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The realm of green energy is in constant flux, drawing considerable attention from stakeholders dedicated to minimizing environmental impact, reducing costs, and developing structures that align with stringent standards. This study introduces an innovative approach aimed at improving onshore wind tower foundation systems, emphasizing both engineering and financial feasibility. The approach involves a comprehensive analysis of design load cases, particularly emphasizing resistance against overturn, while ensuring compliance with Eurocode guidelines. The foundation system is conceptualized as a beam slab with voids filled by soil material. High reduction in concrete quantity is achieved by reaching 30%, while the steel reduction reaches 90%. It is worth mentioning that the total cost is reduced by up to 70%. Furthermore, as a future trend, this study aims to integrate the new foundation system with steel 3D printing technology in the manufacturing process of the wind tower's structural elements. This integration is expected to enhance the precision and customization of the superstructure-foundation system, thereby improving overall performance and efficiency. The optimized design not only significantly reduces construction costs but also streamlines installation, saving time. Simultaneously, this study enhances the structural behavior of the wind tower foundation by focusing on elements crucial to its efficiency. [ABSTRACT FROM AUTHOR]

Summary Variation in leaf venation network architecture may reflect trade‐offs among multiple functions including efficiency, resilience, support, cost, and resistance to drought and herbivory. However, our knowledge about architecture‐function trade‐offs is mostly based on studies examining a small number of functional axes, so we still lack a more integrative picture of multidimensional trade‐offs. Here, we measured architecture and functional traits on 122 ferns and angiosperms species to describe how trade‐offs vary across phylogenetic groups and vein spatial scales (small, medium, and large vein width) and determine whether architecture traits at each scale have independent or integrated effects on each function. We found that generalized architecture‐function trade‐offs are weak. Architecture strongly predicts leaf support and damage resistance axes but weakly predicts efficiency and resilience axes. Architecture traits at different spatial scales contribute to different functional axes, allowing plants to independently modulate different functions by varying network properties at each scale. This independence of vein architecture traits within and across spatial scales may enable evolution of multiple alternative leaf network designs with similar functioning. [ABSTRACT FROM AUTHOR]

The purpose of this research is to develop a new method for automatically optimizing the seawall cross-section with composite slopes and a berm, considering both overtopping discharge and construction cost. Minimizing these competing multi-objectives is highly challenging due to the intricate geometry of seawalls. In this study, the surrogate model optimization algorithm DYCORS (Dynamic COordinate search using Response Surface models) is employed to search for the optimal seawall geometry, coupled with the ANN (Artificial Neural Network) model for determining the overtopping discharge. A total of 20 trials have been run to evaluate the performance of our methodology. Even the worst-performing Trial 7 among these 20 trials shows a satisfactory performance, with a reduction of 17.67% in overtopping discharge and a 12.1% decrease in cost compared to the original solution. Furthermore, compared to other optimization schemes using GAs (Genetic Algorithms) with the same decision vectors, constraints, and multi-objective functions, the methodology has been proven to be more effective and robust. Additionally, when facing different combinations of wave conditions and water levels, there was a 27.8% reduction in objective function value compared to the original solution. The optimal results indicate that this method can still be effectively applied for optimizing the seawall cross-section as it is a general method. [ABSTRACT FROM AUTHOR]

As the global transportation sector increasingly adopts electric vehicles, the demand for advanced and accessible charging infrastructure is rising. In addition to at-home electric vehicle (EV) charging, there is a growing need for the swift development of commercial direct current fast charging (DCFC) stations to meet on-the-go EV charging demands. While government funds are available to support the expansion of the EV charging network in the United States, the establishment of a robust nationwide EV charging infrastructure requires significant private sector investment. This study was conducted to assess the economic feasibility of various business models for fast charging stations in the U.S. using two case studies and exploring different operational strategies including sole ownership and collaborative ventures with public and private entities. The results indicate that based on the current adoption and utilization rates in the U.S., the business model involving an owner-operator collaborating with a public partner ensures profitability and protects the investment in DCFC stations from financial losses. The study also highlights that demand charges and electricity retail prices are the factors that affect the profitability of a DCFC station. [ABSTRACT FROM AUTHOR]

The realm of green energy is in constant flux, drawing considerable attention from stakeholders dedicated to minimizing environmental impact, reducing costs, and developing structures that align with stringent standards. This study introduces an innovative approach aimed at improving onshore wind tower foundation systems, emphasizing both engineering and financial feasibility. The approach involves a comprehensive analysis of design load cases, particularly emphasizing resistance against overturn, while ensuring compliance with Eurocode guidelines. The foundation system is conceptualized as a beam slab with voids filled by soil material. High reduction in concrete quantity is achieved by reaching 30%, while the steel reduction reaches 90%. It is worth mentioning that the total cost is reduced by up to 70%. Furthermore, as a future trend, this study aims to integrate the new foundation system with steel 3D printing technology in the manufacturing process of the wind tower’s structural elements. This integration is expected to enhance the precision and customization of the superstructure-foundation system, thereby improving overall performance and efficiency. The optimized design not only significantly reduces construction costs but also streamlines installation, saving time. Simultaneously, this study enhances the structural behavior of the wind tower foundation by focusing on elements crucial to its efficiency.

