especially in investigations to understand phenomena such as sudden impulse noise. In this paper, direction-of-arrival estimation using the MUltiple SIgnal Classification (MUSIC) method is discussed. We propose a microphone array structure designed according to the Cramer-Rao lower limit. It is shown that this technique improves the accuracy of elevation angle estimates at low frequencies while maintaining good horizontal angle estimation accuracy. The proposed method was tested in a real building in which impulse noise occurred and its effectiveness in identifying the source direction with high spatial resolution even at low frequencies was confirmed.

IMPACT OF MANAGERIAL OWNERSHIP, COMPANY SIZE AND CAPITAL STRUCTURE ON COMPANY VALUE THROUGH PROFITABILITY IN COMPANY SECTORS PROPERTY, REAL ESTATE AND BUILDING CONSTRUCTION LISTED ON INDONESIA STOCK EXCHANGE PERIOD 2019–2021

The research aims to test and analyze the influence of managerial ownership, the size of the company, and the capital structure on the value of a company with profitability as an intervening variable. In this study, the researchers used external data obtained from the Indonesian Stock Exchange, which processed data of the Company's property, real estate and building sectors listed during the period 2019-2021. Sampling using purposive sampling, where the population is 32 companies so in a three-year period of 96. The data analysis process is statistically tested using Structural Equation Modeling (SEM) with a Partial Least Square (PLS) approach and the processing medium is SmartPLS 3.2.9. The value of the company is still a factor that investors pay attention to the ratio used is Price to Book Value, which is a simple valuation method and can be used by investors to analyze whether the price of a stock is undervalued or overvalued. The sectors used in property, real estate and building construction have brilliant prospects and are supported by the government and the valuation of corporations belonging to them has a significant influence on the state. The sector of real estate, building and construction has a relatively stable PBV ratio to a relative stable share price, so the researchers are looking at other financial ratios that are related to profitability and can affect the value of the company. The results show that capital structure and profitability don’t effect the company, but managerial ownership and the size of a company have a negative effect on the company's value. Profitability cannot mediate the effect of managerial ownership, company size and capital structure on the company's value. With this research, the Company can increase the value of the Company and strive to profitability that benefits the investor or the company itself, then able to perform tasks or responsibilities for decision-making with stability and vigilance. Meanwhile, companies are able to evaluate the factors that can affect the value of the company and consider which items need to be upgraded. The research focuses on managerial ownership, company size, capital structure, company value and profitability. It's done because we're so interested in this phenomenon. Company value items are like mirrors that can reflect the company's market assessment and help investors determine the viability of the company to invest in.

Protection Relay Setting based on Overcurrent Phenomena in Commercial Building

Nowadays, every single distribution system needs to install an appropriate relay to keep the system safe. The operational and commonly recommended relay for distribution systems is the overcurrent (OC) relay. Throughout the distribution system, the protective relay is one of the methods that can detect and protect the location according to its observation from any fault from abnormal activity. Note that time coordination between the protective equipment relay needs to be a minimum of time interruption to prevent faults occurs. The ideal setting for all coordination protection relays is necessary to protect the device against electrical failure and interference. This paper analyzes the real results data collected for the selected commercial building of an OC relay implemented in a distribution board for high voltage and low voltage downward at a commercial building. All the parameters need to be clarified first before testing has been made and measurement is carried out using the MICROTEST 860 set. Based on the analysis, it proves that according to the IEC Standard of 0.10-time multiplier Setting (TMS) is practical to be used to obtain the operation time in seconds for the current curve set. Other than that, the results show that the normal inverse curve from manual calculation results is more accurate compared to the service setting (SS) made based on the incoming setting in a real commercial building. The case study for OC relay setting is related between current injection and time-tripping, which complies with the IEC 60255-3 standard using its formula. This method was applied to determine the characteristics of the curve. Hence, this research successfully determined the proper methods for the OC relay setting for the power distribution system. Besides, the feasibility and efficiency of OC relay data transmission are tested and checked successfully to implement the measurement method in the relay coordination study.

