Research on Cost Control of Railway Engineering Based on Grounded Theory

Abstract

An analysis of cost management within railway construction projects has been conducted using the Analytic Hierarchy Process (AHP) and a regression analysis to evaluate and rank key financial and operational factors affecting project costs. This research assesses the impact of various metrics, such as Safety Inspections, Emergency Preparedness, and Equipment Maintenance, along with financial indicators such as Actual Cost and Variance, on cost control strategies by building a hierarchical model and implementing AHP. The results indicate a clear preference for Financial Metrics, with a priority vector of 0.667, over Operational Metrics, which have a priority vector of 0.334. Among the Financial Metrics, Actual Cost, with a priority vector of 0.565, is identified as the most influential, underscoring the importance of direct cost management. Among the Operational Metrics, Emergency Preparedness is the most important, with a priority vector of 0.540, emphasizing the importance of effective risk management. A regression analysis confirms these priorities, with significant correlations presented between these metrics and variances in costs. According to this study, changes in Emergency Preparedness and Equipment Maintenance can predict cost fluctuations, aligning with the findings of the AHP study. The AHP evaluations are demonstrated to be reliable, with consistency ratios significantly below the 0.1 benchmark (0.043 for Financial Metrics and 0.008 for Operational Metrics), indicating a high degree of consistency in judgment. The statistical validation enhances the framework’s effectiveness in steering strategic decisions regarding cost management. This paper discusses the implications of these results to reduce financial risks and improve project outcomes.

1. Introduction

In railway engineering, research on a variety of issues influencing financial management and project execution efficiency has attracted a lot of attention. Cost management strategies are heavily influenced by organizational contexts, and as such are a topic of significant interest in the field of railway construction projects. Numerous studies have demonstrated that the unique traits of an organization’s culture, its structural dynamics, and external factors like market competition and regulatory requirements have a significant impact on how project teams adopt and perceive cost control measures. The culture of an organization has a significant impact on how staff members see cost management, and frequently determines how strictly cost control measures are followed. Team coordination and communication are negatively impacted by structural elements including hierarchical organization and centralized decision making, which are essential for effective cost management [1]. Technological developments in railway engineering, particularly in the field of aerodynamics, are crucial in mitigating the expenses associated with greater train speeds, which in turn intensify aerodynamic difficulties such as drag, slipstream, pressure waves, and micro-pressure waves. These problems require complex technical solutions to control costs throughout the lifespan of railway projects since they not only influence the energy consumption and operating efficiency of trains, but they also increase maintenance expenses and wear on the infrastructure [2].

The strategic management of financial resources through planning and budgeting is another area of concentration (Figure 1). Expenditures associated with the construction and maintenance of railway infrastructure may be accurately predicted and regulated, lowering the possibility of unforeseen financial hazards, thanks to the growing use of contemporary categorization systems and normative budgeting techniques [3]. Algorithms have been created to regulate passenger flow at terminal stations as part of operational innovations. The combination of automatic frequency control systems and cost theory has improved passenger flow management, which in turn has improved operational planning and efficiency. By optimizing travel mode selection and trip duration estimates, these algorithms help manage everyday railway operations more successfully [4]. There has also been an emphasis on the use of graph theory and network science in the strategic design of railway networks. Using this method, a more economical manner is suggested to improve network connection without needless expansions, minimizing capital expenditures and optimizing operating expenses more effectively [5]. Technological advancements include augmented reality, artificial intelligence, and the Internet of Things (IoT), which can also transform processes and workforce dynamics in the railway transport industry [6]. Furthermore, the application of big data and IoT technologies to predictive maintenance signifies a substantial shift in maintenance methodologies. By utilizing IoT devices, railways can adopt predictive maintenance strategies that drastically minimize maintenance expenses and downtime, facilitating the early identification of possible malfunctions and augmenting operational effectiveness [7]. The integration of both macroscopic and microscopic control levels is a crucial aspect of high-speed railway timetabling optimization. Maximizing train operating hours, speed control, and energy efficiency highlights the need for comprehensive planning to reduce environmental consequences [8]. Environmental factors are also very important in railway engineering, especially when calculating the carbon footprint of railway sleepers. Comparing lifespan greenhouse gas emissions for various sleeper materials under different traffic situations has shown that material choice considerably affects environmental sustainability when compared to different traffic scenarios [9]. Railway track lifecycle management innovations efficiently handle financial restrictions and cost demands. These technologies analyze and track component conditions by using measurement data to facilitate renewal planning and preventative maintenance. Predictive analytics may be used to improve track lifetime and safety through more accurate maintenance scheduling and cost-effective resource management [10]. In order to guarantee the resilience of railway infrastructure, it has become more crucial than ever to improve track quality indices (TQIs) through innovative measuring methods, highlighting the necessity of thorough evaluations that strike a compromise between system safety and financial restrictions [11].

