Preliminary Cost Estimates Research Articles

System-based ship design of a deep-sea mining vessel

ABSTRACT This paper applies the system-based ship design (SBSD) method to the conceptual design of a deep-sea mining vessel. The anticipated mine site is situated on the Norwegian extended continental shelf, where three production scenarios are assessed. The design process is documented, including a problem statement, mission description, functions, space requirements of various sub-systems, stability calculations, and preliminary cost estimates. A combination of data from existing technologies and estimates is used to set the relevant parameters, and the applicability of SBSD in the conceptual design of novel vessels is discussed. We find that when data is lacking in conceptual ship design, inspiration can be found from similar systems in established maritime industries, such as the offshore oil and gas industry.

From quarry by-products to a zeolites-based Zn fertilizer with increased resistance to rain leaching

In the face of an extensive literature on the use of zeolites for the removal of metals from water for environmental purposes, it is seldom considered that some metals are also essential nutrients for life and that zeolites could be profitably used to dose their release. Among these, Zn is a key micronutrient, and when its demand by crop is not fully balanced by adequate accessibility, fertilization must be provided using Zn salts that can be, however, easily leached and partly wasted in the environment.In an attempt to solve this critical problem, a new controlled-release formulation of Zn using zeolite-containing geomaterials was designed, prepared, characterized, and tested by applying a sequential, multi-method approach. Different formulations were trialed, and the most effective included 30 wt% pumice by-product and 70 wt% clinoptilolite-rich zeolitized tuff, with about 20 mg/g of exchangeable Zn2+. The enrichment process reached equilibrium after about 8 h, a timing well-tuned with technology transfer. Desorption kinetic tests in a weekly acid environment revealed gradual Zn release, with about 4.28 wt% released after 6 h. When tested as a foliar fertilizer on Vitis vinifera, this formulation demonstrated superior resistance to leaching under simulated rainfall conditions compared to conventional ZnSO4·6H2O fertilizer, maintaining the initial level of Zn (130 mg/kg of dry leaves), while about 22 % of the Zn applied with ZnSO4·6H2O was loss. This outcome was plausibly due to mineral particle adhesion to leaf. Preliminary cost estimates suggest that the product designed here can be placed in the market with competitive sales prices.

Mechanised deep tunnel excavation in saturated clayey soils: a pre-design hydro-mechanically coupled method for the assessment of both spoil and face volume loss

Mechanised tunnel excavation in soils causes over-excavations, potentially leading to large amounts of spoil and settlements at ground level. An accurate estimation of over-excavations is crucial in the pre-design phase for assessing costs, determining the appropriate excavation method and choosing the muck management strategy. Currently, the estimation is based on experience and data from similar projects, but this becomes difficult when project conditions are heterogeneous. As an alternative, finite-element analyses are time-consuming and not suitable for early design stages; therefore, simplified tools are needed. In this paper, the authors present a simplified approach putting in relation face extrusion with estimated spoil mass and face volume loss. This approach, conceived for deep tunnels, is the extension to the case of mechanised tunnelling of a hydro-mechanical coupled meta-model derived from finite-element numerical analyses for tunnels in clayey soils excavated by using conventional techniques (i.e. without any use of tunnel-boring machines). The model has been validated against field data relative to a case study. The approach can be used in the early design process to identify tunnel-boring machine characteristics and provide preliminary cost estimates. In addition, during the construction phase, the method can be employed to interpret monitoring data and pre-design mitigation measures for unforeseen soil profile variations.

Developing preliminary cost estimates for foundation systems of high-rise buildings

This study provided a unique Artificial intelligence (AI) model for the cost estimation of the foundation system for high-rise buildings. First, parametric data research was conducted, including 180 examples of foundation systems for high-rise building models related to criteria such as average column spacing, floor height, slap load, overall weight, base shear, and over. In addition, moment, gravity load, seismic load, design load, various columns (mid-edge-corner), soil profile, bearing capacity, GWT, excavation depth, and foundation systems are all factors to consider (shallow and deep footing). The research encompassed column spacing (5.0, 7.0, and 9.0 m) and the number of stories from 15 to 35, with an average cost decrease of 15%. The AI model utilizes extensive machine learning algorithms to analyze structural and geotechnical parameters. It provides a predictive tool that improves decision-making in the preliminary design. The methodology includes collecting data, training models utilizing innovative neural network techniques and validating them against current cost estimation models. The findings indicate that the AI model significantly improves the accuracy of cost estimations, with a precision rate of 95%, compared to traditional methods. Moreover, the model presents a potential cost savings of up to 15%. Construction designers and decision-makers may utilize the study’s findings to increase the accuracy of foundation system selection, lower costs and construction length, and improve overall project performance. Future work may involve applying this concept to infrastructure and substructure projects and collecting more data for benchmarking and empirical study.