Integrated post-occupancy evaluation and intervention that achieve real-world zero-carbon buildings

Many building standards are available worldwide to support sustainable building design. However, most of them only define compliance before occupancy, overlooking the real building usage and its implications for meeting zero-carbon targets. This research proposes a systematic post-occupancy evaluation and intervention (POEI) protocol to analyse real building performance and support cost-effective and zero-carbon upgrading. The novelty lies in integrating novel diagnostic data science techniques to identify performance gaps, advanced physics-driven energy modelling iteratively calibrated with POE data to disaggregate energy use by end-use, and cost-optimal intervention analysis. The POEI protocol has been validated in a university building. The results demonstrated the POEI benefits for the cost-optimal upgrading of in-use building assets based on real needs. By optimising control, reducing demand, increasing efficiency, and implementing renewables, the building can reach the nearly zero-carbon building target with a payback period of 13 years and a 23 % lower life-cycle cost compared to the baseline scenario. Building energy management interventions were identified as the most important actions to reduce the performance gap, reducing energy consumption by 20 % and carbon emissions by 24 %. The results also highlight the critical role of Information and Communication Technologies (ICT), which may represent up to 49 % of energy use in the future zero-carbon building scenario if overlooked.

Net Zero Energy cost Building system design based on Artificial Intelligence

This paper presents a solution to the energy planning problem in buildings by implementing the net Zero Energy cost Building (nZEcB), a new concept that refers to buildings with zero or almost zero annual energy costs, using Artificial Intelligence (AI) techniques such as bidirectional long short-term memory, ordinary least squares linear regression, K-means, Pearson’s correlation, decision tree, and binary gravitational search algorithm. AI techniques are used to design the optimal structure of a distributed generation system. The system includes wind and photovoltaic renewable energy sources, a battery bank, and an automated capacitor bank for power factor compensation. A case study was conducted in a real public building with an annual consumption of 1.748 GWh, which resulted in the specification of a distributed generation system with a generation of 2.805 GWh per year. This system meets approximately 160.5% of the building’s electrical demand and 99.999% of annual energy costs, i.e., attending the nZEcB concept. The excess production of approximately 60% of the energy is necessary because the exported energy (feed-in tariff) has a lower value than that imported from the network, having to deduct costs with demand, in addition to having to compensate for losses in the battery banks. The project has a payback period of 6.79 years. This novel study demonstrates the feasibility and effectiveness of using AI techniques to achieve nZEcBs more efficiently, economically and sustainably.

Simultaneous in-situ calibration for physical and virtual sensors towards digital twin-enabled building operations

Digital twin-enabled building operations heavily rely on a building sensor network to collect operational data. Virtual sensing, in combination with physical sensors, has great potential in establishing a reliable and informative sensing environment. However, physical sensors can experience hardware errors or systematic errors due to the working environment within building systems. Virtual sensors are also subject to their model uncertainties arising from input physical sensor errors and changes in system operations. These virtual sensor errors have a negative effect on the in-situ calibration accuracy of corresponding physical sensors. To address these issues, this study proposes a simultaneous in-situ calibration (SIC) method designed to calibrate multiple physical and virtual sensors with varying errors in the context of digital twins. The SIC problem is mathematically formulated, including two types of distance functions (direct and indirect) and defining correction functions to calibrate various types of virtual sensors (such as backup, validation, replacement/virtualization, and observation sensors). Importantly, the proposed method tackles the challenges posed by the unknown calibration environment in operational systems. A field application study was conducted for two physical sensors and three different virtual sensors operating in a district heating substation serving real residential buildings in Korea. The case study shows how the different calibration accuracies for each sensor depend on the calibration approaches, such as physical-targeted (existing Case 1), virtual-targeted (existing Case 2), and simultaneous calibration (novel Case 3). Under the simultaneous errors, only Case 3 satisfied the reference accuracy for all sensors. Specifically, the average MAEs in Case 3 were improved by approximately 79.3%, 75.3%, 63.9%, and 87.5% for the target physical sensor, backup virtual sensor, virtualized sensor, and validation virtual sensor, respectively, compared to the accuracies before calibration. These results demonstrate the importance and effectiveness of simultaneous in-situ calibration of physical and virtual sensors in digital twin-enabled building operations. The proposed calibration can contribute to the synchronization of physical and virtual building systems, thus achieving reliable and informative virtual sensing-driven digital twins.