The primary aim of this study is to develop effective cost management strategies for railway construction projects by evaluating and ranking key financial and operational factors. By using the Analytic Hierarchy Process (AHP) and regression analysis, this research seeks to identify the most influential metrics, such as Actual Cost and Emergency Preparedness, and assess their impact on cost control. The objective is to provide a robust framework that can guide strategic decisions, reduce financial risks, and improve the overall project outcomes in the railway construction industry. Figure 1 illustrates the activity management system for railways and vehicles.

2. Literature Review

In order to address the problem of duplicate crossings, research by Soleimani et al. looked at ways to improve a highway-railroad grade crossing consolidation model in East Baton Rouge Parish by applying machine learning, text mining, and spatial analysis approaches. The eXtreme Gradient Boosting (XGboost) consolidation approach was found to be beneficial in previous research, and this work refined the model by integrating new analytical tools. In total, there were 57 collisions between trains and automobiles between 2015 and 2019 that caused a great deal of property damage and injuries. Their research addresses the urgency that these tragedies have produced [12]. Chrzan investigated how money from the European Union affected the modernization of rail traffic control systems and Polish railways. The study focused on the use of LTE (Long-Term Evolution) systems for enhancing railway communication and control because of large expenditures in GSM-R technology [13]. Sancho carried out an investigation of maintenance decision making utilizing a Markov Decision Process (MDP). Using Markov transition matrices and cost/reward vectors, a condition-based maintenance system was constructed based on rail dimensions, utilization, and deterioration. The results have led to the identification of an ideal course of action that leads to a considerable cost reduction over an endless duration [14]. A bi-objective approach was utilized by Song et al. to optimize the alignment of mountain railways, while accounting for the associated seismic and economic concerns. By lowering expenses and minimizing seismic hazards, the quantitative method showed that railway alignment designs have advanced strategically [15]. Loy-Benitez et al. carried out a techno-economic evaluation of an integrated energy system for an electrified railway network, emphasized the advantages for the environment, including a 73% decrease in greenhouse gas emissions as compared to traditional systems, and showed how well a proton-exchange membrane electrolyzer, solid-oxide fuel cell, and lithium-ion battery work together to provide energy [16]. Using deep learning models, Huang et al. investigated train delay patterns. FCF-Net integrates fully connected and convolutional neural networks to analyze train event interactions and non-operational impacts. It was demonstrated that compared to previous models, this one was more accurate in identifying delay propagation patterns [17]. In order to improve safety, efficiency, and environmental sustainability, railway systems must include cutting-edge technology and complex analytical approaches. These works collectively highlight important methodological and technological developments in railway engineering.

Torralba et al. conducted a study on the development of a smart railway operation support system designed for facilities with lower safety requirements, such as ports and logistics hubs, where only freight trains operate at controlled, low speeds. The research focuses on adapting rail traffic control systems, which are undergoing significant global transformations with the implementation of various train control systems based on radio communication, to these specific environments that do not necessitate the high safety standards required for passenger transportation [18]. Xiao et al. presented an innovative solution in 2021 to address power quality challenges, particularly those associated with negative sequence current (NSC) in high-speed railway power supply systems. They proposed a multi-station cooperative NSC compensation approach to enhance the effectiveness of compensating devices and reduce capacity requirements. This approach involves examining the cooperative control strategy and the topology of multiple traction substations [19]. In 2021, Sadeghi et al. developed an integrated railway ballast quality indicator to improve track maintenance management systems. Their study classified and evaluated the mechanical, physical, and environmental characteristics of ballast materials based on their impact on train infrastructure. This approach aimed to streamline the selection process of ballast materials, providing a comprehensive understanding of the factors influencing ballast performance. Ultimately, this method contributes to enhancing the overall effectiveness of track maintenance [20]. Ren et al. examined recent advancements in rail and track inspection, as well as the challenges facing local lines in the US. They discussed methods to enhance the efficiency and accuracy of inspections using machine learning-based techniques, thermal imaging, and a ground-penetrating radar. The research emphasized the importance of implementing innovative approaches to reduce the risks associated with deteriorating track infrastructure and enhance safety on local train lines [21].