The demand for rapid metro systems has been increasing steadily because of the continuous need to increase the scheduled speed of trains. However, some metro routes currently operating in South Korea were built a long time ago, without considering the requirements of a rapid metro system. In a general rapid metro system, additional sidetracks are required to avoid possible collisions between express and local trains. However, due to the high cost of new sidetrack construction on existing metro lines, an effective method for the implementation of a rapid metro system in terms of construction cost and operation time is essential. A new concept for rapid metro system operation is thus proposed by modifying existing Y-branch lines and utilizing high-performance trains as local trains and the existing trains as express trains. The high-performance train provides improved acceleration and deceleration, as well as wider doors to reduce boarding and alighting time. The sum of the acceleration and deceleration of the high-performance local train is then minimized according to the constraints of the minimum time gap between express and local trains and the need to enhance the scheduled speed of all trains. Finally, the optimization results are applied to a simulation of Seoul Metro line no. 5 as a case study. The results show that the use of express and local trains in the proposed rapid metro system achieves operation times reductions of 25.6% and 10%, respectively, compared to the present metro system.

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.

Construction cost is difficult to predict in high-standard farmland projects, due to the short construction period. In this study, the construction cost was predicted for the single-unit irrigation projects, in order to improve the prediction accuracy. The key influencing factors were then screened from the construction cost of high-standard farmland irrigation projects using random forest (RF). Then, convolutional neural networks (CNN) and support vector machine (SVM) were combined to construct a CNN-SVM-based prediction model, in order to improve the prediction accuracy over a single model. The penalty parameter C and kernel function parameter g of the CNN-SVM model were optimized by northern goshawk optimization (NGO). The NGO shared the higher convergence speed and stronger optimization, compared with the rest. Finally, the prediction model (NGO-CNN-SVM) was obtained for the construction cost of high-standard farmland irrigation projects. The data was collected from the irrigation project in the high-standard farmland in Liaoning Province from 2018 to 2023. Coefficient of determination (R² ), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were taken as the accuracy indexes for analysis. The results show that the NGO-CNN-SVM model of construction cost in the channel project shared the MAE and RMSE lower than 0.615 and 0.512 million yuan, respectively, where the R² reached more than 0.968, and the relative error was less than 4.21%; In the project of the inlet sluice gate, the MAE and RMSE were lower than 0.610 and 0.536 million yuan, respectively, where the R² reached more than 0.966, and the relative error was less than 4.41%; In the project of bridge and culvert, the MAE and RMSE were less than 0.494 and 0.477 million yuan, where the R² reached more than 0.970, and the relative error was less than 3.548%. Taking channel engineering as an example, the deep learning network model (CNN) was reduced by about 34%, 20%, 33% in the MAE, RMSE, MAPE, and improved by 3% for the R², respectively, compared with the traditional back propagation neural networks. A higher efficiency of prediction was achieved in the less complexity and nonlinear data dimension. In addition, the CNN-SVM model was significantly reduced by about 40%, 46%, 41% in the MAE, RMSE, MAPE, and improved by 4% for the R², respectively, compared with the model CNN, indicating better performance than that of the single model. Compared with the CNN-SVM model, the MAE, RMSE, MAPE of the NGO-CNN-SVM model were reduced by about 22%, 25%, 17%, and improved by 4% for the R², respectively, indicating that the randomly generated hyperparameters led to the low generalization and prediction accuracy. The optimal optimization of the hyperparameters had further improved the performance of the improved model. In summary, the feature selection, model fusion, optimization, and comparison of different models show that the NGO-CNNSVM model shared higher prediction accuracy and generalization. The model can be applied to directly establish the index system, in order to estimate high-standard farmland projects. The finding can provide the theoretical basis and support to formulate the construction cost and budget quota of irrigation projects in high-standard farmland. [ABSTRACT FROM AUTHOR]

At present, most network models do not fully consider the prior information on construction project cost, and do not do much research to find out more information or effectively utilize the information the find on construction project cost; conversely, some previous networks mainly consider the depth of the network for the reconstruction results. In addition, there is no detailed discussion on the selection of loss functions for various algorithms, and some common error loss functions are directly used. In view of these shortcomings, a fast estimation model of construction cost based on an improved convolutional neural network is proposed. The construction project is decomposed according to the project characteristics. The project characteristic factors that have a greater impact on the project cost and the main project quantity are selected and analyzed. Then, the convolutional neural network principle is used to analyze the project characteristic factors, the project cost and main project quantities. We simulate and compare the established convolutional neural network models, verify the usability of the model, and finally get a model with high reliability. The establishment of this model provides a new method for the estimation of project cost and main economic indicators, which can meet the actual work requirements to a certain extent. [ABSTRACT FROM AUTHOR]

