Building Cost Research Articles

Optimizing the cost of a school building using the Rao algorithm

Optimizing the cost of a school building using the Rao algorithm

A Novel Predictive Analysis and Classification of Land Subsidence Vulnerability Mapping based on GIS using Hybrid Optimized Machine Learning Techniques and Computer Vision

Land subsidence is a natural disaster caused by the extraction of the earth's material strength on the surface that causes the earth's surface to settle and sink. Land subsidence can also result in significant financial losses since it causes structural damage and expensive maintenance costs for roads, trains, barriers, pipelines, ground water, oil, natural gas, mines, and buildings. The Geographic Information System (GIS) and Google Earth Platform (GEP) can be used to map this land subsidence. This provides a comprehensive view of land pattern subsidence and its interactions with other geographical elements. It applies to both progressive subsidence caused by fluid extraction (groundwater, oil, or natural gas) and rapid surface subsidence caused by the collapse of an underground mine. Risk mitigation is the most effective way to deal with the experimental model. This research study focuses on the use of Predictive Data Analysis (PDA), a powerful environment in data analytics that provides a set of tools that make it easier to evaluate optimal models. The proposed model makes use of a dataset that includes specific land subsidence circumstances as input. The prediction result is achieved using a hybrid machine learning technique that combines Stochastic Gradient Descent (SGD) with Long Short-Term Memory (LSTM) and is compared to K-Nearest Neighbor (KNN), Generative Adversarial Network (GAN), and Artificial Neural Network (ANN). The proposed system employs LSTM and SGD as optimizers, resulting in a hybrid model that improves accuracy. Among the four algorithms investigated the LSTM-SGD network has the highest accuracy (97%), followed by KNN (96%), GAN (90%), and ANN (89%). Due to their ability to capture complicated nonlinear correlations in data and manage temporal dependencies, optimized hybrid ML models LSTM-SGD and KNN models appear to be particularly well-suited for land subsidence prediction.

Methods of optimization energy consumption in residential and public buildings with the availability of alternative sources of energy supply

The article is devoted to the analysis of the potential for increasing the efficiency of electricity consumption in residential and administrative buildings in the presence of various energy sources and storage systems. This task is relevant in view of the spread of renewable energy sources both in centralized power supply systems and at local facilities and the significant impact of unstable generation on the cost of electricity in the market. In particular, the main consumers of electricity, which can be used as "consumers-regulators", as well as storage systems available for use in buildings, are considered. Taking into account that a significant amount of electricity is used or can be used in building air-conditioning systems, as well as for the needs of hot water supply, the construction of mathematical models of such systems for existing buildings will allow the creation of effective electrical load management systems to optimize energy costs for traditional buildings and ensure autonomy for buildings with close to zero energy consumption. The article describes the priority tasks to be solved to build an energy management system to minimize energy costs and increase the efficiency of the country's energy system and local energy systems of communities. The formulation of the tasks follows from the analysis of information on the actual consumption schedules of residential and public buildings and the analysis of their mutual correlation, which allows using these findings to develop algorithms for monitoring energy consumption and load management on the consumer side. Further stages of the study include additional measurements of actual energy consumption at the facilities with a discreteness of several seconds to an hour, while simultaneously recording potential influencing factors, which will allow creating a digital model of building energy consumption. A preliminary analysis of the research shows that the most promising mathematical apparatus for building such a model is the use of neural networks.

A decision support system for the selection of FDM process parameters using MOORA

Additive Manufacturing (AM) is an automated process of fabricating three-dimensional (3D) physical objects from a 3D-CAD data by adding layers of materials one upon another through a print head or nozzle without using any tooling components or machining environments. Due to freedom in design, any complex shape can be produced using this process. Fused Deposition Modeling (FDM) is one such AM technology that is commonly used for its simplicity, environment friendliness and low requirement for process monitoring. However, this technology is limited only to small-scale production due to high cost and high build time. The present work focuses on the development of a framework for parametric optimization of the FDM process using multi-objective optimization based on ratio analysis (MOORA). A CAD model of the cam follower mechanism has been prepared in the Solidworks platform and used in this experiment for optimization of build time and cost which have been considered as response variables of the experiment. The experiment has been conducted following the full factorial design of experiment (DoE) method.