Using ground-to-air heat exchangers, Asadi et al. conducted a numerical analysis of the cooling efficiency of cylindrical roof buildings. According to the findings, Ar = 0.082 is the ideal opening for roof spans. In the study, factors such as wind speed, ceiling opening diameter, and ground-to-air heat exchanger dimensions and number were examined for their impact on average room temperature [22]. A comprehensive review of the analytical and numerical studies conducted on ground-to-air heat exchangers, as well as exergoeconomic analyses related to heat transfer, was provided by Asadi et al. Energy efficiency, pollution reduction, and economic savings are all promoted by this field in their research [23]. Using digital twins and artificial intelligence in smart city stormwater infrastructure, Sharifi et al. investigate the scientific literature on urban drainage systems and the application of digital twins [24]. Bilevel topic labelled latent Dirichlet allocation (Bi-TLLDA) was developed by Wei and Zhao to diagnose defects in vehicle on-board equipment. In order to overcome the inefficiencies inherent in conventional experience-based maintenance, this strategy extracts characteristics from unstructured, high-dimensional, and unbalanced fault class distribution data. In order to create a fault feature space, the Bi-TLLDA approach uses Gibbs sampling to define local and global themes for two-level fault types. Using this method, fault detection is improved and machine learning is demonstrated as a useful tool for improving railway safety and reliability [25]. Zhang et al. developed the Mixed Integer Nonlinear Program (MINLP) in order to optimize logistics management. Their approach promises significant improvements in efficiency and cost reduction in logistics operations by integrating various parameters such as distribution time, capacity, and modes of transportation into a model using the General Algebraic Modeling System (GAMS) and solving it with the DICOPT solver [26]. Lin et al. in 2021 explored the use of Non-Uniform Rational B-Spline theory (NURBS) to create a target rail grinding profile. In addition to reducing wear and extending rail life, they wanted to develop a cost-effective grinding technique that leaves the rail’s structural integrity intact. A great deal of promise can be gained by optimizing the grinding process in this approach [27]. According to Kawasaki, the COVID-19 pandemic, increased weather, and population changes had affected the design and administration of railways. According to the study, continuous research and development is necessary to meet these changing demands and to ensure the effective and sustainable operation of trains and transportation systems [28]. A study carried out by Kawasaki et al. in 2021 examined how information and communications technology (ICT) could be used to improve railway signaling and telecommunication systems. Their research is focused on developing autonomous train operation control systems in response to the new RTRI master plan, which begins in FY2022, with the aim of using information and communications technology (ICT) to make railway operations safer, more reliable, and more economical [29].

To optimize railway network maintenance plans, Fecarotti et al. developed a nonlinear integer programming model. With its comprehensive approach to network maintenance that minimizes the impact on service while taking budgetary limits into consideration, this model combines a variety of performance and cost variables, demonstrating an efficient way to solve problems with large-scale network applications [30]. Using fiber Bragg grating sensors, Kerrouche et al. investigated a method to monitor railway switches and crossings in real time. In order to improve the upkeep and dependability of essential railway parts, this technique provides an affordable method for identifying and controlling infrastructure deterioration [31]. Mahmoudzadeh et al.’s study examined the distribution of maintenance funds for multimodal transportation networks, including waterways. Taking into account a variety of operational and environmental factors, they guarantee the effective operation and maintenance of inland maritime transportation networks [32]. A Monte Carlo simulation and a probabilistic fault tree analysis were used by Usman et al. to evaluate the hazards associated with ballasted railway drainage systems. The report emphasizes the importance of a risk-informed management strategy in order to effectively deal with and mitigate challenges associated with railway drainage. By doing so, serious infrastructure failures will be minimized, and the associated costs will be reduced. As a whole, these studies demonstrate a great deal of progress in railway technology and management, covering a broad range of topics from defect finding to asset maintenance and optimizing operations in response to environmental and technological changes [33].

Zhang et al.’s study on the effect of track restrictions on the seismic risk for multi-span simply supported beam bridges used in high-speed railways clarifies how the track adds to bridge dynamic properties. The study examined over 3000 calculation scenarios using a simplified model that successfully balances high accuracy with high computing efficiency in order to guarantee the stability and dependability of the seismic reactions examined [34]. Their findings indicate that seismic risks in the longitudinal direction of the track–bridge model are consistent with those reported in earlier research, including Wang et al.’s study. However, they highlight the need for further research to improve our understanding and ability to predict seismic risks when track constraints are present [35]. To deal with unanticipated disruptions in train operations, Liu et al. presented a two-stage distributionally robust railway timetabling (DRRT) model. Using a Wasserstein ambiguity set based on empirical data distributions, the model minimizes trip time, worst-case predicted delays, and total idle time [36]. The results of the study demonstrated how well the approach reduced delays and improved the quality of passenger service, which was an important advancement in the development of a reliable schedule. Model-based systems’ engineering can be enhanced by incorporating cost analysis using Systems Modelling Language (SysML) as suggested by Kotronis et al. By using this technique, designers will be able to evaluate cost/performance trade-offs within the SysML framework. Model-based systems’ engineering facilitates the creation of more economical and more efficient system designs by enhancing the use of standardized languages and tools [37].

