Cost Estimation Research Articles

Sustainable utilization of sugarcane bagasse for wood-based panels: A promising approach for waste management in Egypt

One method to sustainable development is to reduce waste generation and recycle it in a way that contributes to the economic, social, and environmental goals of sustainable development. The study focuses on particle board production from Sugar Cane Bagasse (SCB). Various fiber-matrix combinations were used to create composites using SCB and epoxy resin matrix. Mechanical testing and water absorbance tests were employed to assess the effects of the epoxy content (0–25 wt%). The outcomes demonstrated that the optimal tensile strength of bio composites was attained by the 25 wt% of epoxy with SCB (18.49 Mpa). The water resistance duration property was improved in this study to reach 120 hours by mixing SCB with 25% epoxy. The results show that sugarcane bagasse-epoxy composites demonstrated acceptable mechanical and multifunctional properties. This portrays the effectiveness of Sugarcane bagasse and it a potential competitor to wood-based structural panels. The study uses the open LCA program to conduct a cradle-to-gate life cycle assessment of binder-less particleboard from sugarcane waste manufacturing. Additionally, the study provided an overview of the cost estimation involved in utilizing bagasse as a substitute for MDF particle boards. Furthermore, the influence of pressing temperature on the properties of the particle boards is investigated.

Applying Building Information Modelling (BIM) Technology in Pre-Tender Cost Estimation of Construction Projects: A Case Study in Iran

The pre-tender cost estimation serves as the foundation for determining the project cost in the early stages and is crucial for all parties involved in the tendering process. It is expected to be highly accurate. However, industry surveys have advocated that in the Iranian construction industry, the pre-tender estimated costs of construction projects are not sufficiently accurate during the bidding stage. Building information modelling (BIM) technology is a modern digital tool deployed in the construction industry that has seen substantial growth of application. It employs useful tools in different sectors and has extended its involvement in various stages of the project lifecycle, including feasibility studies, planning, design, construction, and operation and maintenance. This study examines and assesses the status of BIM development in Iran for its applicability in the pre-tender cost estimating process in construction projects. The findings of this study indicate that while there is a limited number of Iranian building projects’ BIM models that possess the required quality and capabilities for BIM-based cost estimation, the results obtained are more precise when compared to existing manual methods. Additionally, utilising BIM-based cost estimation significantly reduces the time required for this process by enhancing the speed of cost estimation operations. An innovative evaluation framework for assessing the quality and functionalities of BIM models is presented, which improves the accuracy of cost estimation before the bidding process in Iran’s construction industry. The research findings demonstrate the enhanced accuracy and effectiveness of BIM as compared to conventional approaches. It also suggests the incorporation of AI technology for model evaluation, which has the potential to become an established global industry standard.

Vehicle Damage Analysis

Abstract: Vehicle damage, resulting from accidents, environmental factors, and everyday use, presents a ubiquitous challenge to vehicle owners and the broader automotive industry. An efficient and accurate vehicle damage assessment is essential for insurance claims, resale valuations, and overall vehicle safety. However, the traditional damage assessment methods often involve human subjectivity and can be time-consuming, leading to inefficiencies and customer dissatisfaction. This abstract explores the need for modernizing and automating the vehicle damage assessment process. Vehicle damage analysis plays a crucial role in accident reconstruction, insurance claims, safety assessment, and legal proceedings. This abstract provides an overview of the key aspects of vehicle damage analysis, including the methods and tools used, as well as its broader implications. Vehicle damage analysis involves the systematic examination and assessment of damage sustained by vehicles involved in accidents, incidents, or natural disasters. This process is essential for determining the cause and severity of the damage, estimating repair costs, and establishing liability in legal cases. This abstract highlights the multifaceted nature of the problem, encompassing the need for accurate and efficient damage detection, cost estimation, integration with insurance systems, and improved customer experiences. Addressing these challenges requires the application of advanced technologies such as computer vision, artificial intelligence, and machine learning, as well as a commitment to data security, privacy, and regulatory compliance.