Leveraging digital tools for enhanced green building cost analysis: A sustainable approach to construction economics

Leveraging digital tools for enhanced green building cost analysis: A sustainable approach to construction economics

A multi-objective site selection of electric vehicle charging station based on NSGA-II

The planning of charging infrastructure is crucial to developing electric vehicles. Planning for charging stations requires considering several variables, including building costs, charging demand, and coverage levels. It might be advantageous to use a multi-objective optimization method based on the NSGA-II. We need to address the current problems in choosing the location of electric vehicle charging stations. Firstly, urban land use is divided into five functional areas, and the TF-IDF algorithm is applied to the division of functional areas. A combined clustering algorithm is proposed to cluster POIs in functional areas into several clusters and determine the cluster centers as charging demand points. We Analyze charging practices and travel patterns of electric car users, fit the charging likelihood of various functional regions, and calculate the charging demand of each charging demand point in the study area. Introduce the NSGA-II algorithm and consider the charging station's progressive coverage to fit the actual area covered by the charging station.Taking the maximization of system benefits and the maximization of the minimum coverage level as the optimization objectives to carry out multi-objective optimization. Finally, we take the charging station planning in the urban area of Hohhot as an example and provide different site selection planning schemes. The planning schemes for different numbers of charging stations are analyzed to obtain a charging station planning scheme that takes into account both objectives.

Improving early strength and durability of eco-friendly mortars: Investigating the influence of limestone filler fineness and blast furnace slag combination

This research investigates the application of Life Cycle Cost (LCC) analysis in the construction industry, focusing on reinforced concrete structures. LCC analysis goes beyond initial building costs, encompassing all expenses throughout a structure's service life. Among various service life prediction models, Life-365 and DURACON are noteworthy. Life-365, a specialized computer program, predicts the life cycle cost of reinforced concrete exposed to chlorides. This paper presents a case study using Life-365 to compare the LCC of two concrete mixes, TM1 and TM2, in Mumbai, a location with conditions conducive to chloride exposure. The study uses average monthly temperatures and location-specific input parameters to evaluate the LCC of M70-grade concrete mixes with different compositions, including Fly Ash, GGBS, and Micro Silica as partial cement replacements. Results indicate that quaternary blended M70 grade concrete, incorporating supplementary cementitious materials (SCMs), not only enhance durability but also offers economic benefits, reducing overall life cycle costs. These findings provide valuable insights for engineers and decision-makers, promoting durable, cost-effective, and sustainable concrete structures.

Exploring the Role of Quantity Surveyors in Mechanical and Electrical Services Cost Estimation – Challenges, and Client Implication

Mechanical and Electrical (M&E) services costs constitute a substantial proportion of the total cost of a building. Realistic cost estimates for this element are a desirable option by industry stakeholders because the total building cost can potentially be reduced if the cost of M&E services can effectively be managed throughout the life of the project. In the building construction industry, quantity surveyors are engaged to prepare cost estimates on all aspects of the building and provide cost advice services at every stage of the building construction projects. However, M&E services are one aspect of a building that quantity surveyors find very difficult to prepare cost estimates for. This study explored the role of quantity surveyors and, the challenges involved in the delivery of M&E services cost estimation.

Analysis of Product Costs Using the Full Costing Method and Determining Selling Prices at CV Artha Jaya

The aim of this research is to assess the process of determining product cost prices and selling prices at CV Artha Jaya. The results of this research indicate that there are deficiencies in the company's calculation of the product cost, with it being known that the company's product cost price for Prentol roof tiles is IDR. 2,200, mantili roof tiles Rp. 2,100, trihek paving Rp. 1,300, and hexagon paving Rp. 1,200. However, after evaluation, the basic product price for Prentol roof tiles became Rp. 2,299, mantili roof tiles Rp. 2,125, trihek paving Rp. 1,343, and hexagon paving Rp. 1,581. This inaccuracy occurred because the company did not include a number of production costs in calculating the cost price, including depreciation costs for factory buildings, as well as depreciation costs for machinery and equipment

DEVELOPMENT OF METHOD TO OPTIMIZE RAIN GAUGE STATION LOCATION IN DA RANG RIVER BASIN USING SPATIAL INTERPOLATION TECHNIQUE

Rainfall is one of the crucial data types in meteorological research. In the context of increasingly complex climate change, meteorological forecast data is essential for modeling and predicting natural disasters, especially floods and landslides. To ensure the accuracy of rainfall prediction and the cost of building and operating rain gauge stations, determining the number of stations is crucial. Therefore, this study developed an optimization design for the rain gauge station network in the Da Rang River focusing on the quantity and spatial distribution of rain gauge stations. The geostatistical method combined with spatial interpolation algorithms was applied using rainfall data from 2015-2020. The choice of an appropriate spatial interpolation method influenced the accuracy of the optimization process. The spatial interpolation analysis showed the spatial variation of rainfall types across the entire study area by season, as well as the geographical distribution of rainfall. After determining the optimal number of stations, an approach based on geographic information systems produced a spatial distribution map of the optimal rain gauge station locations. The results indicated that an additional 14 rain gauge stations should be installed, randomly located within the rainy area with a distance of 10 km between stations. These additional stations, along with the existing ones in the area, will provide comprehensive information about rainfall, enabling accurate predictions of rainfall distribution in the research area.

