Building Cost Research Articles

Operation optimization in large-scale heat pump systems: A scheduling framework integrating digital twin modelling, demand forecasting, and MILP

The integration of large-scale heat pumps and thermal energy storage can facilitate sector coupling, potentially lowering heating and cooling costs in industries and buildings. This cost reduction can be extended by optimizing the utilization of the available thermal energy storage capacity in accordance to fluctuating electricity prices. Although the literature offers methods for optimizing the operation of these integrated systems, they often overlook the impact of heat pump performance degradation over time, such as from fouling. This oversight can lead to suboptimal system performance and inaccurate operational cost estimates. The present study addresses this gap by introducing a novel operational scheduling framework that aimed to reduce the operational costs of a commercial large-scale heat pump system. The system comprised an open cooling tower, a thermal storage tank and two heat pumps affected by fouling. The framework incorporated a mixed-integer linear programming (MILP) model, thermal demand forecasting, and heat pump performance maps that account for varying fouling levels. These maps were obtained from online calibrated simulation models used as digital twins of the heat pumps. The results demonstrated that the proposed framework enhanced the thermal energy storage utilization in response to variable electricity prices and adjusted the heat pump operation based on the influence of fouling. This resulted in a reduction of operational costs of up to 5% compared to the conventional operation of the system. These savings were observed to vary depending on the forecasting accuracy and the prevailing fouling levels. Overall, this study demonstrates the potential of using the proposed framework for cost reduction in large-scale heat pump systems.

Utilizing the Kolmogorov-Arnold Networks for chiller energy consumption prediction in commercial building

Accurate prediction of chiller energy consumption is essential for optimizing energy usage and reducing operational costs in commercial buildings. Traditional predictive methods often struggle to capture the complex, nonlinear relationships inherent in energy consumption data. This study proposes the use of Kolmogorov-Arnold Networks (KAN) to address this challenge, leveraging their ability to model intricate nonlinear dynamics with high precision. The study introduces KAN as a novel application for real-world chiller energy prediction, using actual data obtained from a commercial building. The methodology involves comparing KAN's performance with Artificial Neural Networks (NN) and a hybrid metaheuristic algorithm combined with deep learning, namely the Teaching-Learning-Based Optimization with Deep Learning (TLBO-DL). The results show that KAN achieves an R2 value of 0.9465 and an RMSE of 6.1023, outperforming NN (R2: 0.9281, RMSE: 6.7709) and TLBO-DL (R2: 0.9366, RMSE: 6.2892). The novelty of this research lies in the innovative application of KAN to chiller energy consumption prediction, coupled with advanced parameter tuning and improved computational efficiency. This study not only demonstrates the superior accuracy of KAN but also contributes to the field by showcasing its practical utility and effectiveness in energy management systems.

Analysis of the Impact of Renewable Energy Application on the Cost of Green Building Projects

Green building is a sustainable ecological building, and the application of renewable energy to green buildings can protect the ecological environment and reduce energy consumption to a certain extent, so this paper makes an in-depth analysis of the impact of renewable energy application on the cost of green building projects. Firstly, the grey correlation analysis was used to analyze the characteristic indicators of renewable energy applied to the cost of green building projects, and the estimation index system of green building projects was constructed. Then, the improved genetic algorithm was used to construct the explanatory variables and control variables of green building project cost control, the variation factors of green building project cost control were analyzed through principal component analysis, and the elastic modulus and regression discriminant method were combined to complete the impact analysis of renewable energy application to green building project cost. The simulation results show that the algorithm can effectively reduce the economic cost of green buildings, ensure the quality of buildings, and protect the natural ecological environment.

Features of mechanization of work during the construction of buildings using low-rise construction technologies

Currently, in Uzbekistan, the low-rise housing construction sector is in a stage of dynamic development and, as a rule, is aimed at developing the territories of suburbs and agricultural areas. First of all, the trend of growth in the volume of low-rise residential construction is due to the fact that the construction of low-rise buildings is much less expensive in comparison with the construction of high-rise residential buildings. In addition, the operating costs of low-rise residential buildings are significantly lower than high-rise ones, since there is no need to install expensive engineering equipment (elevator equipment, high-power pumping equipment, fire protection systems, etc.). State programs in the field of providing the country's citizens with affordable and comfortable housing provide for a comprehensive reduction in its sales value, which is primarily achieved by reducing the cost of construction, including by reducing the consumption of fuel and energy resources (FER) during the construction of buildings.