This study aims to address the challenges of capturing design changes, supply chain fluctuations, and labor cost variations to improve the accuracy and real-time nature of intelligent building construction cost predictions. It seeks to accurately forecast and optimize project costs. The study innovatively constructs an intelligent building construction cost prediction model based on Building Information Modeling (BIM) and Elman neural networks (ENNs), denoted as the BIM-ENN model. The BIM-ENN model first introduces BIM technology to digitize and visualize information related to building structures, electromechanical systems, and pipelines. The digitized data obtained through BIM technology is then used as input data for the ENN, which optimizes the neural network parameters to predict and optimize intelligent building construction costs. Finally, the BIM-ENN model is experimentally evaluated. The results demonstrate that the prediction value of the construction cost of the intelligent building by this model closely matches the original information price, with a prediction accuracy of 95.83 %. Compared with the single ENN, the root mean squared error of the BIM-ENN model is less than 75, and the determination coefficient is above 0.95. This indicates that this model can explain more than 95 % of the construction cost prediction results, making it a feasible solution for actual prediction problems and achieving satisfactory results. The intelligent building construction cost prediction model reported here exhibits high accuracy and reliability. It can successfully forecast construction costs, providing robust decision support for the digitalization and intelligent development of construction enterprises. The practical significance of this study lies in providing the construction industry with an accurate cost management tool that helps enterprises optimize budget control and resource allocation, enhancing risk assessment and management capabilities. Moreover, the potential impact of the BIM-ENN model is its ability to elevate prediction standards within the construction industry, promote technological integration and innovation, enhance enterprise competitiveness, and drive the industry's transition towards digitalization and intelligent sustainable development.

In recent years, a few studies have been working on the optimization of 3D reinforced concrete structures. The number of research on this subject is increasing day by day. However, in these studies, the real world-structure is generally not used; moreover, only regular structures are used, and RC shear walls do not continue on all floors. Therefore, 3D evaluation of the optimization of real-world RC structures is possible for a small number of building types. There is a need for 3D optimization studies that can be used for all building types. In this study, 3D optimization of a real-world reinforced concrete structure is carried out. The sample structure used in the paper is not only a regular reinforced concrete-framed structure, unlike other studies, but also a RC framed structure with RC shear walls continuing on each floor. In the numerical example, the effect of the RC shear walls continuing on each floor is applied in the analysis, and the design processes of the construction projects on a real-world building and the quantity-cost analysis are made together. All optimization processes are directed automatically using the ACDOS programming, which is developed by the authors. ACDOS can take into account the commercial program named Etabs to make design of construction and the metaheuristic algorithms to reduce the total cost of construction. The results obtained from this study showed that the cost (concrete-rebar-formwork) of the 7-story public dormitory building was reduced by 9.18% when compared with its real-world application. [ABSTRACT FROM AUTHOR]

Reinforced concrete (RC) bent structures are widely used in single-story industrial buildings, with a considerable portion of them located in high-intensity seismic areas. Therefore, it is significant to study the seismic performance of the RC bent column while keeping construction costs in mind. To evaluate the seismic performance of the RC bent column from an economic perspective, pseudo-static tests combined with construction cost analysis were first carried out on three RC bent columns designed according to different versions of Chinese codes. A numerical simulation method based on the extended finite element method (XFEM) for the RC bent column was then established and verified. Subsequently, the relationship between the local damage index related to the apparent damage characteristics and the overall damage index was established by conducting numerical analysis on RC bent columns with different design parameters. Based on the established relationship between the local and overall damage indexes, a calculation method for the residual bearing capacity based on apparent damage was developed. The results show that, when compared to the constructional requirements in the Chinese code for seismic design of industrial and civil buildings published in 1978 (TJ11–78), the constructional requirements in the Chinese code for seismic design of buildings published in 1989 (GBJ11–89) can effectively reduce the stress concentration at the column bottom and delay the formation of the plastic hinge with minimal cost increase, while the constructional requirements in the Chinese code for seismic design of buildings published in 2016 (GB50011–2010) can further effectively reduce the damage to the upper column. Besides, the constructional requirements in GBJ11–89 and GB50011–2010 can improve the energy dissipation and maximum bearing capacity of the RC bent column while decrease the cost/ductility coefficient by more than 5.0 %. The proposed calculation method can effectively predict the residual bearing capacity of earthquake-damaged RC bent columns by comparing the calculation results to the test results.