Planning, Designing and Estimation of G+2 Residential Building using Autocad and STAAD PRO

Abstract: This paper presents a comprehensive methodology for the planning, design, and estimation of a G+2 residential building utilizing AutoCAD for architectural drafting and STAAD.Pro for structural analysis and design. The process involves several key stages including site analysis, conceptual design, detailed architectural planning, structural analysis and design, and cost estimation. The initial phase involves site analysis to understand site constraints, orientation, and surroundings, followed by conceptual design to establish the building layout and spatial arrangement. AutoCAD is employed for creating detailed architectural plans, including floor plans, elevations, and sections, ensuring compliance with local building codes and regulations. Subsequently, structural analysis and design are performed using STAAD.Pro, incorporating factors such as loads, material properties, and structural elements. The software facilitates the analysis of various structural systems and helps optimize the design for efficiency and safety. Finally, the estimation phase involves quantifying materials and labor costs based on the finalized architectural and structural designs. This includes itemized lists of materials such as concrete, steel, bricks, and finishing materials, as well as labor costs for construction activities. The proposed methodology integrates AutoCAD and STAAD.Pro to streamline the planning, design, and estimation process for G+2 residential buildings, offering efficiency, accuracy, and adherence to regulatory requirements. The approach presented in this paper serves as a valuable guide for architects, engineers, and construction professionals involved in similar projects

Cost Monitoring and Management Algorithms in the Construction Process of Building Projects

Construction project cost estimation is an essential activity in construction-based fields; it offers a valuable source for the project feasibility studies and the design plan comparisons, and the accuracy can directly influence project investment decisions. A successful implementation of project cost forecasts makes it easier to control and manage project costs. Therefore, this paper presents the Ladybug Reptile Search Algorithm_ Deep Maxout Network (LRSA_DMN) (LRSA_DMN) to estimate construction costs. Additionally, LRSA is included to correct DMN weights. Furthermore, Bootstrap scheme is used to augment data and use this data to input the DMN for cost estimation. In addition, features are selected using a Weighted Correlation Coefficient to choose the best features for further processing. The test results show that LRSA_DMN performs better regarding MSE, RMSE, and Accuracy by 0.092, 0.371, and 0.907, respectively

The potential role of airborne and floating wind in the North Sea region

Abstract Novel wind technologies, in particular airborne wind energy (AWE) and floating offshore wind turbines, have the potential to unlock untapped wind resources and contribute to power system stability in unique ways. So far, the techno-economic potential of both technologies has only been investigated at a small scale, whereas the most significant benefits will likely play out on a system scale. Given the urgency of the energy transition, the possible contribution of these novel technologies should be addressed. Therefore, we investigate the main system-level trade-offs in integrating AWE systems and floating wind turbines into a highly renewable future energy system. To do so, we develop a modelling workflow that integrates wind resource assessment and future cost and performance estimations into a large-scale energy system model, which finds cost-optimal system designs that are operationally feasible with hourly temporal resolution across ten countries in the North Sea region. Acknowledging the uncertainty on AWE systems’ future costs and performance and floating wind turbines, we examine a broad range of cost and technology development scenarios and identify which insights are consistent across different possible futures. We find that onshore AWE outperforms conventional onshore wind regarding system-wide benefits due to higher wind resource availability and distinctive hourly generation profiles, which are sometimes complementary to conventional onshore turbines. The achievable power density per ground surface area is the main limiting factor in large-scale onshore AWE deployment. Offshore AWE, in contrast, provides system benefits similar to those of offshore wind alternatives. Therefore, deployment is primarily driven by cost competitiveness. Floating wind turbines achieve higher performance than conventional wind turbines, so they can cost more and remain competitive. AWE, in particular, might be able to play a significant role in a climate-neutral European energy supply and thus warrants further study.

3D printing feasibility of a controlled dynamization device for external circular fixation

Aimto assess the small-scale 3D printing feasibility and cost estimation of a device for controlled dynamization. Materials and MethodThe two-part device previously developed by our research group was printed with a carbon fiber-reinforced nylon filament (Gen3 CarbonX™ PA6+CF, 3DXTECH Additive Manufacturing) by a professional 3D printer (FUNMAT HT, Intamsys). Electricity, material, and labor costs for production in a Brazilian city in the Santa Catarina state were calculated. ResultsThe devices for controlled dynamization were successfully printed in accordance with the planned design and dimensions. Six out of 38 printed devices presented defects in the bolt hole and were discarded. The average printing time per device was 1.9 h. The average electricity, material, and labor costs per printed device were respectively US$0.71, US$13.55, and US$3.04. The total production cost per device reaches approximately US$20 by adding the average cost of defective devices (15 %). Conclusion3D printing of the controlled dynamization device is feasible and its cost seems affordable to most healthcare services, which could optimize the consolidation of diaphyseal fractures and reduce treatment time for patients.