Agricultural damage following the recent expansion of wild boar (Sus scrofa)—farmer perceptions and preconditions

The wild boar (Sus scrofa) population has recently increased in Fennoscandia, leading to increasing interactions with humans, e.g. in the form of hunting opportunities or property damages. These experiences may shape the attitudes towards wild boars and affect their population management. We studied attitudes among 165 farmers towards wild boar by means of survey data. We also made preliminary estimates of wild boar damage costs in Finland. Our analysis revealed three attitude groups among farmers, emphasizing various perspectives: “hunting resource”, “pest-of-concern”, and “ambivalent”. The benefits, costs, feelings, and perceived wild boar population development partly explained the division. The direct annual damage costs in southeast Finland were broadly estimated to be circa 990,000€ and mitigation costs circa 350,000€. Reconciling differing opinions and goals into a widely accepted management strategy is a key precondition for sustainable coexistence. While the level of economic loss is so far not substantial, monitoring of crop damages is vital. Our results provide a valuable baseline for future evaluations, as the wild boar population is expected to grow.

Practical Exploration of Eco-Geological Survey Mapping in Qinghai–Tibet Plateau: Framework, Standard and Preliminary Cost Estimation

It is a common responsibility of all countries to protect the ecological environment and promote sustainable development. Eco-geology, which provides a basic guarantee for the rational utilization of natural resources, ecological protection and restoration, has gradually become a new hot spot of geological survey institutions in various countries. Eco-geological survey in the Qinghai–Tibet Plateau plays an important role in regional ecological protection and global change research. Setting up workable budgets symbolizes the competence of geological survey organizations in fulfilling their responsibilities, and unreliable cost estimates can cause economic and political complications. The unclear scope definition and scarcity of project information available at early stages make it hard to generate reliable preliminary cost estimates. Hence, based on historical data and the practical experience of scientists who have been engaged in eco-geological survey in the Qinghai–Tibet Plateau for a long time, this research aimed to develop a cost estimation model using statistical learning methods for geological survey organizations to forecast preliminary costs during the early stages of an eco-geological survey project to fulfill different cost control and managerial functions. This research makes full use of modern remote sensing technology and reasonably arranges the input of ground survey, drilling, geophysical exploration and other workloads, supplemented by the existing ecological monitoring station data. It comprehensively sorts out the work means and workload of field and office work needed to complete the map work, and it puts forward a consistent index system and preliminary cost calculation framework. On this basis, based on the principle of matching quantity and price, a reasonable budget standard or market price is selected to calculate the budget estimate of a single map sheet, and then, through the regional adjustment coefficient, the budget estimate of a map sheet covering the whole Qinghai–Tibet Plateau is extended and calculated. The budget estimate will provide effective support for the declaration of eco-geological survey projects in the Qinghai–Tibet Plateau and the calculation of the scale of project funds so as to ensure the smooth development of eco-geological surveying in the region.

Preliminary cost forecasting and optimization for the construction of steel pedestrian bridges in Egypt