The purpose of this research is to develop a new method for automatically optimizing the seawall cross-section with composite slopes and a berm, considering both overtopping discharge and construction cost. Minimizing these competing multi-objectives is highly challenging due to the intricate geometry of seawalls. In this study, the surrogate model optimization algorithm DYCORS (Dynamic COordinate search using Response Surface models) is employed to search for the optimal seawall geometry, coupled with the ANN (Artificial Neural Network) model for determining the overtopping discharge. A total of 20 trials have been run to evaluate the performance of our methodology. Even the worst-performing Trial 7 among these 20 trials shows a satisfactory performance, with a reduction of 17.67% in overtopping discharge and a 12.1% decrease in cost compared to the original solution. Furthermore, compared to other optimization schemes using GAs (Genetic Algorithms) with the same decision vectors, constraints, and multi-objective functions, the methodology has been proven to be more effective and robust. Additionally, when facing different combinations of wave conditions and water levels, there was a 27.8% reduction in objective function value compared to the original solution. The optimal results indicate that this method can still be effectively applied for optimizing the seawall cross-section as it is a general method.

When the statistical data on occupational accidents in Turkey are analyzed, it is seen that there is an increase in the number of deaths, occupational diseases and permanent incapacity to work as a result of occupational accidents. Among all sectors, the construction sector ranks first compared to other sectors in terms of the number of occupational accidents and severe consequences. In this study, data on occupational accidents that occurred in 2020 and 2021 in workplaces in the construction sector operating under 3 headings in the NACE codes in the Workplace Hazard Classes List were obtained from the General Directorate of Service Delivery of the Social Security Institution and analyzed. The causes of occupational accidents were determined by classifying the notifications made to the Social Security Institution as a result of occupational accidents, and it was determined that the most fatal occupational accidents occurred due to falling from height. In order to prevent occupational accidents caused by falling from height, which is the most important cause of fatal occupational accidents in the construction sector, the use of temporary edge protection systems in accordance with the standards and their effect on the project cost were investigated. [ABSTRACT FROM AUTHOR]

This research examined the uncertainty events encountered in the process of constructing highways and evaluates their impact on South African highway construction costs. Due to the lack of appropriate evaluation of the impact of uncertainty events encountered in the construction process of highway projects, the construction costs of these projects are underestimated. To counteract such underestimation, this research developed an adaptive neuro-fuzzy inference system (Anfis) to assess the impact of uncertainty events involved in the construction of linear infrastructure projects. To validate the Anfis model, the stepwise regression analysis (SRA) and fuzzy Bayesian network (FBN) models were designed, and their results were compared with the outputs of Anfis. The prediction performance comparison proved that Anfis has a higher performance than SRA and FBN. Based on the results of the study, it can be deduced that the Anfis model is an accurate and reliable technique in assessing the impact of uncertainty events on the cost of construction projects. Therefore, the study concluded that using hybrid intelligent machine learning techniques not only minimises the time and difficulty of the estimation process but also reduces the potential inconsistency of correlation between variables in construction cost prediction. [ABSTRACT FROM AUTHOR]

The issues that the construction sector currently faces with regard to productivity and efficiency are well acknowledged. In the construction industry, there is plenty of space for efficiency to improve, with an increasing number of new tools and methods coming out. One of the solutions to increase efficiency is the application of modern methods of construction. The modern methods of construction, especially dry construction techniques, are developing so that there is a larger volume of high-quality production with a shorter time for procurement. Not only in the construction of skeletons but also in the finishing works, it is a huge advantage if there are implemented techniques that eliminate traditional wet construction works and thus shorten the construction time. On the other hand, however, the question of efficiency in relation to their costs is raised. Based on theoretical and empirical research, the aim of this study is to demonstrate the potential of modern dry construction systems and solutions for finishing works, especially in relation to the construction time and construction cost. For this purpose, an expert knowledge system, named the complex COMBINATOR, was developed. Through a set of simulations with the help of the COMBINATOR, the effects of different combinations of dry construction systems and techniques (DCSTs) and traditional wet construction systems and techniques (WCSTs) on the time and cost of finishing construction works were measured. Based on the results of simulations carried out through the complex COMBINATOR with an inference engine that enabled these simulations, the potential of dry construction techniques for the implementation of finishing works in the construction of residential buildings was demonstrated. Without simulating the effects of the individual technological models for finishing construction works in relation to two of the most important parameters of construction projects, namely time and cost, it would not be possible to obtain the resulting parameters for different combinations of DCSTs and WCSTs from the study presented. Therein lies the huge importance of the presented knowledge system for deciding on the benefits of DCSTs. [ABSTRACT FROM AUTHOR]