Research on the Cost Control of Green Building Project Based on the Application of Renewable Energy

Promoting and optimizing green building projects based on renewable energy applications means that green building projects require more investment than traditional buildings. Green buildings using renewable energy are different from conventional construction cost control. Full-life cycle cost management is a way to analyze and manage the planning, construction, operation, maintenance, and residual costs of construction projects. It would be best if you had the law. This study uses mathematical models combined with relevant accounting theories and research methods to analyze the overall situation of green building cost control based on the application of renewable energy.

Optimizing power system efficiency and costs in smart buildings with renewable resources

The increasing industrial development and energy demand have necessitated the maximization of available energy use and the deployment of renewable resources. Effective energy management, optimal modeling, and efficient planning are essential to transform the power system into a high-efficiency, optimal model. This study focuses on the initial step of modeling smart buildings (SBs) equipped with non-responsive devices and renewable photovoltaic sources. A comprehensive energy management (EM) plan is formulated for these buildings, incorporating the KNX protocol for solar energy system management. Batteries are integrated into the building model to store energy during periods of low consumption and serve as generators during peak load conditions, with the primary goal of minimizing power system losses and related costs. To address the complexity of this model, whale optimization algorithm (WOA) is employed for optimization. Optimal candidate buses are selected for interconnected building management based on a suggested sensitivity analysis to minimize losses. Cost performance is then assessed, considering energy production and sales. The findings indicate that substantial control of operating costs can be achieved through strategic management of battery charging and discharging, as well as the utilization of photovoltaic units. The proposed model is evaluated across various scenarios using a test system comprising 30 modified system, demonstrating its effectiveness in enhancing energy efficiency and management.

114. Evolution of costs of pig buildings from 2015 to 2021 and 2023

114. Evolution of costs of pig buildings from 2015 to 2021 and 2023

The Influence of Atmospheric Air Temperature on the Consumption of Natural Gas in Terms of Heating Costs of a Single-Family Building – A Case Study

The Influence of Atmospheric Air Temperature on the Consumption of Natural Gas in Terms of Heating Costs of a Single-Family Building – A Case Study

Dynamic Cost Allocation Allows Network-Forming Forager to Switch Between Search Strategies

Network-forming organisms, like fungi and slime molds, dynamically reorganize their networks during foraging. The resulting rerouting of resource flows within the organism's network can significantly impact local ecosystems. In current analysis limitations stem from a focus on single-time-point morphology, hindering understanding of continuous dynamics and underlying constraints. Here we study ongoing network reorganization in the foraging slime mold , identifying three distinct states with varying morphology and migration velocity. We estimate the energetic cost of each state and find a trade-off between building and transport costs within the morphological variability, facilitating different search strategies. Adaptation of state population to the environment suggests that diverse network morphologies support varied foraging strategies, though constrained by associated costs. Our findings provide insights for evaluating the impact of resource flow rerouting in changing ecosystems. Published by the American Physical Society 2024

Leveraging Cost-Effective AI and Smart Technologies for Rapid Infrastructural Development in USA

High cost of building makes houses expensive for US citizens and residents. Thus, this study proposes the leveraging of cost-effective artificial intelligence (AI) and smart technologies (ST) for rapid infrastructural development in US. It considers them as sustainable means of tackling the challenges for the attainment of affordable houses. The study explores the potentials of prominent AI and smart technologies capable of reducing the cost of building houses in the US, for which houses would become affordable for all. The primary data are obtained from telephone interviews with 10 construction workers and 5 experts of AI, alongside observation and introspection. The secondary data are drawn from library and the internet. Qualitative method, thematic and content analyses, systematic review, and descriptive and interpretive tools are employed. The results show Machine Learning, Natural Language Processing, Computer Vision, Reinforcement Learning, and Robotic Process Automation to be prominent cost-effective AI technologies, while Building Automation Systems, Internet of Things, Renewable Energy Systems, and Smart Water Management Systems are cost-effective smart technologies. The study concludes that the identified AI and smart technologies are not only cost-effective, but also transformative and innovation-driven and can be leveraged to increase efficiency, productivity, quality delivery and satisfactory services. The study recommends them to government and organizations for cost-effectiveness towards attaining rapid infrastructural development in the USA.

