Prediction of damage intensity to masonry residential buildings with convolutional neural network and support vector machine.

During their life cycle, buildings are subjected to damage that reduces their performance and can pose a significant threat to structural safety. This paper presents the results of research into the creation of a model for predicting damage intensity of buildings located in mining terrains. The basis for the research was a database of technical and mining impact data for 185 masonry residential buildings. The intensity of damage to buildings was negligible and ranged from 0 to 6%. The Convolutional Neural Network (CNN) methodology was used to create the model. The Support Vector Machine (SVM) methodology, which is commonly used for analysis of this type of issue, was used for comparisons. The resulting models were evaluated by comparing parameters such as accuracy, precision, recall, and F1 score. The comparisons revealed only minor differences between the models. Despite the small range of damage intensity, the models created were able to achieve prediction results of around 80%. The SVM model had better results for training set accuracy, while the CNN model achieved higher values for F1 score and average precision for the test set. The results obtained justify the adoption of the CNN methodology as effective in the context of predicting the damage intensity of masonry residential buildings located in mining terrains.

Segmentation of 3D Point Clouds of Heritage Buildings Using Edge Detection and Supervoxel-Based Topology.

This paper presents a novel segmentation algorithm specially developed for applications in 3D point clouds with high variability and noise, particularly suitable for heritage building 3D data. The method can be categorized within the segmentation procedures based on edge detection. In addition, it uses a graph-based topological structure generated from the supervoxelization of the 3D point clouds, which is used to make the closure of the edge points and to define the different segments. The algorithm provides a valuable tool for generating results that can be used in subsequent classification tasks and broader computer applications dealing with 3D point clouds. One of the characteristics of this segmentation method is that it is unsupervised, which makes it particularly advantageous for heritage applications where labelled data is scarce. It is also easily adaptable to different edge point detection and supervoxelization algorithms. Finally, the results show that the 3D data can be segmented into different architectural elements, which is important for further classification or recognition. Extensive testing on real data from historic buildings demonstrated the effectiveness of the method. The results show superior performance compared to three other segmentation methods, both globally and in the segmentation of planar and curved zones of historic buildings.

Smart Buildings: A Comprehensive Systematic Literature Review on Data-Driven Building Management Systems.

Buildings are complex structures composed of heterogeneous elements; these require building management systems (BMSs) to dynamically adapt them to occupants' needs and leverage building resources. The fast growth of information and communication technologies (ICTs) has transformed the BMS field into a multidisciplinary one. Consequently, this has caused several research papers on data-driven solutions to require examination and classification. This paper provides a broad overview of BMS by conducting a systematic literature review (SLR) summarizing current trends in this field. Unlike similar reviews, this SLR provides a rigorous methodology to review current research from a computer science perspective. Therefore, our goal is four-fold: (i) Identify the main topics in the field of building; (ii) Identify the recent data-driven methods; (iii) Understand the BMS's underlying computing architecture (iv) Understand the features of BMS that contribute to the smartization of buildings. The result synthesizes our findings and provides research directions for further research.

Multi-Scale Study of a Phase Change Material on a Tropical Island for Evaluating Its Impact on Human Comfort in the Building Sector.

Our study explores the utilization of a phase change material (PCM) to optimize energy efficiency and thermal comfort in buildings in tropical climates. Employing a comprehensive multi-scale approach, this research encompasses both microscopic and macroscopic analyses to rigorously evaluate the PCM's performance under various environmental conditions. It evaluates the effect of PCMs on ambient conditions in the face of temperature variations and high humidity, utilizing experimental methods at different scales (microscopic and macroscopic). Microscopic analyses reveal the composite structure of the PCM, consisting of microencapsulated paraffin within a cellulose fiber matrix. At a macroscopic scale, experiments using two real-scale test cells evaluated thermal performance and its influence on thermal comfort. Temperature and humidity data were meticulously collected over an extended period to assess the PCM's impact on indoor regulation. We employed type T thermocouples and flux meters to monitor thermal dynamics and energy flux across the building walls. This setup facilitated a detailed comparison of temperature variations and thermal comfort metrics between the PCM-equipped test cell and a control cell. The results indicate a seasonal duality of the PCM: beneficial in winter for thermal regulation but problematic in summer due to excessive heat retention. The conclusions highlight the importance of carefully selecting and adapting PCMs for tropical climates, thus providing valuable insights for designing sustainable buildings in regions facing similar climatic challenges.