It is important for regional sustainable development that how to enhance the sewage treatment capacity of cities and improve river water quality. This paper took the Nanming River n Guiyang as the study area, built a one-dimensional coupling simulation modal of water quantity and quality based on MIKE 11 software to study the distribution of ammonia-nitrogen concentration and the causes of water pollution. Also a multi-objective optimization modal of sewage treatment plants was constructed n consideration of cost and water quality for man polluted reaches, setting cost minimizing and water cleaning as optimization goals, and NSGA- II algorithm was adopted to quantitatively analyze the correlation between the investment of sewage treatment plants and ammonia-nitrogen concentration of the river. Then the multi-objective optimization scheme of sewage treatment plants was recommended. The results are shown as follows. 1 There are three areas where the ammonia-nitrogen concentration exceeds the standard, with a total of 5 .23 km of reaches polluted, mainly polluted by Plant 2,Plant 13 and Guancheng River. 2 According to the optimization model, the reasonable investment of the sewage treatment plants should be 628-~850 million yuan, with the maximum ammonia-nitrogen concentration between 1.39 mg/L and 1.50 mg/L. For every 0.01 mg/L decrease of ammonia-nitrogen concentration in the river, the investment of the sewage treatment plants needs to increase by 20 million yuan. 3 The discharge water quality of plant 2 and plant 13 is proposed to improve from the first-level A standard to the quasi-IV class, and four optimization schemes are proposed for the new sewage treatment plant to meet the multi-objective optimization scheme settings of the sewage treatment plant under different needs. [ABSTRACT FROM AUTHOR]

The article presents a comprehensive analysis of technology, time and costs of three methods of building a single-family house; traditional brick, reinforced concrete prefabrication and timber frame. The goal of this study was to determine if prefabricated and timber frame building methods and materials have the potential to replace traditional method of construction in the context of cost and time. For this purpose, a qualitative analysis was performed, including a list of benefits of each of the analysed construction technologies and a quantitative analysis in which the cost of finished houses per 1 m2 of usable area was compared. The analyses were conducted for two single-family houses with similar characteristics using scheduling and cost estimation software. The conducted analyses have shown that the shortest time to build a house is in the prefabricated reinforced concrete technology. The used construction technology from ready-made prefabricated elements affects the time of building house and thus, the costs of its construction. The construction time for the house in case of a timber frame structure and made of ready-made reinforced concrete prefabricated elements is similar but the cost of a timber frame structure is much higher. It takes longest time to build a house in traditional brick technology and requires the involvement of the largest financial resources from all three analysed construction technologies. Despite this, traditional brick technology is the most used in construction in Poland and other Central and Easter Europe countries. This is due to the widespread belief of investors about the durability of a building made in this technology and the habits of investors resulting from a long-standing tradition of construction. However, the study’s results in the world showed that a change in build technology is a step in addressing the concerns of poor quality and reduce construction costs and time, increasing the construction sector’s productivity and sustainability.

Accurate construction cost calculation is crucial for assessing project viability and selecting design programs. This paper enhances calculation accuracy by first employing the Boruta algorithm to identify vital cost-influencing factors, which serve as the basis for an improved construction cost model. We introduce an enhanced Artificial Neural Network (ANN) model that integrates the AdaBoost algorithm and cost-sensitive methods to refine construction cost estimations. The efficacy of this model is demonstrated through its overall engineering cost error rate of 3.92%, with specific errors in single-side cost, labor, materials, and machinery usage at 3.51%, 7.09%, 3.36%, and 7.93%, respectively. These results meet established accuracy standards, showcasing the model’s potential to significantly improve construction cost management and control.

Excessive weight and a growing number of permit vehicles can cause an accelerated bridge damage. Therefore, there is a need to better control operation of heavy permit vehicles. This paper presents an approach to assess bridge life consumption by permit loads. An economic lifetime of a bridge is considered in terms of the number of load cycles to failure so that the structure has to be replaced. Each passage of a truck causes one or more load cycles and thus reduces or consumes a part of the total remaining fatigue life. An incremental consumption equation is derived, and it allows to convert fatigue damage to dollar damage by considering the bridge construction cost, traffic volume, and bridge parameters. The incremental consumption covers only weight exceeding the legal limits. This consumption approach is presented for representative average values and the individual consumption approach with the GIS system using Florida as an example. A developed approach can help state agencies to establish a rational and fair permit fee structure. The methodology is transparent and can be used by any state utilizing the available databases. [ABSTRACT FROM AUTHOR]

Prefabricated volumetric modular building construction is an emerging technology in many countries used to develop the construction industry through its value-added benefits. The adoption of these novel technologies in the Sri Lankan construction industry has been relatively slow compared with other developing countries, delaying the development of the construction industry and the attainment of the added benefits of these technologies. Therefore, this study aims to identify the suitability of modular construction for the Sri Lankan construction industry via a survey and case study. A questionnaire survey was developed and distributed to assess and identify the benefits of implementing the concepts and constraints in Sri Lanka. Then, the construction cost of a proposed single-story house delivered through conventional and modular practices was compared. The survey results showed that introducing a modular concept to the industry is suitable and would benefit the Sri Lankan construction industry. Further, survey participants believed that the economic benefits brought in by modular construction are significantly more important than environmental and social benefits when selecting modular construction. The case study on a single-story affordable modular house showed a 32% reduction in total construction costs and a 36% reduction in labour costs compared to conventional house construction. Further, costs for total modular construction and labour were 32% and 36% less than those of conventional construction costs and labour costs. Moreover, a 16% reduction in embodied energy was observed when compared with conventional construction. Therefore, the modular concept could be used to construct affordable houses and will be cost-effective with the correct choice of material. [ABSTRACT FROM AUTHOR]