Scheduling of the HVAC system in a real commercial building considering equipment cycling and rebound effects

Commercial buildings consume a large portion of energy and the heating, ventilation, and air conditioning (HVAC) system is the major contributor to this high consumption. Though there are many reported works on the optimal scheduling of HVAC systems, practical considerations such as equipment cycling and possible rebound effects are rarely considered. This paper provides insights into the optimal scheduling of the HVAC system in an actual 3-floor commercial building where the cycling and rebound effects are addressed in the optimization. The experimental results prove that energy cost reduction can be achieved while ensuring temperature comfort and longer life expectancy of the HVAC. The results also show that considering the cycling and rebounds will affect the potential cost reduction.

An adaptive crack inspection method for building surface based on BIM, UAV and edge computing

Unmanned aerial vehicle (UAV) and building information modeling (BIM) have been applied to inspect building surfaces to improve efficiency and reduce labor costs. However, most of the current research focus on the inspection mode of “first collect and then identify” with limited intelligence. Therefore, this study proposes a “global-local” adaptive inspection method for building surfaces by integrating BIM, UAV (with multifunction camera), and edge computing, enabling real-time synchronization of crack data collection and analysis. First, the building surface area to be inspected is extracted from BIM to generate global inspection points using the selected camera's field of view (FOV) and considering coordinates transformation between BIM and reality. Then, A star algorithm integrating with genetic algorithm (GA) is used to optimize the global flight path based on generated inspection points. Finally, the coordinates of the inspection points based on BIM are converted into real-world coordinates, and the flight parameters such as the flight yaw angle (FYA) and the gimbal yaw angle (GYA) are calculated to generate the inspection mission. During global inspection, a local adaptive inspection is proposed for each FOV through real-time local crack identification and flight replanning using edge computing and zoom camera. In addition, multiple battery supply points are automatically added considering the endurance of the UAV. A real building has been used to validate the proposed “global-local” adaptive inspection method. The experiment results show that the inspection time has been significantly reduced compared with the traditional method, and the crack identification of building surfaces can be implemented with high efficiency, precision, quality, and low cost.

Glare analysis of an integral daylighting and lighting control strategy for offices

Complex Fenestration Systems (CFSs) can significantly impact both the visual and non-visual daylight effects on the occupants as well as the energy performance of buildings. To ensure that those impacts improve the overall situation, proper control algorithms are required if the CFS can be operated. The state of the CFS directly and immediately effects the indoor lighting situation. The thermal condition of the building is usually strongly but not directly influenced by the condition of the façade system due to thermal capacities.In this study an experimental integral control strategy, called Integrated Lighting Module (ILM) is investigated in terms of it’s glare prevention capabilities and compared to a cut-off- and a simple rule based control logic. The ILM is an autonomous software package capable to run on building management hardware to control façade systems and artificial lighting in real buildings in an energetically optimised way. The adaptive sampling algorithm raytraverse is employed to calculate Daylight Glare Probability (DGP) values, which are used as a benchmark for the control strategy comparison. The other aspect of this comparison is the opening of the façade, which is evaluated as statistics how often each possible façade state is selected by each control strategy.