Environmental impact of the variations in solar reflectance index values of some selected dimension stones due to cyclic thermal shock

Natural stones are widely used environmentally appropriate building materials as dimension stone both for exterior cladding and floor coverings in urban areas. Depending on their solar reflectivity, these materials may absorb more solar radiation, especially with noontime summer sunlight exposure, which may cause both increasing cooling costs in commercial buildings and overheating of a city with the formation of urban heat islands. Solar reflectance index (SRI) is defined as the ability of a surface to reject solar heat which tends to change with physical, chemical and biological degradation processes by varying solar reflectance (SR) and thermal emissivity (TE) values. Weakening of the stone matrix structure by physical degradation mechanism, such as thermal shock (T-S) cycles mostly have significant impact on SRI. Within the scope of this study, SRI values were calculated before and after cyclic T-S by measuring the optical properties such as SR and TE values of 30 selected sedimentary and metamorphic originated carbonate-based natural stone types. The changes in surface properties such as color, roughness, and gloss, that may have an impact on the SRI values were also analyzed. In addition, a multivariate regression analysis was established to predict SRI value from such properties with a high degree of accuracy. Finally, it was found that, initial SRI values of 9 natural stone types (mostly metamorphic originated) have been increased considerably by cyclic T-S and tend to increase their ability to reduce urban temperatures by higher SRI values, one of the most important selection criteria in energy consumption for natural stones.

Achieving Net-Zero Energy Buildings: Analyzing and Optimizing Strategies Using Sensitivity Analysis

Enhancing building energy efficiency and incorporating renewable energy technologies are crucial for combating climate change and promoting sustainability. This study employed a sensitivity analysis to evaluate the influence of various parameters on building costs and energy consumption. Standardized Regression Coefficients (SRC) were used to measure the sensitivity of each variable. The type of glass, wall construction, window-to-wall ratios, and shading elements were identified as having the most significant impact on building costs and electricity usage. SRC values provided a numerical representation of the strength and direction of the relationship between these factors and the output variables. Furthermore, the study quantified the energy savings achieved through optimization methods. The data indicated an average reduction in consumption of 22%, with variations between 21% and 23% across different floors. These results highlighted the effectiveness of optimizing variables and applying energy-efficient design principles. The findings of this investigation enhance our understanding of the role of sensitivity analysis in optimizing building energy efficiency. They can serve as a reference for making decisions on integrating renewable energy technologies into buildings and designing them. These strategies can help reduce environmental impact, promote sustainable construction practices, and achieve significant energy savings.

Seismic performance and cost comparison of RC moment resisting and dual frames using UBC 97 and IBC 2021

The transition from the Uniform Building Code (UBC-97) to the International Building Code (IBC-21) marked a major shift in the definition of seismic hazard. The term “seismic hazard” in the form of peak ground acceleration (PGA) is replaced by spectral acceleration. This paper investigates the effect of using new seismic hazards on the structural performance of reinforced concrete (RC) buildings. It also looks into the financial impact on the capital costs of new buildings. Useful insights are made to understand the structural performance and financial impact of adopting IBC 21 for structural design in contrast to UBC 97. This study was carried out from the perspective of a developing country, Pakistan. Reinforced concrete moment resisting and dual frames are used as the main structural system of a typical 7-story residential building to investigate the aforementioned effect. The frames are assumed to be located in two locations with high and low seismic hazards. The effect on structural performance is investigated via nonlinear pushover analysis. Financial impact is judged mainly through cost estimation for steel and concrete. A detailed discussion is also presented on the seismic design guidelines in both codes.

Combinatorial machine learning approaches for high-rise building cost prediction and their interpretability analysis

ABSTRACT This study focuses on addressing housing gaps in populous southern countries by emphasizing the importance of accurate early cost estimation in construction projects. It compares individual cost prediction models (Decision Tree, BP Neural Network, and Support Vector Machine) with combined prediction models (BP-DT and BP-SVM) for high-rise building cost prediction. The study employs interpretative analysis methods such as the Shapley additive explanation method, partial dependence plots, and individual conditional expectation plots to explore feature interplay and improve model transparency. Results show that the BP-SVM combination algorithm significantly reduces Mean Absolute Error (MAE) compared to other models. Key factors influencing cost estimation include “Estimated project duration” and “Ground floor area”, while others like “Below-ground floors” have minimal impact. The study highlights the interaction of various feature groups on prediction outcomes. Through feature interaction analysis, it is found that the Estimated project duration and Building architecture can have some negative correlation interaction. Estimated project duration and Interior decoration may have to cancel effects Overall, it contributes to construction management by aiding investors in better-assessing project profitability, thereby enhancing investment decision efficiency and quality.