The growth trend in the container industry has had a significant operational and infrastructural impact on the ports. In this context, the offshore port systems are expected to be an innovative port system in the effective use of mega container ships by offering operational and structural advantages. This study aims to guide the authorities on whether OCPS is worth investing in, in terms of investment costs. This article also highlights the financial feasibility of an OCPS that can allow countries with difficult nautical access to mega vessels to enter the global trade network. The data in this research were obtained from similar projects and studies with a series of industry assumptions. Overall, these results indicate that OCPS can compete with conventional ports in terms of investment costs, operating costs, and return on investment. Thus, a more sustainable container shipping network can be achieved with the advantages of OCPS. [ABSTRACT FROM AUTHOR]

Inserting a prestressed high-intensity concrete pipe pile into a cement–soil mixing pile can form a new composite pile named the composite foundation with stiffened deep cement mixing (SDCM) pile. The Huanghuai alluvial stratum in China is selected to carry out the in situ test for the SDCM pile, and the bearing capacity of the SDCM pile is tested by the slow-speed maintenance load method. The results show that for an SDCM pile with dense silt and fine sand as the bearing layer, the characteristic value of bearing capacity can reach 2,300 kN, which means an SDCM pile with a length of 8.0 m meets the load requirement of a general high-rise building. Further analysis shows that the SDCM pile saves more than 40% of the cost compared with the traditional CFG pile and has better quality control advantages at the same time. This research can provide basic data and design references for similar site foundation projects.

The affordability of sustainable building remains a significant challenge; developers, building owners, and design teams often point to high construction costs as the primary obstacle hindering the uptake of sustainable building. Because of a lack of clarity, empirical studies and evidence supporting the claim of sustainable building as expensive are inadequate, thus the perceived high cost of sustainable building is still debatable. This study has a three-pronged approach: (1) to collect actual construction costs of certified sustainable buildings, (2) to take a deep dive into construction cost using empirical data and analyze the correlation between the level of sustainability and the construction cost, and (3) to examine the influence of cost components on the total construction cost. Data from 72 LEED buildings was collected and included in the analysis. The findings show the following: (a) the construction costs of the studied sustainable buildings are comparable to those of conventional buildings (even lower); (b) the correlation between the level of sustainability and the construction cost is inconclusive; and (c) soft costs, especially the design cost, have a similar level of influence on the total construction cost as those of hard costs. [ABSTRACT FROM AUTHOR]

The European Union set ambitious targets to achieve climate neutrality by 2050, and one of the measures taken towards this goal was the implementation of nearly Zero-Energy Buildings. Despite the commitments of the EU member states, many householders and investors had a disposition to incredulity regarding the energy efficiency of the buildings due to the higher cost of the investment and the relatively long payback time. However, at the end of 2021, the energy crisis significantly rewrote the circumstances, and energy prices and the costs of construction materials began to rise. In this situation, it was necessary to reconsider the importance of energy efficiency for buildings due to the maintenance costs. This article aims to assess changes in payback periods over the past 13 years and conduct life cycle cost evaluations by comparing energy-efficient residential buildings with traditional houses. The analysis considers variations in construction materials and labor costs in Romania, as well as energy price changes during the building's operational phase. Through these methodologies, it has been demonstrated that the implementation of energy-efficient buildings offers a cost-effective solution already in the medium term, providing incentives for investors and future houseowners to reduce their dependence on energy and pursue long-term decarbonization. [ABSTRACT FROM AUTHOR]

In many different development projects, we need to minimize construction costs and usable area according to the design constraints assigned to each project. The aim of the research is to contrive a mathematical model for calculating the dimensions and sizes of rooms in a residential plan to get the least cost and manage the resources of construction. This paper presents a mathematical model (nonlinear programming) to achieve the above-mentioned goals according to the various design constraints and conditions, depending on the nature of the project and the goal of its establishment. To check the efficiency and accuracy of results the system of the nonlinear objective function and nonlinear constraints are solved using Mathematica and GAMS programs in two different cases. Tables and graphical representations of the results are very useful to the problem stated in this study. The study was applied to one of the projects of the New Urban Communities Authority in Egypt.