Application of a multi-criteria decision support system for assessing development potential in flood risk areas - Case study of the Warta River.

The paper discusses the concept of a proprietary decision-making model that allows for determining whether the planned development in areas not protected by flood embankments and at risk of flooding can be shaped in a safe manner. The model was used to evaluate the possibility of shaping the development in flood-risk areas on the example of one of the types of Oleder villages - a dispersed village located in the floodplains of the Warta River (western Poland). So far, there has been no comprehensive evaluation method supporting proper spatial planning for flood-risk areas. The use of multi-criteria analysis methods enabled to specify the key criteria of this evaluation, which form the basis of the decision-making support system. Additionally, the elaborated method enables to determine whether the localities under study can retain their current functional nature and if there is a potential for further spatial development based on the characteristics of an Oleder village. The presented methodology can be easily adapted to other cultural areas located in countries with different levels of development, traditions, landscape or climate.

Powering the Future Green Buildings: Multifunctional Ultraviolet-Shielding Transparent Wood.

Indoor UV damage is a serious problem that is often ignored. Common glasses cannot filter UV rays well and have fragility and environmental issues. UV-shielding transparent wood (TW) holds promise, yet striking the right balance between blocking UV rays and allowing sufficient visible-light transmission poses a challenge. The pronounced capillary force, fueled by persistent moisture and extractives in wood, alongside the existence of multiphase interfaces, collectively hinder the uniform penetration of polymers and the effective dispersion of nanomaterials within the wood skeleton. Here, we incorporate high-pressure supercritical CO2 fluid-assisted impregnation (HSCFI) into fabricating UV-shielding TW. The supercritical CO2 pretreatment efficiently eliminates moisture and refines wood structure by extracting polar substances, resulting in a prominent 52.4% increase in average water permeability. Subsequently, this HSCFI method facilitates the infiltration of methyl methacrylate (MMA) monomer and Ce-ZnO nanorods (NRDs) into the refined anhydrous wood, leveraging the excellent solvency of supercritical CO2 for MMA. The impregnation rate of PMMA undergoes a substantial increase from 34.5 to 59.1%. With the robust UV-blocking capability of Ce-ZnO NRDs, thanks to dual-valence Ce doping widening the ZnO energy gap via the Burstein-Moss effect and their unique photoactive microstructure featuring a solid prism with a sharp hexahedral pyramidal tip, along with intrinsic physical scattering/reflection actions, Ce-ZnO NRDs@TW achieves an impressive 99.6% UVA radiation blockage (the highest for TW) and maintains high visible-light transmission (83.2%). Furthermore, Ce-ZnO NRDs@TW presents favorable energy-saving, sound absorption, and antifungal abilities, making it a promising candidate for future green buildings.

Complex artificial intelligence models for energy sustainability in educational buildings.

Energy consumption of constructed educational facilities significantly impacts economic, social and environment sustainable development. It contributes to approximately 37% of the carbon dioxide emissions associated with energy use and procedures. This paper aims to introduce a study that investigates several artificial intelligence-based models to predict the energy consumption of the most important educational buildings; schools. These models include decision trees, K-nearest neighbors, gradient boosting, and long-term memory networks. The research also investigates the relationship between the input parameters and the yearly energy usage of educational buildings. It has been discovered that the school sizes and AC capacities are the most impact variable associated with higher energy consumption. While 'Type of School' is less direct or weaker correlation with 'Annual Consumption'. The four developed models were evaluated and compared in training and testing stages. The Decision Tree model demonstrates strong performance on the training data with an average prediction error of about 3.58%. The K-Nearest Neighbors model has significantly higher errors, with RMSE on training data as high as 38,429.4, which may be indicative of overfitting. In contrast, Gradient Boosting can almost perfectly predict the variations within the training dataset. The performance metrics suggest that some models manage this variability better than others, with Gradient Boosting and LSTM standing out in terms of their ability to handle diverse data ranges, from the minimum consumption of approximately 99,274.95 to the maximum of 683,191.8. This research underscores the importance of sustainable educational buildings not only as physical learning spaces but also as dynamic environments that contribute to informal educational processes. Sustainable buildings serve as real-world examples of environmental stewardship, teaching students about energy efficiency and sustainability through their design and operation. By incorporating advanced AI-driven tools to optimize energy consumption, educational facilities can become interactive learning hubs that encourage students to engage with concepts of sustainability in their everyday surroundings.