Urban Infrastructure Construction Planning: Urban Public Transport Line Formulation

Urban public transport line formulation has its appeal in promoting public convenience and developing environmentally friendly cities. During the bus line planning stage, the line frequency and stop location determination is a key issue for decision makers. Our study focuses on the integrated formulation problem between line frequency and stop planning featuring multi-type vehicles. The multi-type vehicles are able to accommodate the various passenger demands at either peak hours or off-peak hours. The a priori magnitudes of user demands are investigated by drone-based technique methods in the tactical-level plan. The collected geospatial data can assist the public transport user forecast. A mixed-integer linear programming (MILP) model is proposed. The objective is to minimize the walking cost of passengers, the building cost of stops, and the operation cost of service frequency. The effectiveness of the model is validated by a real case in Nantong, China. CPLEX is used to resolve the MILP model. Yielding to the budget constraint, in high-price, medium-price, and low-price scenarios, the optimal high-quantity stop scheme can save 3.04%, 3.11%, and 3.38% in overall cost compared with the medium-quantity stop scheme, respectively; their cost savings are 8.53%, 8.70%, and 9.09% more than the costs of the low-quantity stop scheme.

Applying Ai Techniques To Analyze Soil Data And Improve Foundation Design In Construction In Iraq

In geotechnical engineering, building robust structures is crucial to ensure the bearing capacity of structures against external forces, so making sure soil strength and unreliable build cost and duration prediction are also very important and preliminary aspects of any construction project. Therefore, in this first-of-its-kind modern examine, the capability of various artificially intelligent (AI)-based models toward reliable forecasting and estimation of preliminary construction expenses, duration, and strength at shear is explored. First, background information about the revolutionary artificial intelligence (AI) technique along with its many distinct models ideal for geotechnical and building engineering problems is presented, The use of AI-based models in the literature for the aforementioned construction and maintenance applications is discussed in a number of current works, together with their benefits, drawbacks, and future directions. Several important input elements that significantly affect the preliminary price of construction, construction time, and soil's shear strength estimation are listed and given through analysis. Finally, some obstacles to employing AI-based models for precise forecasts in these applications are discussed, along with elements influencing the problems with cost overruns. Thus, this work can help civil engineers make effective use of artificial intelligence (AI) to solve difficult and risky tasks. It can also be used to Internet of Things (IoT) environments for self-learning applications like smart architectural health-monitoring systems

The Issue of Estimating the Maintenance and Operation Costs of Buildings: A Case Study of a School

The Issue of Estimating the Maintenance and Operation Costs of Buildings: A Case Study of a School

Evaluating the Energy Efficiency of Combining Heat Pumps and Photovoltaic Panels in Eco-Friendly Housing

This article aims to analyze the energy efficiency of combining heat pumps with photovoltaic (PV) panels in energy-efficient homes. The research methodology involved a detailed energy balance analysis, assessment of the impact of mechanical ventilation, location, heat loss, and the choice and operation of heat sources, with a particular focus on heat pumps in synergy with PV installations. The results demonstrate that integrating heat pumps with PV panels can significantly reduce the demand for external energy sources and lower the operating costs of buildings, while contributing to their energy self-sufficiency. This study highlights that such a combination of technologies is key to promoting sustainable development and achieving energy efficiency goals in the residential sector. The results of this analysis expand knowledge about the effectiveness of such systems and provide practical recommendations for designers and engineers interested in implementing renewable energy technologies in modern energy-efficient buildings, taking into account the impact of these solutions on reducing CO2 emissions as well.

АНАЛИЗ СТРУКТУРЫ ЕДИНОВРЕМЕННЫХ ЗАТРАТ ПРИ ЭНЕРГОСБЕРЕГАЮЩИХ МЕРОПРИЯТИЯХ ДЛЯ ИНДИВИДУАЛЬНОГО ЖИЛОГО ЗДАНИЯ

The paper presents the results of an analysis of the impact of energy-saving measures on the struc-ture of one-time costs for an individual residential building. The options of insulation of the external vertical shell of the building for a wall made of ceramic bricks, aerated concrete and a two–layer structure - aerated concrete with polystyrene are considered

ROAD SAFETY AS FUNDAMENT IN ROAD INFRASTRUCTURE PLANNING

The road safety is a key indicator for the development of the society. May it be a key factor in the development of the road infrastructure? How will it affect the costs of road planning, construction, and maintenance? Will the “saved costs” from lowering the road accidents cover the higher cost of building and maintaining the roads? All those questions are difficult to answer; some seem even impossible. Nevertheless, it is important to analyse the problem, establish theoretical models, and formulate hypotheses. So we need to analyse the correlation between road safety, all the costs for road infrastructure, and the social effect in a theoretical manner. The paper aims to formulate the basis of the problem, and the potential solutions, analyse the needed data, and establish the theoretical models.