This paper presents the development of a novel multiobjective model for optimizing the planning of airport airside expansion projects that provides the capability of minimizing both airport operations disruption cost and total construction cost. The model computations are performed in four main modules: (1) a simulation module that calculates and quantifies the impact of airport construction activities on airport operations, (2) a multiobjective genetic algorithms optimization module that identifies an optimal schedule for airport airside expansion projects, (3) a scheduling module that calculates the start and finish times of each construction activity, and (4) a cost module that computes the total cost of construction-related disruptions in airport operations and the total construction cost. A real-life case study of an airport airside expansion project is analyzed to illustrate the use of the model and highlight its original contributions to the body of knowledge including its novel methodologies for (1) quantifying and minimizing the impact of construction activities on airport operations, (2) analyzing and minimizing the impact of air traffic data and airport operations on total construction cost, and (3) generating a set of optimal construction plans including optimal project start time, activities start time, and weekly and daily work plans for all activities. The results of the case study clearly illustrate the original contributions of the developed model and its novel capabilities that are expected to support airport and construction planners in their efforts to improve the cost-effectiveness and functional performance of airports during airside expansion projects. [ABSTRACT FROM AUTHOR]

This study aims at investigating the cost-effectiveness of developing a new type of house, the floating house, as a solution to land scarcity in metropolitan coastal cities. Under value management framework, the "function analysis system technique" diagram was established to identify the functions of land-based and floating houses. Participants with different backgrounds shared their views through the two designed questionnaires, assisting in determining the cost allocation and functional performance levels of the houses. Fuzzy set theory was utilized to convert the collected professional knowledge and public opinions into numerical measurements. The obtained function values disclosed that, despite weaknesses in the conventional living and structural issues, the floating house still shows competitiveness and opportunities in customer attraction, environmental sustainability, and encouraging local tourism. This paper offers the evidence and reference to both practitioners and governments seeking for a better value of the money they invest in future floating house projects. [ABSTRACT FROM AUTHOR]

The article presents a comprehensive analysis of technology, time and costs of three methods of building a single-family house; traditional brick, reinforced concrete prefabrication and timber frame. The goal of this study was to determine if prefabricated and timber frame building methods and materials have the potential to replace traditional method of construction in the context of cost and time. For this purpose, a qualitative analysis was performed, including a list of benefits of each of the analysed construction technologies and a quantitative analysis in which the cost of finished houses per 1 m² of usable area was compared. The analyses were conducted for two single-family houses with similar characteristics using scheduling and cost estimation software. The conducted analyses have shown that the shortest time to build a house is in the prefabricated reinforced concrete technology. The used construction technology from ready-made prefabricated elements affects the time of building house and thus, the costs of its construction. The construction time for the house in case of a timber frame structure and made of ready-made reinforced concrete prefabricated elements is similar but the cost of a timber frame structure is much higher. It takes longest time to build a house in traditional brick technology and requires the involvement of the largest financial resources from all three analysed construction technologies. Despite this, traditional brick technology is the most used in construction in Poland and other Central and Easter Europe countries. This is due to the widespread belief of investors about the durability of a building made in this technology and the habits of investors resulting from a long-standing tradition of construction. However, the study's results in the world showed that a change in build technology is a step in addressing the concerns of poor quality and reduce construction costs and time, increasing the construction sector's productivity and sustainability. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the burgeoning growth of China's construction industry, the limitations of traditional building methods, such as substantial raw material consumption, environmental pollution stemming from materials, and inefficient production processes, have become increasingly evident. In contrast, prefabricated buildings have emerged as a sustainable alternative, celebrated for their low carbon footprint, eco-friendliness, accelerated construction timelines, and enhanced safety and reliability. Prefabricated construction aligns with the national imperative for green building practices, but it does present cost challenges that the construction industry must address. As the construction landscape evolves, the rapid advancement of Building Information Modeling (BIM) technology takes center stage in effectively controlling costs. This paper focuses on the optimization and cost control of prefabricated buildings, leveraging the transformative potential of BIM technology. By analyzing the various stages of prefabricated building construction, from architectural design and production to transportation and assembly, we scrutinize the primary factors influencing costs. Through the integration of BIM technology, we establish a collaborative, visual, and streamlined cost control system. This system not only aligns with the principles of sustainability and efficiency but also leads to a reduction in construction costs. The research culminates in the development of a BIM-based cost control system specifically tailored to prefabricated buildings. Its feasibility and effectiveness are validated, underscoring its potential significance for the construction industry. Our findings reveal that employing Autodesk Revit software to create BIM models and calculate the costs of prefabricated buildings can optimize construction expenses, timelines, and quality. It enhances construction efficiency, refines assembly precision, and ultimately reduces costs. These research outcomes hold practical implications for the construction industry as it seeks to embrace prefabricated building construction while effectively managing costs. By leveraging biotechnological advancements in construction materials and techniques, informed by BIM technology, we stand at the precipice of a future where sustainable, cost-effective, and biotechnology-infused building practices are the norm. [ABSTRACT FROM AUTHOR]