Home indoor air quality and cognitive function over one year for people working remotely during COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic triggered an increase in remote work-from-home for office workers. Given that many homes now function as offices despite not being designed to support office work, it is critical to research the impact of indoor air quality (IAQ) in homes on the cognitive performance of people working from home. In this study, we followed 206 office workers across the U.S. over one year under remote or hybrid-remote settings during 2021-2022. Participants placed two real-time, consumer-grade indoor environmental monitors in their home workstation area and bedroom. Using a custom smartphone application geofenced to their residential address, participants responded to surveys and periodic cognitive function tests, including the Stroop color-word interference test, Arithmetic two-digit addition/subtraction test, and Compound Remote Associates Task (cRAT). Exposures assessed included carbon dioxide (CO2) and thermal conditions (indoor heat index: a combination of temperature and relative humidity) averaged over 30 minutes prior to each cognitive test. In fully adjusted longitudinal mixed models (n≤121), we found that indoor thermal conditions at home were associated with cognitive function outcomes non-linearly (p<0.05), with poorer cognitive performance on the Stroop test and poorer creative problem-solving on the cRAT when conditions were either too warm or too cool. Most indoor CO2 levels were <640 ppm, but there was still a slight association between higher CO2 and poorer cognitive performance on Stroop (p=0.09). Our findings highlight the need to enhance home indoor environmental quality for optimal cognitive function during remote work, with benefits for both employees and employers.

Designing energy-efficient buildings in urban centers through machine learning and enhanced clean water managements.

Rainwater Harvesting (RWH) is increasingly recognized as a vital sustainable practice in urban environments, aimed at enhancing water conservation and reducing energy consumption. This study introduces an innovative integration of nano-composite materials as Silver Nanoparticles (AgNPs) into RWH systems to elevate water treatment efficiency and assess the resulting environmental and energy-saving benefits. Utilizing a regression analysis approach with Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), this study will reach the study objective. In this study, the inputs are building attributes, environmental parameters, sociodemographic factors, and the algorithms SVM and KNN. At the same time, the outputs are predicted energy consumption, visual comfort outcomes, ROC-AUC values, and Kappa Indices. The integration of AgNPs into RWH systems demonstrated substantial environmental and operational benefits, achieving a 57% reduction in microbial content and 20% reductions in both chemical usage and energy consumption. These improvements highlight the potential of AgNPs to enhance water safety and reduce the environmental impact of traditional water treatments, making them a viable alternative for sustainable water management. Additionally, the use of a hybrid SVM-KNN model effectively predicted building energy usage and visual comfort, with high accuracy and precision, underscoring its utility in optimizing urban building environments for sustainability and comfort.

Analysis of urban residential greening in tropical climates using quantitative methods.

Urban green spaces play a crucial role in mitigating urban heat islands, providing shade, cooling, absorbing carbon dioxide, and releasing oxygen to enhance air quality. Understanding the user perceptions of residential greeneries is essential for effective planning and implementation of greening systems. This quantitative research explored user perceptions and preferences regarding residential greeneries through a structured questionnaire survey from 578 respondents. The responses from the densely populated Chennai city and the rest of Tamil Nadu, India, were analyzed. About 90% of residents are interested in having a garden, irrespective of location and residential characteristics. The most available space in Chennai's urban region is a balcony at 45%, followed by front and back gardens at 30% and vice versa for Chennai's suburban areas. The most preferred type is potted plants (30%) and climbers (20%) on balconies and near windows in Chennai. The most perceived challenges are installation and maintenance costs. The most influencing factors over the preference for greeneries and green walls are the house typology, house ownership, and site location. This study provides more insights to building designers and architects on planning and implementation of residential greeneries as per end users' preferences and perceptions.

Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions.

In today's world, a significant amount of global energy is used in buildings. Unfortunately, a lot of this energy is wasted, because electrical appliances are not used properly or efficiently. One way to reduce this waste is by detecting, learning, and predicting when people are present in buildings. To do this, buildings need to become "smart" and "cognitive" and use modern technologies to sense when and how people are occupying the buildings. By leveraging this information, buildings can make smart decisions based on recently developed methods. In this paper, we provide a comprehensive overview of recent advancements in Internet of Things (IoT) technologies that have been designed and used for the monitoring of indoor environmental conditions within buildings. Using these technologies is crucial to gathering data about the indoor environment and determining the number and presence of occupants. Furthermore, this paper critically examines both the strengths and limitations of each technology in predicting occupant behavior. In addition, it explores different methods for processing these data and making future occupancy predictions. Moreover, we highlight some challenges, such as determining the optimal number and location of sensors and radars, and provide a detailed explanation and insights into these challenges. Furthermore, the paper explores possible future directions, including the security of occupants' data and the promotion of energy-efficient practices such as localizing occupants and monitoring their activities within a building. With respect to other survey works on similar topics, our work aims to both cover recent sensory approaches and review methods used in the literature for estimating occupancy.