PurposeThis research aims to create a methodology that integrates optimization techniques into preliminary cost estimates and predicts the impacts of design alternatives of steel pedestrian bridges (SPBs). The cost estimation process uses two main parameters, but the main goal is to create a cost estimation model.Design/methodology/approachThis study explores a flexible model design that uses computing capabilities for decision-making. Using cost optimization techniques, the model can select an optimal pedestrian bridge system based on multiple criteria that may change independently. This research focuses on four types of SPB systems prevalent in Egypt and worldwide. The study also suggests developing a computerized cost and weight optimization model that enables decision-makers to select the optimal system for SPBs in keeping up with the criteria established for that system.FindingsIn this paper, the authors developed an optimization model for cost estimates of SPBs. The model considers two main parameters: weight and cost. The main contribution of this study based on a parametric study is to propose an approach that enables structural engineers and designers to select the optimum system for SPBs.Practical implicationsThe implications of this research from a practical perspective are that the study outlines a feasible approach to develop a computerized model that utilizes the capabilities of computing for quick cost optimization that enables decision-makers to select the optimal system for four common SPBs based on multiple criteria that may change independently and in concert with cost optimization during the preliminary design stage.Social implicationsThe model can choose an optimal system for SPBs based on multiple criteria that may change independently and in concert with cost optimization. The resulting optimization model can forecast the optimum cost of the SPBs for different structural spans and road spans based on local unit costs of materials cost of steel structures, fabrication, erection and painting works.Originality/valueThe authors developed a computerized model that uses spreadsheet software's capabilities for cost optimization, enabling decision-makers to select the optimal system for SPBs meeting the criteria established for such a system. Based on structural characteristics and material unit costs, this study shows that using the optimization model for estimating the total direct cost of SPB systems, the project cost can be accurately predicted based on the conceptual design status, and positive prediction outcomes are achieved.

Comparative Study of Machine Learning Algorithms for Early Cost Estimation of Building Projects in Nepal

Construction cost estimation is crucial to a project’s success, but because of the many variables that impact it, it is challenging to make an accurate prediction. Traditional methods are being used for preliminary cost estimation in the construction industry of Nepal. There still exists the problem of cost overrun, and time delay due to incorrect cost budgeting. This study aims to analyze a modern method of preliminary cost estimation in Nepal to prove its efficiency over the traditional method. In this work models such as Linear Regressor, Decision Tree Method, Random Forest method, Artificial Neural Networks, Support Vector Machine, Boost method, Extra tree method, Voting Regression, and Stacking method are used. Regarding the datasets, the buildings that were used are Educational Building, Commercial Building, Hospital Building, Residential Building, Public Building, Official Building, and Hotel Building having 0 to 2 basements ranging above 1 crore. The input features were taken from the literature review, and validated by expert opinion, and after successfully conducting pilot testing, the survey questionnaire was distributed among contractors and consultants. Data preprocessing was done and training and testing data sets were developed. The model was developed for nine algorithms. Mean absolute error (MAE), Mean square error (MSE), Root mean square error (RMSE), and R square value are used as evaluation metrics. In the evaluation of various regression models, three stand out as the most promising for predicting the target variable. The Decision Tree model exhibited remarkable performance with an MSE of 0.088575, an MAE of 0.104625, an RMSE of 0.297615, and an R2 of 0.876170. Similarly, the Extra Tree model closely followed with an MSE of 0.088601, an MAE of 0.102909, an RMSE of 0.297659, and an R2 of 0.876134. The Voting Model with an MSE of 0.105035, an MAE of 0.222807, an RMSE of 0.324091, and an R2 of 0.853159. This study also opens the path for the exploration of other models and motivate to follow the trends of machine learning in the present era.

Preliminary Cost Estimation of Highway Projects Using Statistical Learning Methods

Setting up workable budgets symbolizes the competence of state highway agencies (SHAs) in fulfilling their responsibilities, and unreliable cost estimates can cause economic and political complications. The unclear scope definition and scarcity of project information available at early stages make it hard to generate reliable preliminary estimates. Hence, based on the 1,249 projects retrieved from the Florida Department of Transportation (FDOT) database, this research aimed to develop a cost estimation model using statistical learning methods for SHAs to forecast preliminary costs during the early stages of a transportation project to fulfill different cost control and managerial functions. However, the currently used methods have serious limitations. This study introduced alternative statistical learning approaches to the currently most used methods: least absolute shrinkage and selection operator (LASSO) and general regression neural network (GRNN). LASSO regression, for instance, has proved in other areas of science to be remarkably better in terms of variable selection, interpretability, and numerical stability. In addition, this study also accounted for economic factors in model development because economic conditions are influential on highway construction costs but have received limited attention. Using the same dataset, LASSO and GRNN models were developed, and then their performances were evaluated based on a set of criteria, e.g., the mean absolute error and mean absolute percentage error. In comparison to the current practice with state DOTs, this research contributes to the body of knowledge by introducing a series of objective modeling approaches that can prevent human errors, requiring no substantial experience in preliminary estimating. Besides the introduction of statistical learning methods, this study took economic indicators into account when developing the models because they are important factors but have been ignored in previous studies. In addition, these statistical learning methods can produce reliable estimates in a much faster and more consistent fashion, which is critical, particularly considering the massive workload faced by most SHAs and the allowable time to make a preliminary estimate.