The estimation of construction costs for shield tunneling projects is typically based on a standard quota, which fails to consider the variation of geological parameters and often results in significant differences in unit cost. To address this issue, we propose a novel model based on a random forest machine learning procedure for analyzing the construction cost of shield tunnelling in complex geological conditions. We focus specifically on the unit consumption of grease, grouting, labor, water, and electricity. Using a dataset of geotechnical parameters and consumption quantities from a shield tunneling project, we employ KNN and correlation analysis to reduce the input dataset dimension from 17 to 6 for improved model accuracy and efficiency. Our proposed approach is applied to a shield tunneling project, with results showing that the compressive strength of geomaterial is the most influential parameter for grease, labor, water, and electricity, while it is the second most influential for grouting quantity. Based on these findings, we calculate the unit consumption and cost of the tunnelling project, which we classify into three geological categories: soil, soft rock, and hard rock. Comparing our results to the standard quota value, it is found that the unit cost of shield tunneling in soil is slightly lower (6%), while that in soft rock is very close to the standard value. However, the cost in the hard rock region is significantly greater (38%), which cannot be ignored in project budgeting. Ultimately, our results support the use of compressive strength as a classification index for shield tunneling in complex geological conditions, representing a valuable contribution to the field of tunneling cost prediction. [ABSTRACT FROM AUTHOR]

Background and Aims For a comprehensive understanding of the mechanisms of changing plant photosynthetic capacity during plant evolutionary history, knowledge of leaf gas exchange and optical properties are essential, both of which relate strongly to mesophyll anatomy. Although ferns are suitable for investigating the evolutionary history of photosynthetic capacity, comprehensive research of fern species has yet to be undertaken in this regard. Methods We investigated leaf optical properties, gas exchange and mesophyll anatomy of fern species with a wide range of divergence time, using 66 ferns from natural habitats and eight glasshouse-grown ferns. We used a spectroradiometer and an integrating sphere to measure light absorptance and reflectance by the leaves. Key Results The more newly divergent fern species had a thicker mesophyll, a larger surface area of chloroplasts facing the intercellular airspaces (S c), thicker cell walls and large light absorptance. Although no trend with divergence time was obtained in leaf photosynthetic capacity on a leaf-area basis, when the traits were expressed on a mesophyll-thickness basis, trends in leaf photosynthetic capacity became apparent. On a mesophyll-thickness basis, the more newly divergent species had a low maximum photosynthesis rate, accompanied by a low S c. Conclusions We found a strong link between light capture, mesophyll anatomy and photosynthesis rate in fern species for the first time. The thick mesophyll of the more newly divergent ferns does not necessarily relate to the high photosynthetic capacity on a leaf-area basis. Rather, the thick mesophyll accompanied by thick cell walls allowed the ferns to adapt to a wider range of environments through increasing leaf toughness, which would contribute to the diversification of fern species. [ABSTRACT FROM AUTHOR]

Copyright of Builder (1896-0642) is the property of PWB MEDIA Zdzieblowski sp.j. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this present article, a systematical research and analysis is newly introduced on traditional investment estimation of nuclear power plants, where 2/3 of the first core nuclear fuel cost is incorporated into construction cost on account of historical factors, which is considered unreasonable and inappropriate. In view of facts, the cost should be distinguished into operating cost according to “Two-Stage Treatment Scheme”. It can be manifested that decreasing construction cost as well as increasing economic benefit will be reached by including 2/3 of the first core nuclear fuel cost into operating cost.

Copyright of PARC Pesquisa em Arquitetura e Construção is the property of Universidade Estadual de Campinas - Portal de Periodicos Eletronicos Cientificos and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

While energy prices have increased substantially recently, we also observe that renovation prices have increased twofold wihtin last decade. This could seriously affect the motivation to invest in building renovations because of negative yield and in turn jeopardise the carbon reduction plans. Therefore, we attempt to show some evidence of renovation cost dynamics based on sample of 112 apartment buildings which received state support for deep energy renovations during 2010 and 2017. We found that investments started out very cautiously in 2010. Eventually renovations were embraced as rational investment and renovations ambitions grow with prices. Construction price index grow during the study period 20 percent and renovations over two times. We could not confirm the popular hypothesis that demand pressure will also increase prices. However, most significant increase of renovation price was due to the additional construction works and quality improvement. This was especially evident after the redesign of grant rules in 2015 which allowed more indirect construction works to be eligible for the subsidy. [ABSTRACT FROM AUTHOR]

The objective of this research study is to formulate and develop an automated multiobjective optimization model for modular construction projects. The model is designed to support planners of modular construction in generating optimal floor plan designs that minimize the total cost of modular construction projects while maximizing their floor plan functional performance. A case study of a modular construction project was analyzed to highlight the use of the model and demonstrate its capabilities. The results of the case study analysis illustrate the original contributions of the model in automating the generation of optimal trade-offs between the two aforementioned important objectives of modular construction projects by optimizing the holistic impact of floor plan modularization, module finishing level, and module transportation planning decisions. The results also confirm the novel capabilities of the model in identifying for each prefabricated module an optimal (1) width and length; (2) finishing level; and (3) truck assignment, delivery day, and location and orientation on the assigned truck. [ABSTRACT FROM AUTHOR]