Deploying Wireless Sensor Networks in Multi-Story Buildings toward Internet of Things-Based Intelligent Environments: An Empirical Study.

With the growing integration of the Internet of Things in smart buildings, it is crucial to ensure the precise implementation and operation of wireless sensor networks (WSNs). This paper aims to study the implementation aspect of WSNs in a commercial multi-story building, specifically addressing the difficulty of dealing with the variable environmental conditions on each floor. This research addresses the disparity between simulated situations and actual deployments, offering valuable insights into the potential to significantly improve the efficiency and responsiveness of building management systems. We obtain real-time sensor data to analyze and evaluate the system's performance. Our investigation is grounded in the growing importance of incorporating WSNs into buildings to create intelligent environments. We provide an in-depth analysis for scrutinizing the disparities and commonalities between the datasets obtained from real-world deployments and simulation. The results obtained show the significance of accurate simulation models for reliable data representation, providing a roadmap for further developments in the integration of WSNs into intelligent building scenarios. This research's findings highlight the potential for optimizing living and working conditions based on the real-time monitoring of critical environmental parameters. This includes insights into temperature, humidity, and light intensity, offering opportunities for enhanced comfort and efficiency in intelligent environments.

A Gaussian-plume based Monte Carlo method for calculating radiation dose in the near field of buildings.

A Monte Carlo (MC) programme was written using the dose point kernel method to calculate doses in the roof zone of a building from nearby releases of radioactive gases. A Gaussian Plume Model (GPM) was parameterised to account for near-field building effects on plume spread and reflection from the roof. Rooftop recirculation zones and building-generated plume spread effects were accounted in a novel Dual Gaussian Plume (DGP) formulation used with the MC model, which allowed for the selection of angle of approach flow, plume release height in relation to the building and position of the release point in relation to the leading edge of the building. Three-dimensional wind tunnel concentration field data were used for the parameterisation. The MC code used the parameterised concentration field to calculate the contributions to effective dose from inhalation, cloud immersion from positron/beta particles, and gamma-ray dose for a wide range of receptor dose positions in the roof zone, including receptor positions at different heights above the roof. Broad trends in predicted radiation dose with angle of approach flow, release position in relation to the building and release height are shown. Alternative approaches for the derivation of the concentration field are discussed.

Developing a dynamic life cycle assessment framework for buildings through integrating building information modeling and building energy modeling program.

The construction and building sector is one of the largest contributors to the global carbon emissions. Therefore, it is imperative to accurately assess the carbon emissions of buildings throughout the life cycle. Many studies conducted life cycle assessment (LCA) of buildings to evaluate carbon emissions. However, due to the lack of dynamic data, most studies adopted the static LCA methodology, which neglected the dynamic variations during life cycle stages of a building. Unlike previous studies that collected static data from questionnaires and documents, the present study aims to establish a novel dynamic life cycle assessment (D-LCA) framework for buildings by incorporating the building information modeling (BIM) and the building energy modeling program (BEMP) into the static LCA. First, a static LCA is established as the baseline scenario that covers the "cradle-to-grave" life cycle stages. A BIM model is established using Revit to obtain the inventory of building materials. The Designer Simulation Toolkit (DeST) is used as a BEMP to simulate the operating energy consumption of the studied building, taking into account changes in energy mix, climate change, and occupant behavior. At the same time, the DeST results are further used as a data input for dynamic scenarios. The D-LCA framework is applied to a high-rise commercial building in China. This study found that the difference between static and dynamic scenarios was up to 66.7 %, mainly reflected in the dynamic energy consumption during the operation phase, indicating the inaccuracy of traditional static LCA. Therefore, a D-LCA by integrating BIM and BEMP can facilitate dynamic modeling and improve the accuracy and reliability of LCA for buildings.

A hierarchical model of accelerating factors to promote urban renewal and reconstruction of unsafe and old buildings.