Occupant-centric metadata paradigms for comfort optimization in buildings

Three decades of building performance standards have resulted in a meaningful reduction in energy consumption in commercial buildings, but limited improvement in thermal comfort has been observed. ASHRAE Standard 55, the primary reference used to design and operate “comfortable” buildings, specifies six thermodynamic factors that must be addressed to provide comfort in a space. In practice, survey data reports only a small percentage of buildings are considered satisfactory by occupants. We perform a literature review to demonstrate that the range of each of the six thermodynamic dimensions of comfort can be observed well outside of the applicability of ASHRAE 55 in real buildings. The typical practice of modulating air temperature (Ta ) is shown to result in frequent occupant dissatisfaction. We posit that to be able to more consistently operate within a given “comfort zone” in a wider range of thermodynamic environments, we must embed within building systems higher-dimensional, occupant-centric parameters. To understand if two popular metadata schemas, Project Haystack and Brick Schema, are equipped to support higher-dimensional control paradigms, we (1) define a model, measure, manipulate (M3) evaluation criteria, which we (2) apply to each of the above schemas to determine if they can be used to codify closed-loop control for a given set of thermodynamic dimensions of comfort. We conclude that recent extensions to the Brick Schema provide much of the background necessary to codify closed control loops in six dimensions, and that Project Haystack requires a greater expansion.

HBIM-based workflow for the integration of advanced photovoltaic systems in historical buildings

The European Union is pushing towards the adoption of energy production systems from Renewable Energy Sources, in both new and existing buildings. However, integrating photovoltaic (PV) applications into historical heritage context remain challenging due to a number of different barriers, primarily related to social acceptability. This research aims to define a collaborative and interoperable workflow that utilizes Heritage Building Information Modelling (HBIM) tools to facilitate the integration of advanced and customized PV technologies and components in historical buildings refurbishment operations. The workflow combines architectural refurbishment practices with parametric rendering and a best-choice protocol to establish an efficient stakeholder value-chain for a collaborative approach. The methodology includes the following four steps: (i) historical research and on-site survey; (ii) HBIM model construction; (iii) selection of PV interventions and technologies; (iv) modelling of the selected PV components and integration in the HBIM model. The study also demonstrates the output of applying this workflow to a real historical building through a case study. Heritage-compatible building integrated PV (BIPV) interventions are evaluated following a multi-level risk-benefits scheme that considers factors such as aesthetic impact, technological compatibility, theoretical energy performances, economic costs, and environmental issues. Subsequently, selected interventions and advanced customized BIPV elements are modelled in BIM environment. The study highlights the positive implications of this HBIM-based workflow in various aspects, including decision-making (e.g., promoting collaboration and interoperability), modelling (e.g., pre-emptive evaluation and reduction of impacts, PV component customization, and optimization), and data and process management (e.g., creating an all-in-one model for future assessments and operations).

Towards deep learning methods to improve photovoltaic prediction and building decarbonization in benchmarking study

High energy demand, energy transition, energy consumption control are challenges for the future, especially for Building Integrated Photovoltaic (BIPV). There is a great potential to harvest large amounts of photovoltaic (PV) energy on horizontal and vertical surfaces. However, this high potential is often hindered by the slow deployment of these panels, the complex integration into existing buildings, and the possible complex interactions between different factors, such as visualization and active projection of buildings in the decarbonization process. Building Information Modeling (BIM) offers complete and real generative building data that is used in our deep learning methods. Indeed, there is currently no framework for design linking photogrammetry, BIM and PV for BIPV. In this work, we propose artificial learning models, such as Deep Learning, to predict PV energy production for BIPV decarbonization. We determined the optimal prediction of PV production by testing and evaluating different models on a building case study. We compared the PV power generation prediction results with 3D simulation software for solar architecture.