Aerocapture: Enabling Small Spacecraft Direct Access to Low-Circular Orbits for Planetary Constellations

Small satellite constellations in multiple-inclination, low-circular orbits around Mars and Venus have the potential to perform a range of high-value science investigations within cost-constrained missions. A major challenge for small satellites is that they require large ΔV to enter low-circular orbits, which can drive up both spacecraft mass and cost. Compared to chemical propulsion, which requires large amounts of propellant, and electric propulsion, which requires large solar arrays and comes with long flight times, aerocapture enables direct access to low-circular orbits at Mars and Venus with minimal ΔV. The study shows how drag-modulation aerocapture, when combined with small B-plane targeting maneuvers, allows the delivery of multiple small satellites to various-inclination, low-circular orbits to establish a constellation. Preliminary cost estimates indicate that by reducing the required ΔV for orbit insertion, aerocapture can potentially reduce the cost of a small satellite going to a low-circular Mars orbit compared to propulsive insertion. The ability of low-cost spacecraft to enter planetary orbits will enable a new paradigm of interplanetary missions using small dedicated launch vehicles and planetary constellations at Mars and Venus.

A Comparison of Contract Law Between Pakistan and China Regarding the China-Pakistan Economic Corridor (CPEC)

In 2015, China and Pakistan signed up for the China-Pakistan Economic Corridor (CPEC) project. The two countries’ economies will benefit greatly from this massive initiative, which will also provide them with easier access to global markets. The preliminary cost estimate for the project was $46 billion, and its expected lifespan was fifteen years. It's a long and expensive project, so there are a lot of things that could go wrong. The present study aimed to review the background of CPEC and FTA, and compare the contract law of both two countries.

Cost and Material Quantities Prediction Models for the Construction of Underground Metro Stations

This paper addresses the gap in the scientific literature regarding construction cost estimates for the construction of underground metro stations. It provides preliminary cost estimation models using linear regression for use by the Greek underground metro public transport authority for planning future extensions to the Athens and Thessaloniki networks. At the same time, it contributes to the body of knowledge by proposing material quantity prediction models and presents a two-stage preliminary cost estimation model for the construction of civil engineering works of underground metro stations. Stage one uses the construction cost budgets of six metro stations in Greece to develop a multilinear regression equation for the prediction of the overall cost for construction of civil engineering works; stage two provides estimates of material quantities using linear regression, key quantity ratios, and artificial neural networks. The data analyzed are from the prior measurements of quantities for the construction of the Chaidari to Piraeus extension of the Athens Metro Line 3. After comparing the actual values of costs and quantities with the corresponding predictions, acceptable discrepancies are observed. All models provide estimates within ±25% discrepancies, which are acceptable at the conceptual planning phase in order to initiate project funding quests.

Parametric modeling of radio engineering devices at the stage of system design

System design provides for solving the task of comparative analysis and choosing the best option for constructing a radio engineering device. Its main result should be a structural diagram of the product and an assessment of the effectiveness of its implementation. The number of possible structural options for their implementation is limited when designing radio engineering devices of a certain class, so the search for the best option is mainly determined by the costs of its implementation, provided that the requirements of the technical specifications are fully satisfied. The main way to reduce the costs of developing devices is to use high-availability modules as much as possible in the designed product, which means electronic modules that have passed certain stages of development or have at least working design documentation. Then, the task of system design is reduced to solving the task of covering the given structures of high-availability modules, and the efficiency of its solution depends to a large extent on the number of possible alternatives. The methodology of using parametric modeling to determine the best option of construction of a radio engineering device (system) is considered. It is proposed to use an information approach to assess the degree of coincidence of the parametric models of highavailability modules with the values of parameters specified in the technical specifications while maintaining a given level of reliability. The degree of coincidence is evaluated by three criteria: informativeness, adequacy, and accuracy. It is shown that the considered methodology makes it possible to solve the parametric synthesis task of the effective structure of a radio engineering device while maintaining its specified reliability. The resulting options are highly reliable because the cost estimation is performed by real developers. As information on the actual costs for possible modifications of high-availability modules is accumulated, the preliminary cost estimation procedure can be automated.

