Project cost forecasting is a complex and critical process, and it is of paramount importance for the successful implementation of engineering projects. Accurately forecasting project costs can help project managers and relevant decision-makers make...
Inaccuracies in cost estimation on construction projects is a contested topic in praxis. Among the leading explanations for cost overrun (CO), factors accounting for large variances in actual cost are shown to have psychological or political roots. T...
The high uncertainty of megaproject results in increasing complexity in the decision-making and ultimately leads to different degrees of cost overrun and project delays. One of the critical reasons for cost overrun and delay is the optimism bias of d...
Climate change in recent years has produced viable shipping routes in the Arctic. However, critical uncertainties related to maritime operations in the Arctic make it difficult to predict ship speeds in ice and, thus, the voyage time and fuel costs....
Forecasting the unit cost of every product type in a factory is an important task. However, it is not easy to deal with the uncertainty of the unit cost. Fuzzy collaborative forecasting is a very effective treatment of the uncertainty in the distribu...
Forecasting the final cost of construction projects during the construction phases is challenging, particularly for long-duration projects, due to the rise in uncertainties associated with future cost performance index values after report dates and t...
The Construction Cost Index (CCI) is an important tool that is widely used in construction cost management to monitor cost fluctuations over time. Numerous studies have been conducted on CCI development and forecasting models, including time series,...
In recent years, the diffusion of electric plants based on renewable non-dispatchable sources has caused large imbalances between the power generation schedule and the actual generation in real time operations, resulting in increased costs for dispat...
Most large construction projects face the problem of cost overruns and failures to meet deadlines mainly due to changes in the cost of building materials. A lot of studies proved the high importance of the cost of building materials for the project b...
This study reviews the extant literature on hydrogen production cost forecasts to identify and analyze the historical trend of such forecasts in order to explore the feasibility of wider adoption. Hydrogen is an important energy source that can be us...
Despite the rapid improvement in the availability and resolution of real-time electricity data, budget development processes in mining have remained relatively unchanged. Currently, there is no standard for the evaluation of mine electricity cost bud...
Construction price indices play a critical role in shaping construction activity and determining the economic success of building projects in Germany, where they can serve as central inputs to cost planning and to updating trade-level project budgets...
Precise and steady substation project cost forecasting is of great significance to guarantee the economic construction and valid administration of electric power engineering. This paper develops a novel hybrid approach for cost forecasting based on a...
Traffic forecasting is an important task for transportation engineering as it helps authorities to plan and control traffic flow, detect congestion, and reduce environmental impact. Deep learning techniques have gained traction in handling such compl...
Accurately forecasting streamflow values is essential to achieve an efficient, integrated water resources management strategy and to provide consistent support to water decision-makers. We present a simple, low-cost, and robust approach for forecasti...
The accurate estimation of electricity supply costs has become increasingly relevant due to growing demand, variable generation sources, and regulatory changes in emerging power systems. This study models the average unit cost of electricity supply (...
Raising the final contract cost (FCC) is a significant risk for project owners. This study hypothesizes that the factors that cause owner’s cost estimation (OCE) accuracy and FCC changes share the same causes, and a case study confirmed that th...
In today’s volatile market environment, supply chain management (SCM) must address complex challenges such as fluctuating demand, fraud, and delivery delays. This study applies machine learning techniques—Extreme Gradient Boosting (XGBoos...
Perishable products are very common, but managing inventory of perishable products can be very challenging for firms, especially in distribution systems, including multiple chain stores. In this environment, we consider a dynamic lot-sizing problem f...
The research network “Basic Concepts for Convection Parameterization in Weather Forecast and Climate Models” was organized with European funding (COST Action ES0905) for the period of 2010–2014. Its extensive brainstorming suggests how the subgrid-sc...
When identifying and comparing forecasting models, there may be a risk that poorly selected criteria could lead to wrong conclusions. Thus, it is important to know how sensitive the results are to the selection of criteria. This contribution aims to...
The growing interest in deploying photovoltaic systems and achieving their benefits as sustainable energy supplier raises the need to seek reliable medium-term and long-term operations with optimal performance and efficient use of economic resources....
Certain wind turbine maintenance tasks require specialist equipment, such as a large crane for heavy lift operations. Equipment hire often has a lead time of several weeks but equipment use is restricted by future weather conditions through wind spee...
Forecasting the cash flow for infrastructure projects has not received much attention in the existing models. Moreover, disregarding the cost flow behaviour and proposing models that entail a relatively high dimensionality of inputs have been the mai...
An accurate cost estimate not only plays a key role in project feasibility studies but also in achieving a final successful outcome. Conventionally, estimating cost typically relies on the experience of professionals and cost data from previous proje...