Automated data labeling of building automation systems using time series data and conditional probabilities

As energy efficiency demands increase, buildings get smarter, and the amount of data to analyze grows, where each building device may generate multiple data streams. These extensive quantities of monitoring data serve as a great opportunity for detecting anomalies in building automation systems and for optimizing their control. However, each building usually uses a custom format for data labels, therefore requiring an individual data label analysis per building. This makes the conceptually manageable task of detecting energy systems from the raw data increasingly complex and error-prone, which is a further hurdle that any building operation optimizer must resolve. This paper presents a methodology for automatically categorizing and labeling raw monitoring data from building automation systems. Using statistical features of the data, the method checks which data streams follow which known building operation rules and patterns. Therefore, an initial labeling of the data streams takes place. Furthermore, examining the correlation between the data streams indicates possible related system components using the concept of conditional probability. As a use case for the methodology, unlabeled data from a real building automation system are examined. The results show that, using unlabeled time series, data types from certain sensors and actuators can be reliably identified. The proposed methodology could therefore simplify the implementation of energy applications such as operation optimization and fault detection of building automation systems

Development and performance assessment of a new opensource Bayesian inference R platform for building energy model calibration

Many factors contribute to the inherent uncertainty of energy consumption modeling in buildings. It is essential to perform a calibration and sensitivity analysis in order to manage these uncertainties. Despite the availability of several calibration methods, they are often deterministic and lack quantified uncertainties. Moreover, the selection of parameters in building energy modeling for calibration depends on the user’s experience. Therefore, a more rigorous selection process is required. This study developed a new automated Bayesian Inference calibration platform running as an R package. A sensitivity analysis module and a Bayesian inference module determine the calibration parameters and uncertainties, respectively. The Meta-model module is developed to replace the building energy model for the Markov Chain Monte Carlo process to save computing time. The proposed platform is successfully demonstrated on a synthetic high-rise office building and a real high-rise residential building in a hot and arid climate. The relationship between the number of calibration parameters, calibration performance, and the accuracy of the Meta-model is further discussed. The developed calibration platform in this study proved to have clear advantages over the existing platforms, with the ability to reasonably estimate building energy performance in a short computing time.

Synconn_build: A python based synthetic dataset generator for testing and validating control-oriented neural networks for building dynamics prediction

Applying model-based predictive control in buildings requires a control-oriented model capable of learning how various control actions influence building dynamics, such as indoor air temperature and energy use. However, there is currently a shortage of empirical or synthetic datasets with the appropriate features, variability, quality and volume to properly benchmark these control-oriented models. Addressing this need, a flexible, open-source, Python-based tool, synconn_build, capable of generating synthetic building operation data using EnergyPlus as the main building energy simulation engine is introduced. The uniqueness of synconn_build lies in its capability to automate multiple aspects of the simulation process, guided by user inputs drawn from a text-based configuration file. It generates various kinds of unique random signals for control inputs, performs co-simulation to create unique occupancy schedules, and acquires weather data. Additionally, it simplifies the typically tedious and complex task of configuring EnergyPlus files with all user inputs. Unlike other synthetic datasets for building operations, synconn_build offers a user-friendly generator that selectively creates data based on user inputs, preventing overwhelming data overproduction. Instead of emulating the operational schedules of real buildings, synconn_build generates test signals with more frequent variation to cover a broader range of operating conditions.•Synconn_build is an open-source tool designed to address the lack of datasets for benchmarking control-oriented building dynamics prediction models.•The tool automates simulations, data acquisition, and EnergyPlus configuration, guided by user inputs.•Synconn_build prevents data overproduction by selectively creating data, offering a user-friendly approach to dataset generation.

Toward Structural Health Monitoring with the MyShake Smartphone Network.

The field of structural health monitoring (SHM) faces a fundamental challenge related to accessibility. While analytical and empirical models and laboratory tests can provide engineers with an estimate of a structure's expected behavior under various loads, measurements of actual buildings require the installation and maintenance of sensors to collect observations. This is costly in terms of power and resources. MyShake, the free seismology smartphone app, aims to advance SHM by leveraging the presence of accelerometers in all smartphones and the wide usage of smartphones globally. MyShake records acceleration waveforms during earthquakes. Because phones are most typically located in buildings, a waveform recorded by MyShake contains response information from the structure in which the phone is located. This represents a free, potentially ubiquitous method of conducting critical structural measurements. In this work, we present preliminary findings that demonstrate the efficacy of smartphones for extracting the fundamental frequency of buildings, benchmarked against traditional accelerometers in a shake table test. Additionally, we present seven proof-of-concept examples of data collected by anonymous and privately owned smartphones running the MyShake app in real buildings, and assess the fundamental frequencies we measure. In all cases, the measured fundamental frequency is found to be reasonable and within an expected range in comparison with several commonly used empirical equations. For one irregularly shaped building, three separate measurements made over the course of four months fall within 7% of each other, validating the accuracy of MyShake measurements and illustrating how repeat observations can improve the robustness of the structural health catalog we aim to build.