The issue of urban renewal is complex and multifaceted. In this study, six specialists in the construction industry were invited to conduct audio interviews, which were compiled into verbatim text. The key phrases were extracted by Grounded theory, and three levels of coding were retrieved. The data were categorized into ten accelerating urban renewal factors in three constructs to establish an Analytic Hierarchy Process framework. Using institutional theory to construct outcomes based on grounded theory, transforming these into specific urban renewal relation issue elements. 113 AHP questionnaires were collected from five types of specialists, including practitioners, professionals, participants in urban renewal, academics, and government staff. The results show that relaxing the plot ratio control and incentives is ranked No. 1 by practitioners, participants in urban renewal, professionals, and academics, indicating that the factor is highly valued by specialists but neglected by government staff. Secondly, practitioners, academics, participants in urban renewal, and professionals identified incentives and rewards for urban renewal and enhancing the trust and credibility of urban renewal projects as crucial factors. However, the government staff showed a different weighting. This indicates that government staff is determined to accelerate urban renewal. Finally, the suggestion of this study is in line with the views of the specialists interviewed, who suggest that the government should hold public hearings regularly and seriously to listen to people and specialists. Only through public hearings can all parties reach a consensus. The government should consolidate the views of all parties to amend or enact a bill on urban renewal that is more in line with the changes of the times, including appropriately relaxing the control of building plot ratio and other accelerating factors, to promote urban renewal in Taiwan.

Assessing the relationship between the biochemical and the morphological factors (leaf surface area and leaf surface texture) of industrial and roadside plants.

The study of biochemical parameters provides an idea of the resistance of plants against air pollutants. Biochemical and Physiological parameters are studied with the help of Air pollution tolerance index (APTI). Fifteen plant species were evaluated to assess biochemical and APTI from two polluted sites (Phagwara Industrial area and Phagwara Bus stand area). The values of APTI were found to be highest for Mangifera indica (19.6), Ficus religiosa (19.3), and Ficus benghalensis (15.8) in the industrial area. On the roadside, Mangifera indica (16.8), Ficus benghalensis (16.5), and Ficus religiosa (16.4). Mangifera indica, Ficus religiosa, and Ficus benghalensis were found to be excellent performers in reducing pollution at both the sampling sites as per the APTI values. The order of tolerance was Mangifera indica > Ficus religiosa > Ficus benghalensis > Polyalthia longifolia > Mentha piperita in both the polluted sites. Morphological changes were observed in the plants, suggesting the possibility of pollution stress, which is probably responsible for the changes in biochemical parameters. As a result, the relationship between morphological and biochemical parameters of selected plant species growing in roadside and industrial areas was explored. The findings revealed that relative water content showed a significant positive and negative correlation with leaf surface texture and leaf surface area. On the other hand, ascorbic acid showed a significant positive correlation with them. In conclusion, it has been studied that morphological parameters including biochemical parameters can be proved to be important in investigating the ability of plants to cope with air pollution and in calculating tolerance index.

Associations between residential daytime indoor temperature and self-reported sleep disturbances in UK adults: A cross-sectional study.

BACKGROUND: In the past few decades, research on the association between indoor temperature and sleep has primarily used laboratory rather than field data collected in epidemiological cohorts. METHODS: Secondary data on 2493 individuals aged 43 years was obtained from the National Survey of Health and Development (NSHD). Logistic regression models were used to investigate the associations between temperatures (indoor at home, spot measurement when the nurses visited during the day; and outdoor, monthly average) and self-reported sleep disturbances, adjusting for socio-demographics, health variables, housing variables, and temperature-related variables. RESULTS: Associations were found between daytime indoor temperature with difficulty initiating (OR: 0.95, 95%CI: 0.91-0.98) and maintaining sleep (OR: 0.96, 95%CI: 0.93-0.99). Compared with neutral indoor temperatures (17-28 °C), low indoor temperature (≤17 °C) was associated with difficulty initiating sleep (OR: 1.79, 95%CI: 1.21-2.65). Stratified analysis results across tertiles showed that associations with difficulty initiating (OR: 0.87, 95%CI: 0.77-0.99) and maintaining sleep (OR: 0.88, 95%CI: 0.79-0.98) were observed respectively in the lowest (≤20 °C) and highest tertile (≥23 °C) of indoor temperature. There was no association between outdoor temperature and self-reported sleep disturbances in this study. CONCLUSION: In this first UK-based epidemiology study investigating temperature and sleep, self-reported sleep disturbances were associated with residential daytime indoor temperatures. Low indoor temperature had significantly higher odds ratio for difficulty initiating sleep compared with the neutral indoor temperature. A warmer indoor environment might be more suitable for sleep maintenance than sleep initiation. Indoor temperature in this study was a superior indicator of sleep disturbances than outdoor temperature. Although these findings are based on a UK sample, they may be relevant to other high-income settings with similar housing stock and climatic conditions.