Estimating Soil Strength Using GIS-Based Maps - A case study in Sweden

Soil strength is an important parameter for planning of forest roads and harvesting operations. Locating roads to areas with high soil strength reduce both build and maintenance costs. Locating logging trails to high strength areas minimise soil disturbances, e.g., rutting and compaction of forest soils. GIS-based maps of soil type and soil moisture can be valuable tools to estimate soil strength. The aim of this study was to evaluate the use of soil moisture map, i.e., depth-to-water (DTW), maps and soil type maps, to estimate soil strength expressed as California bearing ratio (CBR). CBR, volumetric water content, and ground penetration depth were measured in 120 sample points, separated on three soil classes (clay-silt sediments, sand sediments, glacial till) and two soil moisture classes (wet, dry). In each point, soil samples were collected for validation of the soil type maps. There was a high conformance between soil moisture predicted by DTW maps and field measurements, but conformance of the soil type between maps and field estimates varied between soil types. For sediment soils, dry soils were consistently stronger than wet soils. Soil strength of glacial till soils was more complicated with a binary CBR distribution depending on soil stoniness. Glacial till soils possible to penetrate to 20 cm depth with the dynamic cone penetrometer had CBR values close to those for sand sediments. There is a potential to estimate soil strength from DTW and soil type maps, but these variables should preferably be complemented with other data.

Большой город как предпосылка развития альтернативного школьного образования

A large city 'gives birth' to groups of carriers of certain ideas, values, and attitudes, who seek each other out. Parents choose schools that appear closer to their own values, where the methods of transmitting these values and the forms of classes seem more preferable and coherent. Schools 'attract' those parents whose basic life attitudes align and correlate with the school's mission, values, and strategy. The study aimed to identify factors influencing the development of alternative education in large cities. To achieve this, information was collected and analyzed on 667 private alternative educational projects in the five largest cities of Russia (Moscow, Saint Petersburg, Novosibirsk, Yekaterinburg, Kazan). In each city, five leaders of private alternative educational projects were identified and interviewed in-depth. Interviews and focus groups were also conducted with experts in alternative education. The study revealed that the concentration of maximum professional, infrastructural, economic, cultural, religious, political-ideological, and social diversity in large cities is a precondition for the development of alternative school education. Stimulating factors for the development of alternative education in large cities include overcrowded schools and the quality of education not meeting parents' expectations. The factor of parents plays a significant role: their age, involvement in their children's education, high level of information, etc. The lifestyle of city dwellers is a formative factor. Here, two strategies can be identified: the 'all inclusive' strategy and the strategy of education connoisseurs. At the same time, in a large city, there are also limiting factors for the development of alternative education: a complex transport system and logistical component, expensive rent and/or cost of buildings.

Expert studies on the impact of risk on the life cycle costs of buildings

Expert studies on the impact of risk on the life cycle costs of buildings

A Study on Sustainable Concrete by using Recycled Concrete Aggregate and Waste Glass

Sustainable and affordable building materials are a developing concern in the construction sector. This abstract outline a creative method for creating a cost-effective concrete mix that uses discarded glass as the fine aggregate and recycled coarse aggregate (RCA) as the main coarse material. The goal is to investigate whether using recycled materials instead of traditional aggregates is feasible while both addressing environmental issues and lowering construction costs. The proposed concrete mix's mechanical qualities, financial ramifications, and environmental impact were all compared through laboratory experiments, a thorough assessment of the literature, and comparative analysis. The scrap glass came from glass bottles and other abandoned containers, while the RCA came from crushed concrete waste. The experimental studies assessed different concrete mix proportions while taking into account various replacement amounts for RCA and waste glass. Along with other crucial qualities like workability, density, and durability, the mechanical properties including compressive strength, tensile strength, and flexural strength were evaluated. The test outcomes were contrasted with a control concrete mixture made up only of common aggregates. The results of the research show that using waste glass as fine aggregate and recycled coarse material in the making of concrete can substantially reduce building costs. The mechanical performance of the concrete was not significantly affected by the use of the RCA and waste glass in place of normal aggregates. Additionally, using these recycled materials lowers the amount of waste that is transported to landfills and helps conserve natural resources. Economic concrete mixtures that use waste glass as fine aggregate and recycled coarse material have enormous potential for green building techniques. This method is appealing for large-scale construction projects due to the lower material cost and positive environmental effects. To make sure the best mix proportions are employed, it is necessary to take the specific application into account as well as the engineering requirements. This study supports continuing initiatives to advance affordability and sustainability in the building sector. As well as studying further strategies to improve the engineering features of recycled aggregate-based concrete, future research could concentrate on examining the long-term performance and durability of the suggested concrete mix.