A Regression Model for Predicting the Cost of Laterite- Quarry Dust Cement Blocks

This is part of a study investigating the structural properties of blocks produced from using different combination of laterite and quarry dust as full replacement for conventional river sand as aggregate. The use of alternative materials in blocks production and other concrete works is becoming increasingly popular across most developing countries. This work developed a mathematical model using Osadebe Regression Theory (ORT) for the prediction of cost/cum of laterite- quarry dust cement block. The lack of fit of the model was carried out using statistical tool (excel) and found to be adequate. The model will be very useful especially for commercial blocks producers and built experts in Nigeria and sub-Sahara African countries for construction cost estimation and profit projection.

Artificial intelligence-based pre-conception stage construction budget decision-making model and tool for residential buildings

PurposeDeveloping a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.Design/methodology/approachDue to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).FindingsThis research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.Research limitations/implicationsThe current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.Originality/valueThis research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.

Economical Group Chain Sampling Plans for Weibull Distribution Using Bayesian Approach

The paper focuses on the economic design of group chain sampling plans (GChSP) for the Weibull distribution using Bayesian methodology. The GChSP is a technique to accept or reject a product based on a sample from a lot. The study addresses situations where destructive testing is costly and utilizes the Bayesian approach to make informed decisions. The research outlines the methodology of developing GChSP including the stages of construction, performance evaluation, and cost estimation. The study compares the proposed plans with an existing one and demonstrates that the Bayesian approach generally yields lower costs. We will provide tables, figures, and calculations related to various aspects of the proposed plans and their comparison with existing methods.

How pressure affects costs of power conversion machinery in compressed air energy storage; Part I: Compressors and expanders

This study addresses a critical economic aspect in compressed air energy storage that has not been discussed much in existing literature: the impact of operating pressure on machinery capital cots. It aims to answer whether the cost per unit of power for power conversion systems changes with the maximum storage pressure. Considering that higher storage pressures are associated with greater energy density, enhanced energy storage capabilities and improved system efficiency. This paper helps clarify uncertainties in initial cost estimations for power-generation plants. Effects of operating pressure on the components and overall sizes and consequently costs of power conversion machinery are individually investigated in two parts. Part I encompasses the compressor and expanders, and part II comprehensively discusses the effects of the operating pressure on the costs of heat exchangers. The analysis employs a conceptual engineering approach, revealing that higher intake pressure reduces overall compressor/expander size, leading to cost savings. Additionally, increasing the number of compression stages for higher storage pressures enhances exergy storage cost-effectiveness. To establish an advanced adiabatic CAES plant with a storage pressure of 200 bar instead of 50 bar, there is potential for a 6 % reduction in $/kW expenditure.

Batch to Continuous: From Laboratory Recycle Trickle Bed Test Reactor Data to Full-Scale Plant Preliminary Design—A Case Study Based on the Hydrogenation of Resorcinol

The fine chemical and pharmaceutical sectors are starting to advocate for the use of flow chemistry due to reasons such as the environment, health and safety, efficiency, cost saving, and regulatory compliance. The use of a trickle bed or fixed bed system could replace a batch autoclave typically used for hydrogenation reactions. However, there are few studies that detail the process from laboratory proof of concept through design to commercial realization. This study, using the production of 1,3-cyclohexanedione from the catalytic hydrogenation of resorcinol as a case study, demonstrates how the laboratory-scale recycle trickle bed can be used for catalyst screening and selection. Further, design data are generated by operation over a range of design superficial velocities and operating pressures that are used to derive a design correlation that is then used to specify a single stream plant at a level of definition consistent with a Preliminary Design for capital cost estimation. Finally, the further actions required in terms of data generation to increase the level of definition and confidence to a sanction grade or final design are discussed.

Main cost estimation models in projects used by banks and fintechs in Brazil

Main cost estimation models in projects used by banks and fintechs in Brazil

Illuminating the path to sustainable rice harvesting: A solar-based paddy harvester

Paddy is the most important agricultural crop product in Bangladesh since it is widely used in daily life as a main meal intake. Currently, traditional manual harvesting process is widely used to harvest paddy grains in Bangladesh. However, manual harvesting is laborious, time-consuming, and costly. Utilization of modern machineries such as combined paddy harvester machines can increase grain productivity while reducing significantly the number of labour-hours. Combine rice harvesters possess traditional diesel engine that burns gasoline fuels i.e. diesel. The overriding concern of this diesel oil combustion is the production of greenhouse gases that adversely affect the environment. In addition, reports suggest that oil reserve might be depleted to zero in next 40 years. This manuscript presents a comprehensive review on the paddy grain production statistics, the pros and cons of traditional rice crop harvesting processes, and the historical development of mechanization in the agricultural sectors of Bangladesh. It also presents a novel design route to convert commercial diesel-driven combine paddy harvester machine to solar energy-operated combine rice harvester machine with its required accessories. In particular, a new design concept with a remarkable cost estimation process of solar-powered harvesters is proposed. A field study has been conducted in Sonagazi Upazilla under Feni district of Bangladesh to evaluate the potential use of the proposed solar-powered harvester machine. Our study suggests that solar-driven rice harvester is a promising concept in the paddy crop harvesting process and it will reduce the overall costs for short and long-term uses compared to estimated costs of commercial diesel-driven harvesters. To combat the scarcity of gasoline in the future, it is an obvious choice to bring a paradigm shift in solar energy harvesting and its utilization in the agricultural sectors of Bangladesh. It is expected that the proposed solar-driven rice harvester concepts with their in-depth analysis (including costs) would be able to draw attention to business entrepreneurs and investors who will be excited to initiate a program to manufacture solar-powered rice harvester machines in practice.