Design and Evaluation of a High Temperature Phase Change Material Carnot Battery

In the current study, a high temperature thermal storage system with a hybrid of phase change material and graphite as the storage materials is designed and evaluated as to its applicability for use as a utility-scale Carnot battery. The design includes an externally heated liquid sodium tank, which is used as the heat transfer fluid. This is used to charge and discharge the storage system consisting of a graphite storage medium sandwiched by two phase change materials. Finally, electrical generation is by way of a supercritical carbon dioxide Brayton cycle operated at 700 °C. Detailed modelling of these designs was conducted by way of a previously validated numerical model to predict performance metrics. Using the aforementioned designs, a preliminary cost estimate was undertaken to better determine applicability. From these results, it was found that while the graphite system was the most effective at storing energy, it was also the highest cost due to the high cost of graphite. In total, 18 storage tanks containing nearly 17,400 tons of storage material were required to store the 1200 MWht required to run the sCO2 power block for 10 h. Under the study conditions, the cost of a PCM-based Carnot battery was estimated to be $476/kWhe, comparable to other storage technologies. Furthermore, it was found that if the cost of the graphite and/or steel could be reduced, the cost of the system could be reduced to $321/kWhe.

Statistical cost modelling for preliminary stage cost estimation of infrastructure projects

Reliable and accurate cost estimates are essential to construction projects. They are even more critical in infrastructure projects as they require more time, cost, and public constraints. Therefore, a better cost model is required for infrastructure projects. An extensive literature review was carried out to identify various statistical modelling techniques and models, as well as models developed using these techniques. The literature identified seven statistical modelling techniques. They are; regression analysis, Monte-Carlo simulation, support vector machine, case-based reasoning, reference class forecasting, artificial neural networks, and fuzzy logic. These techniques were all used in various cost models developed for construction projects. According to the analysis of results, neural networks and support vector machine-based models displayed better performance in their cost estimation models. However, it was found that combining several techniques into a hybrid model, for example, the neuro-fuzzy hybrid, can significantly increase these results. Thus, the reliability and accuracy of the current estimation process can be improved with these techniques. Finally, the techniques identified as having better performance can be used to develop a cost estimation model for the preliminary stage. This is because these techniques perform well even though the availability of information is lower. The results of this research are limited to the seven identified techniques and the literature used in the review.

Evaluation of the Performance of Tightly Coupled Parallel Solvers and MPI Communications in IaaS From the Public Cloud

IaaS from the public cloud is becoming a new option for organizations in need of HPC capabilities. IaaS offers greater flexibility in hardware choices and operational costs. Furthermore, IaaS enables small organizations to access resources previously reserved for organizations with larger capitals. This article uses the HPCG benchmark to assess the performance of parallel solvers, which are critical in computational engineering, and microbenchmarks to measure collective MPI operations. The benchmarks encompass IaaS from five cloud vendors (AWS, Google Cloud Platform, Azure, Oracle CIoud Infrastructure, and Packet), and two architectures, ARM and x86_64. The benchmarks cover clusters with up to 4500 cores and illustrate the benefits of higher network bandwidth when scaling up clusters. The results for some of the clusters are particularly promising as they exhibit good scalability and compare well to on-premises supercomputers. Additionally, the study includes a preliminary cost estimate based on on-demand prices for IaaS computational power and memory.

Toward Optimal Meat Pricing: Is It Time to Tax Meat Consumption?

Livestock is known to contribute significantly to climate change and to negatively impact global nitrogen cycles and biodiversity. However, there has been little research on economically efficient policies for regulating meat production and consumption. In the absence of first-best policy instruments for the livestock sector, second-best consumption taxes on meat can address multiple environmental externalities simultaneously as well as improve diet-related public health. In this article, we review the empirical evidence on the social costs of meat and examine the rationales for taxing meat consumption in high-income countries. We approach these issues from the perspective of public, behavioral, and welfare economics, focusing in particular on (1) the interaction of multiple environmental externalities of meat production and consumption, (2) “alternative protein” technologies, (3) adverse effects on human health, (4) animal welfare, and (5) distributional effects of meat taxation. We present preliminary estimates of the environmental social costs associated with meat consumption and find that meat is significantly underpriced. We conclude by identifying several directions for future research on optimal meat taxation.