Comparing the effects of immersive and non-immersive real estate experience on behavioral intentions

Nowadays, the web presents real estate sites providing the possibility to use low-cost virtual reality (VR) solutions without sacrificing the high quality of graphic visualization of their contents to attract customers through enhanced digital experiences and increase the probability of selling their products. However, whether immersive VR technologies improve the chances of selling real estate is unclear. This study examines the effects induced by a real estate website that offers the opportunity to evaluate the potential purchase of a property in two ways: by viewing photos of the house (non-immersive conditions) and by taking a virtual tour of the same house using a VR head-mounted display (immersive conditions). Forty participants interested in evaluating the purchase of a new house, randomly assigned to one of the experimental conditions, participated in the experiment. During the real estate experience, electroencephalography (EEG) and skin conductance (SC) were recorded to collect biological data about the emotional effects raised by each condition. After the study, psychological self-reported questionnaires were collected measuring: emotions, presence, user experience (UX), and a three-item ad hoc scale exploring behavioral intentions (e.g., the perceived pleasantness of the house, the willingness to visit the house, and the purchase intention). Results show that immersive navigation is associated with more positive emotions (EEG), more intense physiological arousal (SC), higher scores on the presence questionnaire, and higher scores on the UX questionnaire compared to the non-immersive one. The psychological scales measuring emotions did not yield significant results. No significant difference in the three-item ad-hoc self-questionnaire was found, except for the item examining willingness to visit the real house building. Implications are also discussed.

Modified shape coefficient of building with Thermal considerations: concept, experiment and Application in solar-enriched areas

ABSTRACT The shape coefficient of building (SCB) characterizes the correlation between building shape and building energy consumption. Strictly restricting the SCB was harmful to reducing energy consumption, neglecting the potential benefits of solar radiation. A novel concept and determining method of modified shape coefficient of building (MSCB) were proposed based on the energy dissipating and solar heat gain. Field measurements of the indoor air temperature of model buildings for solar radiation, building orientations, and U-values under natural and heating conditions were conducted in western Sichuan. Results demonstrated that with identical SCB, the indoor thermal environment was substantially affected by the above parameters, proving the one-sidedness of SCB simultaneously. A simulation method verified via the experiments was employed to forecast the dynamic heating load of a real building. The influence of building orientations on the MSCB under actual climatic conditions from different time scales was discussed. Results illustrated that the SCB could be reduced by 34.6% in the present case. The monthly correction factor trend and the heat consumption were consistent. The restriction on SCB could be liberalized moderately even if the heating demand was tremendous. This research could provide a reference for the energy-saving design of passive buildings.

Modelling the perception of visual design principles on façades through fuzzy sets: towards building an automated architectural data generation and labelling tool

Recent studies showed that deep learning techniques and image processing can identify the distinguishing design principles in architectural façades. However, predicting the strength of a principle is still a challenging task, as it requires a huge amount of annotated design variations. The difficulties in both searching such big numbers of data – and its labelling by experts – slow down the research. This paper proposes a computation approach for obtaining this type of data faster. With the help of parametric modelling and evolutionary algorithms, we could manipulate the design elements, and thereby generate different solutions. An integrated fuzzy logic decision mechanism could enable to carry human knowledge in the judging and labelling of alternatives automatically. The final synthetic data developed from real building images could be used for machine learning applications to enhance our understanding of artistic expression.