Comparison of Safety and Cost of Cross-match Tests and Antibody Screening before Blood Transfusion in Patients Undergoing Orthopedic Surgery: A Cross-sectional Study

Introduction: Planning for blood request and balancing supply and demand are still contentious issues in hospitals and blood transfusion centers. This study aimed to compare the safety and cost-effectiveness of two pre-transfusion tests for red blood cells in patients undergoing orthopedic surgery. Method: In this descriptive-analytical cross-sectional study, patients undergoing surgery who required blood transfusion during the operation were included via convenience sampling method. The necessary information for this study was collected using a researcher-made checklist consisting of two parts: background information, blood bag information, and information related to potential complications. The cost estimation of the tests was also extracted using the records. The findings were analyzed using descriptive and inferential statistics with SPSS software (version 18), and the significance level was set at 0.05. Results: A total of 212 patients with a mean age of 44.98 years were included in the study. Cross-match was performed for 108 patients (50.9%), and antibody screening test was used for 104 patients (49.1%). Regarding complications after blood transfusion, at least one sign was observed in 12 patients (5.7%). Jaundice, fever, and itching were the most common symptoms. The cross-match to transfusion (C/T) ratio was higher than international standard criteria (5.25 vs. 2.5). Conclusion: The results of this study demonstrated that the safety and accuracy of both pre-transfusion methods were similar, and no significant difference was observed. However, the cross-match test was more cost-effective compared to the match antibody screening test.

Examining factors influencing delay of construction projects in Tanzania: A case of Gerenzani- Mbagala BRT road

This study examined the factors influencing the delay in the Gerezani-Mbagala BRT road construction project in Dar es Salaam. Specifically, the study examined client and consultant related factors that resulted in the delay in road construction in the study area. The study was guided by resource-based theory. The study used a descriptive research design and adopted purposive and stratified sampling techniques to select 50 members of the sample of the study. On the other hand, a questionnaire and interview tools were used to solicit information from the respondents. Additionally, the appropriate model for data analysis was the logistic regression model. The findings showed that limited funds provided by the client and poor communication had statistically significant associations with project delays in the Gerezani-Mbagala BRT construction project. Also, the results revealed that a delay in approving major changes and a lack of experience among the consultants had a statistically significant influence on the delays in the Gerezani-MbagalaBRT construction project. The study further revealed that delays in payments from the client and rework due to errors made by the construction personnel were significantly associated with project delays in the Gerezani-Mbagala BRT construction project. The study concludes that client-related and consultantrelated significantly caused the delays in the Gerezani-Mbagala BRT construction project. The study recommends that the client should provide enough time to produce project briefs and other feasibility studies, as this would lessen the impact of inaccurate project cost estimation, unforeseen site circumstances, and slow site clearances on the delivery of the construction project.

The Impact of Building Information Modeling Technology on Cost Management of Civil Engineering Projects: A Case Study of the Mombasa Port Area Development Project

Introduction: This study examines the impact of building information modeling on the cost management of engineering projects, focusing specifically on the Mombasa Port Area Development Project. The objective of this research is to determine the mechanisms through which building information modeling facilitates stakeholder collaboration, reduces construction-related expenses, and enhances the precision of cost estimation. Furthermore, this study investigates barriers to execution, assesses the impact on the project’s transparency, and suggests approaches to maximize resource utilization. Methodology: This study employed a mixed-method research design comprising document reviews and surveys. During the document review, credible databases including ScienceDirect and Institute of Electrical and Electronics Engineers Xplore were explored. The survey included 69 professionals, among which were project managers, cost estimators, and building information modeling administrators. The mixed-methods approach prioritized ethical considerations and the statistical Package for the Social Sciences and Microsoft Excel were used in the analysis. Results: The results show that building information modeling is a valuable system for organizations looking to reduce project costs. The results note that the technology improves cost estimation accuracy, facilitates the identification of cost-related risks, and promotes collaborative decision-making. Conclusions: Building information modeling is an effective cost-estimating technology that positively impacts additional project aspects such as decision-making, collaboration, performance, and delivery time. Therefore, the Mombasa Port Area Development Project should inspire other stakeholders in engineering and construction to embrace building information modeling.