Deterrents to the IoT for smart buildings and infrastructure development: A partial least square modeling approach.

Implementing Internet of things (IoT) technology in the context of intelligent buildings and infrastructure development has garnered significant attention within the construction sector. Nonetheless, the implementation of IoT could be improved by assessing various barriers. The purpose of this study was to examine the obstacles related to the adaptation of IoT techniques within the construction sector, as well as the effects on the advancement of intelligent building and infrastructure systems. The study employed a mixed-method approach involving exploratory factor analysis (EFA) and structural equation modeling (SEM) to determine six types of barriers: knowledge, technical, standardization, creativity, complexity, and economics. The study revealed that the implementation of IoT for developing smart construction and infrastructure in the construction sector was significantly influenced by all six constructs. The results of this study offer significant ramifications for the field. The study underscores the necessity for heightened consciousness and instruction regarding the advantages of implementing IoT. The study posits that the technical barriers, including interoperability, modernization of legacy infrastructure, and coordination and collaboration difficulties, require attention from the industry. The study highlights the significance of establishing industry-wide standards and protocols for implementing IoT and regulatory and legal frameworks. Finally, the study underscores the necessity for augmented funding and financing options for IoT endeavors. Subsequent study endeavors may expand upon the present findings by delving into the barriers encountered by alternative sectors and nations and assessing the efficacy of the suggested measures in this investigation.

Technical and economic assessment of hydrogen-based electricity generation from PV sources in tertiary buildings: a case study of a hospital building in Algeria.

The largest anthropogenic source of carbon dioxide emissions is the global energy system, which means transforming the global energy system is one of the most significant ways to reduce greenhouse gas emissions and mitigate climate change. Buildings play a critical role in our transition to a lower-carbon future, accounting for approximately 47% of global energy consumption and about 25% of global greenhouse gas emissions. Renewable hydrogen represents one of the most environmentally friendly options for energy generation. This study presents an energetic, economic, and environmental impact of a self-sufficient system for energy production from renewable energy sources in buildings. To achieve this objective, a hydrogen-based generation system was selected to meet all the electrical requirements of tertiary building in Algeria throughout the year. The results indicate that the hybrid renewable energy system can avoid the emission of approximately 1056 tons of carbon dioxide per year. Furthermore, the payback period is 7 years. These results clearly demonstrate that the integration of hydrogen energy in buildings is the optimal choice for environmental sustainability.

Courtyard geometry's effect on energy consumption of AlKharga city residential buildings, Egypt.

The research aim is to clarify the effect of courtyard placement, the ratio between length and width, and courtyard orientation on energy consumption in residential buildings in hot and dry climates, to seek planning and designing alternatives for new cities and new residential complexes that are consistent with the environment and climate and save the consumption of energy used in the buildings. The research method was conducted through Design Builder software for simulation purposes. The reference model with the 157.25 m2 which accommodates a central square courtyard measuring 5 m × 5 m, on a residential building model in the New Valley Governorate of Kharga City, Egypt. The courtyard simulation is aimed to determine Less energy consumption inside the different case studies, in 9 courtyard placements The studied alternatives for Courtyard Placement, are (the center of the building, sub facades, and main facades). The different ratios are (1:1, 1.25:1, 1.5:1-1.75:1, 2:1, 2.25:1, 2.5-1). The longitudinal axis of the Courtyard has been oriented to the east-west direction for all placements, and north-south direction for all placements. Also, (orientation angle is Zero), it offered the percentages of better and worst cases in each position of the courtyard. The research findings suggest that the best Placement of the Courtyard that achieved the highest rate of saving of energy consumed inside the used building model was at the southwestern façade, with a saving rate of 18.73%. Then, the Placement of the Courtyard at the northwestern and southeastern facades with a saving rate of 17.91%, with a length-to-width ratio (2.5:1) if the longitudinal axis of the Courtyard is oriented in the north-south direction, Through the study, we conclude that the placement and orientation of the courtyard and its regular formation have contributed to rationalizing energy consumption in residential buildings, the study reached some important standards that could represent a methodological framework for designing contemporary residential buildings with an energy-efficient inner courtyard.