Developing a Preliminary Engineering Cost Estimation Method for a Portfolio of Bridge Construction Projects in Project Planning Phase

For state departments of transportation (DOTs), estimating preliminary engineering (PE) costs over a multiple-year horizon is critical for proper planning of engineering resources and budgets to ensure timely delivery of a portfolio of bridge construction projects, which incur PE costs that are relatively higher than those required for other highway project types. However, given the uncertainties in the planning phase, it is challenging to estimate PE costs effectively and efficiently. The purpose of this study was to provide DOTs with a PE cost estimation method using different ratios of PE cost to construction cost for bridge projects by project type and size. To that end, the study tested the variability of PE costs by analyzing 628 Texas DOT bridge construction projects divided into three project types: bridge maintenance (BMN), bridge replacement (BR), and bridge widening and rehabilitation (BWR). The analysis found that the ratios of PE cost to construction cost for the BMN and BR project types were significantly different by project size, but was not conclusive for the BWR project type because of limited sample size. The validation results indicate that the proposed method is effective for planning and estimating the PE costs of a portfolio of projects. The study findings can provide DOTs with an improved PE cost estimation process for a portfolio of bridge projects during the project planning phase.

Synthesis of Practices and Tools for Cost Estimation and Cost Management for Transportation Projects

Cost escalation during project development is inevitable on transportation projects. The lack of a systematic process for establishing a baseline budget with the consideration of potential issues (risks) that negatively impact project cost throughout project development presents a major challenge for State Departments of Transportation (DOTs). The overarching objective of this paper is to identify best practices for cost estimation and management that can aid project managers and engineers throughout the project development process (PDP). To achieve the objective, this paper conducted a systematic literature review, comprehensive content analysis of cost estimation and cost management processes in other State DOTs, and interviews with transportation cost professionals. A systematic literature review identified four key areas for cost estimation and cost management, including: (1) risk analysis and contingency; (2) identification of cost escalation factors; (3) accuracy of preliminary cost estimate; (4) and constructability review. Cost estimation and control processes in Minnesota, California, Texas, Ohio, and Washington State DOTs are provided as examples of best practices for establishing reliable baseline cost estimates. This paper represents a current synthesis of state DOTs’ practices on cost estimation and cost management in the U.S.

A Theoretical Study on Reversible Solid Oxide Cells as Key Enablers of Cyclic Conversion between Electrical Energy and Fuel

Reversible solid oxide cells (rSOC) enable the efficient cyclic conversion between electrical and chemical energy in the form of fuels and chemicals, thereby providing a pathway for long-term and high-capacity energy storage. Amongst the different fuels under investigation, hydrogen, methane, and ammonia have gained immense attention as carbon-neutral energy vectors. Here we have compared the energy efficiency and the energy demand of rSOC based on these three fuels. In the fuel cell mode of operation (energy generation), two different routes have been considered for both methane and ammonia; Routes 1 and 2 involve internal reforming (in the case of methane) or cracking (in the case of ammonia) and external reforming or cracking, respectively. The use of hydrogen as fuel provides the highest round-trip efficiency (62.1%) followed by methane by Route 1 (43.4%), ammonia by Route 2 (41.1%), methane by Route 2 (40.4%), and ammonia by Route 1 (39.2%). The lower efficiency of internal ammonia cracking as opposed to its external counterpart can be attributed to the insufficient catalytic activity and stability of the state-of-the-art fuel electrode materials, which is a major hindrance to the scale-up of this technology. A preliminary cost estimate showed that the price of hydrogen, methane and ammonia produced in SOEC mode would be ~1.91, 3.63, and 0.48 $/kg, respectively. In SOFC mode, the cost of electricity generation using hydrogen, internally reformed methane, and internally cracked ammonia would be ~52.34, 46.30, and 47.11 $/MWh, respectively.