Multi objective optimization for house roof using artificial neural network model

Roof models with low heat loss and low heating costs for buildings are crucial for reducing energy consumption and greenhouse gas emissions in cold regions. This study aimed to calculate the heat loss for various roof models with low heat loss, which are widely used in houses in Europe and other cold regions. We created 324 models for three types of roofs (light, medium, and heavy) with different materials and thicknesses using the HAP program. We then trained a neural network (ANN) to generate a mathematical function that can be used to calculate the optimal surface using a multi-objective genetic algorithm method. The resulting optimal roof design was simulated using the Ansys program on a January day. We compared the heat loss results for the optimal roof for HAP, ANN, and transient thermal analysis by Ansys, respectively, showing a close agreement among the methods.

Modeling of the interaction between human body and electromagnetic waves near resonance using machine learning

Human-Like digital models have been around for quite some time. They significantly contributed to the increase of the accuracy of the whole-body-average specific absorption rate estimations. However, the anatomical and morphological diversity between human beings has not yet been embraced by the actual anthropomorphic models for several reasons such as financial costs, excessive exposure of volunteers to electromagnetic waves, and the required number of technical experts needed to build one voxelized model. Recently, machine learning has been used to reduce the complexity of certain tasks. Yet, at least, having an anthropomorphic model per nation is still far away to achieve. To reduce the building cost of new human-like models, we build on the success of anthropomorphic models and machine learning to derive mathematical equations that make it possible to predict the Whole-body-average SAR from low frequencies up to twice the resonance frequency without any cost and excessive electromagnetic exposure of new volunteers. The completely new machine learning based equations are applicable for any age, ethnic group, and for both genders. They depend only on the human body’s morphological (height and weight) and anatomical parameters (tissue weights). In this work, we first address the whole-body-average SAR peak and we present a set of two estimators. In second, we show that the resonance frequency is not only a function of the height of the human body, to end up with a third estimation for the resonance frequency. These completely new estimators are finally combined into a novel equation that links the whole-body-average SAR to the frequency. It shows the accurate prediction for low frequencies (10 MHz) up to twice the resonance frequency. The derived estimators for the maximum WBASAR and the resonance frequencies showed better results for low frequency exposure.

Building energy performance metamodels for district energy management optimisation platforms

Reactive control strategies lack the flexibility necessary to optimize the operational costs of buildings and district systems. To overcome this limitation and to enable the transition to model predictive control strategies (MPC), the development of dedicated control platforms and models is required. Predictive models for district systems management should provide supply and demand side integrated modelling, high accuracy, generalization capacity and reduced computational times. However, traditionally available MPC solutions do not meet these requirements as simplified models offer short computational times but lack the required accuracy; detailed physics-based models provide satisfactory generalization but at the expense of high computational costs; and the generalization capacity of data models is constrained by the quality and availability of data. In contrast, metamodels developed through the combined use of physics-based models and machine learning techniques offer a powerful alternative at reduced computational cost. This paper describes an upgraded Integrated District Model concept developed through co-simulation coupling metamodels of buildings with a district heating infrastructure Modelica model. Furthermore, the process to produce the metamodels and optimization engine required to generate demand flexibility optimization functionalities for the buildings of the Stepa Stepanovic subnetwork (Belgrade) is depicted. Starting from the development of metamodels of instances of specific buildings (residential and educational use) the process was expanded to provide additional generalization to define, (1) a generic metamodel with the capacity to reproduce the behaviour of any instance of building of the residential typology, and (2) metamodels with generalization capacity in relation to operational settings. As part of this process the potential of several machine learning algorithms (e.g Support Vector Machines, etc) was evaluated including the latest ensemble boosting methods (e.g. Adaboost, Gradient Boosting and Extreme Gradient Boosting) with comparatively low use in the building simulation community. Finally, a virtual test bed consisting in metamodels coupled to an optimization engine based on genetic algorithms, was implemented, and compared to a traditional Physics-based model MPC solution (EnergyPlus-GENOPT), to evaluate the potential of the developed building level optimization functionalities. The metamodels and optimization engine were able to reproduce the optimized settings identified by the EnergyPlus-GENOPT MPC solution with cost savings potentials of 5–10%.