In the scope of energy management systems (EMSs) for microgrids, the forecasting module stands out as an essential element, significantly influencing the efficacy of optimal solution policies. Forecasts for consumption, generation, and market prices...
The unevenness of the electricity consumption schedule at enterprises leads to a peak power increase, which leads to an increase in the cost of electricity supply. Energy storage devices can optimize the energy schedule by compensating the planned sc...
The construction industry is heavily influenced by the volatility of material prices, which can significantly impact project costs and budgeting accuracy. Traditional econometric methods have been challenged by their inability to capture the frequent...
Machine learning technologies have recently emerged as transformative tools for enhancing project management accuracy and efficiency. This study introduces a data-driven model that leverages the hybrid eXtreme Gradient Boosting-Simulated Annealing (X...
Existing works in photovoltaic (PV) power generation focus on accurately predicting the PV power output on a forecast horizon. As the solar power generation is heavily influenced by meteorological conditions such as solar radiation, the weather forec...
This paper presents a decentralized energy management system (EMS) based on Multilayer Perceptron Artificial Neural Networks (MLP-ANNs) and a Modified Cheetah Optimizer (MCO) to account for uncertainty in renewable generation and load demand. The pro...
Classifying imbalanced data is a well-known challenge in machine learning. One of the fields inherently affected by imbalanced data is credit datasets in finance. In this study, to address this challenge, we employed one of the most recent methods de...
Particulate air pollution has aggravated cardiovascular and lung diseases. Accurate and constant air quality forecasting on a local scale facilitates the control of air pollution and the design of effective strategies to limit air pollutant emissions...
To accurately assess the development potential of a high-speed rail (HSR) express in the logistics system, this study constructs a forecasting method for HSR express volume. Grey relational analysis is used to identify key influencing factors, and a...
This article presents a systematic literature review exploring the integration of Artificial Intelligence (AI) methodologies in project management (PM). Key applications include cost estimation, duration forecasting, and risk assessment, which are cr...
Particulate matter (PM) pollution is a major global concern due to its negative impact on human health. To effectively address this issue, it is crucial to have a reliable and efficient forecasting system. In this study, we propose a framework for pr...
We propose a new approach for estimating the state-level direct and indirect economic cost of obesity in the United States for the time period 1996 to 2018. Our unique top-down methodology integrates a prevalence-based method with various medical-lev...
This study evaluates eight-year ownership costs for battery electric vehicles (BEV) versus non-plugin hybrid vehicles, using forecasting to estimate future electricity and conventional gasoline prices and incorporating these in a multiple design of e...
In the context of sustainable buildings, this paper investigates power flow management for an isolated DC microgrid and focuses on efficiency and energy cost reduction by optimal scheduling. Aiming at high efficiency, the local produced power has to...
This article investigates integrated maintenance, production, and product quality control policy for a supply chain consisting of a single machine producing only one type of product, a main storage warehouse, and multi-purchases warehouses. The varia...
With the large-scale construction and commissioning of transmission lines over the past two decades, the grid is facing a large-scale centralized decommissioning of transmission lines. The transmission line’s economic life is crucial to rationa...
Cost overruns are a significant issue in infrastructure projects, adversely affecting not only economic performance but also sustainability goals by straining resources and extending project timelines. There is consensus among researchers about the p...
Pharmaceutical manufacturing and logistics rely on accurate prediction and decision making to safeguard product quality, delivery reliability, and patient outcomes. Despite rapid advances in artificial intelligence (AI) and machine learning (ML), few...
The Internet of Medical Things, Smart Devices, Information Systems, and Cloud Services have led to a digital transformation of the healthcare industry. Digital healthcare services have paved the way for easier and more accessible treatment, thus maki...
Traditional learning curve theory assumes a constant learning rate regardless of the number of units produced. However, a collection of theoretical and empirical evidence indicates that learning rates decrease as more units are produced in some cases...
In a deregulated electricity market, optimal hydropower operation should be achieved through informed decisions to facilitate the delivery of energy production in forward markets and energy purchase level from other power producers within real-time m...
29 July 2025
Demand for mental health support has exerted unprecedented pressure on statutory services. Innovative solutions such as Green or Nature-Based Social Prescribing (NBSP) programmes may help address unmet need, improve access to personalised treatment,...
Saudi Arabia relies heavily on fossil fuels for electricity generation, leading to significant environmental challenges, including high levels of greenhouse gas emissions. This study evaluates the environmental and financial impacts of integrating so...
The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this...
Machine learning can be used for social good. The employment of artificial intelligence in smart agriculture has many benefits for the environment: it helps small farmers (at a local scale) and policymakers and cooperatives (at regional scale) to tak...
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