Design of small-scale hybrid energy systems taking into account generation and demand uncertainties

The adoption of energy systems powered by renewable sources requires substantial economic investments. Hence, selecting system components of an appropriate size becomes a critical step, which is significantly influenced by their distinct characteristics. Furthermore, the availability of renewable energy varies over time, and estimating this availability introduces considerable uncertainty. In this paper, we present a technique for the optimal design of hybrid energy systems that accounts for the uncertainty associated with resource estimation. Our method is based on stochastic programming theory and employs a surrogate model to estimate battery lifespan using a feedforward neural network (FFNN). The optimization analysis for system design was conducted using a genetic algorithm (GA) and the poplar optimization algorithm (POA). We assessed the effectiveness of the proposed technique through a hypothetical case study. The introduction of a surrogate model, based on an FFNN, resulted in an approximation error of 9.6 % for cost estimation and 20.6 % for battery lifespan estimation. The probabilistic design indicates an energy system cost that is 25.7 % higher than that obtained using a deterministic approach. Both the GA and POA achieved solutions that likely represent the global optimum.

Scaling law method for cost estimation of Indonesia’s first HTGR (PeLUIt-40) and its implementation strategy

The Indonesian government has planned to build its first high-temperature gas reactor (HTGR), PeLUIt-40. A lack of estimates for PeLUIt-40′s costs hamper planning and implementation. Therefore, research is needed to determine the cost of PeLUIt-40 and develop an implementation strategy. Cost estimation is done using the scaling law method. The estimated cost for FOAK PeLUIt-40 is 215.83 million USD for investment, 14.20 million USD for annual O&M, 5.32 million USD for annual fuel and transportation, and 6.16 million USD for decommissioning costs. Using FOAK PeLUIt-40 for electricity generation produces an LCOE in the range of 37.82 to 11 cents USD/kWh. The LCOE value is lower than the diesel power plant's generation cost, which is 39.86 cents USD/kWh and produces an NPV of 15.94 million USD. Some implementation strategies can be made to make PeLUIt-40 more economically feasible, i.e., increasing NOAK from PeLUIt-40, providing incentives by the government, and adjusting PeLUIt-40′s technology grade. The results indicate that the strategies significantly impact the feasibility of PeLUIt-40 implementation. A sensitivity analysis was also conducted by changing the portion of indirect costs of FOAK HTGR to 77% of the direct costs and changing the discount rate from 10% to 3%. The discount rate change makes the LCOE of PeLUIt-40 range from 37.82 to 11 cents USD/kWh, NPV from 15.94 million USD to 871.45 million USD, and IRR from 10.75% to 37.87%.

Artificial Intelligence-Centric Low-Enthalpy Geothermal Field Development Planning

Low-enthalpy geothermal energy can make a major contribution towards reducing CO2 emissions. However, the development of geothermal reservoirs is costly and time intensive. In particular, high capital expenditures, data acquisition costs, and long periods of time from identifying a geothermal resource to geothermal heat extraction make geothermal field developments challenging. Conventional geothermal field development planning follows a linear approach starting with numerical model calibrations of the existing subsurface data, simulations of forecasts for geothermal heat production, and cost estimations. Next, data acquisition actions are evaluated and performed, and then the models are changed by integrating the new data before being finally used for forecasting and economics. There are several challenges when using this approach and the duration of model rebuilding with the availability of new data is time consuming. Furthermore, the approach does not address sequential decision making under uncertainty as it focuses on individual data acquisition actions. An artificial intelligence (AI)-centric approach to field development planning substantially improves cycle times and the expected rewards from geothermal projects. The reason for this is that various methods such as machine learning in data conditioning and distance-based generalized sensitivity analysis assess the uncertainty and quantify its potential impact on the final value. The use of AI for sequential decision making under uncertainty results in an optimized data acquisition strategy, a recommendation of a specific development scenario, or advice against further investment. This approach is illustrated by applying AI-centric geothermal field development planning to an Austrian low-enthalpy geothermal case. The results show an increase in the expected value of over 27% and a reduction in data acquisition costs by more than 35% when compared with conventional field development planning strategies. Furthermore, the results are used in systematic trade-off assessments of various key performance indicators.