Air purifier or fresh air system? A comparison of energy consumption and cost in urban China residential buildings

Air purifier or fresh air system? A comparison of energy consumption and cost in urban China residential buildings

The Influence of Construction Project Management on the Planning and Design of Residential Green Buildings

<p>我國綠建築評估系統EEWH至今已有20年歷史，然而在住宿類建築當中綠建築標章案件數仍僅占總使用執照數量1%以下，標章案之總樓地板面積僅占總使用執照總樓地板面積20%以下，因此對於推廣綠建築之成果仍有進步空間。分析綠建築申請率低的原因，包含大眾認為綠建築之成本較高、綠建築標章之總建築專案期程較久、所需之審查資料繁多…等。再者，申請綠建築標章時之分數常少於申請候選綠建築證書時之分數，甚至有案件未申請綠建築標章。專案管理係指對於專案生命週期之各項工作項目予以妥善管理，以減少專案成果與預期目標的落差。於過往在建築業的案例與相關研究中，指出專案管理對於管控建築成本與工期具有實際績效，然而案例多為大型工程與廠辦類建築物，對於住宿類建築物之探討相對較少。故本研究先透過專家問卷探討建築專案管理管控綠建築指標項目之能力，以提出在專案管理思維下，設計住宿類綠建築時可優先考量採用之建築技術，以消除大眾對於綠建築在成本與時程上之顧慮。研究成果顯示，不同綠建築技術與設施設備之採用情形，對於建築專案的管控能力確實具有差異性。在EEWH-RS版本下之指標項目，以日常節能指標與二氧化碳減量指標下之指標項目，較能夠受專案管理管控。但綠化量指標、水資源指標以及汙水垃圾改善指標等，由於其技術已經成熟、成為標準配備或已規格化，致使專案管理對其無法具有較高的影響能力。研究成果係可協助建築設計師於住宿類綠建築規劃設計時，擬定出較易管控成本與時程之方案，進而提升業主採用綠建築技術與設施設備之意願。</p> <p> </p><p>Taiwan’s green building assessment system (EEWH) has a history of 20 years, but the number of green building label cases in residential buildings still accounts for less than 1% of the total number of Building Use permit, and the total floor area of the label cases are less than 20% of the total building which get Building Use permit. So there is still room for promoting the achievements of green buildings. Analyze the reasons for the low green building application rate, including the public’s view that the cost of green building is relatively high, the overall construction project period of the green building label is longer, and the amount of review information required is more than general building... Furthermore, the score for applying for a green building seal is often less than the score for applying for a candidate green building certificate. There are even cases where the green building seal is not applied. Project management refers to the proper management of various work items in the project life cycle to reduce the gap between project results and expected goals. In the past cases and related research in the construction industry, it was pointed out that project management has actual performance in controlling construction costs and duration. However, most of the cases are large-scale projects and factory buildings, and there is relatively little discussion on residential buildings. Therefore, in this study, an expert questionnaire will be used to discuss the ability of the construction project management to control the green building index project, in order to put forward the construction technology that can be considered first when designing residential-type green buildings under the project management thinking, so as to eliminate the public’s cost and schedule for green buildings Concerns. The research results show that the adoption of different green building technologies and facilities and equipment has indeed different management and control capabilities for construction projects. The indicators under the EEWH-RS version are more able to be controlled by the project management based on the daily energy saving indicators and the CO2 reduction indicators. However, the greenery indicators, water resource indicators and sewage and garbage improvement indicators, etc., because their technology has matured, become standard equipment or has been standardized, the project management cannot have a high influence on it. The research results can assist architects in the planning and design of residential-type green buildings, and draw up plans that are easier to control costs and schedules, thereby enhancing the owners’ willingness to adopt green building technologies and facilities.</p> <p> </p>

ҒИМАРАТТАРДЫҢ ҚОРШАУ ҚҰРЫЛЫМДАРЫ АРҚЫЛЫ ЖЫЛУ ШЫҒЫНЫН АЗАЙТУДЫ ЗЕРТТЕУ

One of the most important ways to save fuel and energy resources is to reduce heat losses through the building envelope. The use of high-performance heat-insulating, refractory materials makes it possible to create lightweight building envelopes that meet modern architectural requirements, allowing to reduce the operating costs of buildings by reducing heat loss. The purpose of this work is to study the causes of defects in operating buildings with enclosing structures made of sandwich panels to identify heat loss in the interface zones. The article addresses the issues of improving the nodal connections of enclosing structures made of sandwich panels, which is of great interest to designers in the construction of low-rise prefabricated buildings.