With an emphasis on eco-driving and effective timetable management, Wang et al. studied the optimization of urban railway operations. A dual-objective approach was used in the study to optimize speed curves and schedules using quadratic programming combined with an improved artificial bee colony method [38]. In comparison with conventional approaches, the study demonstrated a 15% reduction in passenger waiting times and a 12% reduction in energy consumption, emphasizing the advantages of incorporating eco-friendly practices into urban railway operations. Ahmadi et al. generated a global elevation map for Florida’s coastal area using deep learning techniques and a digital twin model. Using the U-Net network, the study demonstrated how deep learning can generate a comprehensive terrain and height map and can accurately classify different terrain categories [39]. According to Moghim et al., cumulus and microphysics schemes were used in the WRF model to simulate heavy rainfall events and Cyclone Sidr in Bangladesh. The WRF model accurately captured the cyclone’s track, intensity, and landfall location [40]. Using SAM and U-Net deep learning models, Ahmadi et al. evaluated their performance for detecting cracks in concrete structures. A combination of the SAM and U-Net models offers promising results for crack detection in concrete structures that are more accurate and comprehensive [41]. In 2024, Safaria et al. examined the possibility of land value capture (LVC) as a substitute funding source for Indonesia’s urban railway infrastructure. Owing to their findings, although LVC provides a feasible financing option, in order to assure its efficacy and acceptability as a financing model, regulatory frameworks must be carefully prepared. Comprehensive assessments of costs, benefits, market dynamics, sustainability, and community involvement are also necessary [42]. By using cutting-edge techniques and tools to solve operational, budgetary, and engineering problems, these studies together enhance railway engineering and planning. A summary of the literature research is presented in

Table 1. The survey of the recent papers.

Author(s)	Year	Area of Study	Method	Key Results
[43]	2020	Transport Infrastructure	SWOT Analysis	Identified benefits of polygon technology in railway traffic management.
[3]	2020	Railway Budgeting	Normative Budgeting	Developed a risk management algorithm to minimize losses in railway operations.
[2]	2020	Train/Tunnel Aerodynamics	Empirical Study	Increased train speed exacerbates aerodynamic issues affecting energy use and costs.
[4]	2020	Railway Passenger Flow	Algorithm Development	Created an algorithm for passenger flow recognition at terminal stations.
[44]	2020	Railway Freight Operation	Optimization Model	Increased market share and reduced carbon emissions for freight services.
[45]	2020	Train Trajectory Optimization	Markov Decision Process	Developed a model to ensure on-time arrival of trains under uncertain conditions.
[8]	2020	Timetabling for Railways	Micro–Macro Integration	Integrated approach for energy-efficient train timetabling.
[46]	2020	Wildlife–Train Collisions	Experimental Setup	Warning signals increased wildlife awareness, potentially reducing collisions.
[12]	2021	Railway Grade Crossing	Machine Learning	Improved consolidation model for highway-railroad grade crossings.
[13]	2021	Polish Railway Systems	Case Study	Examined the implementation of LTE systems for railway traffic control.
[14]	2021	Maintenance Decision Making	Markov Decision Process	Developed a condition-based maintenance plan for rail components.
[16]	2021	Integrated Energy Systems	Techno-Economic Assessment	Demonstrated significant reductions in GHG emissions using renewable energy sources.
[17]	2021	Train Delay Patterns	Deep Learning	Enhanced model for recognizing train delay propagation patterns.
[18]	2021	Smart Railway Systems	System Development	Developed a low-safety requirement operational aid system for freight trains.
[47]	2022	Railway Seismic Response	Simulation Study	Evaluated seismic response differences across bridge spans.
[48]	2022	Tunnel Performance Evaluation	Delphi and AHP Methods	Established a model for tunnel maintenance strategy evaluation.
[49]	2023	Integrated TMS and ATO	Simulation Study	Improved punctuality and energy efficiency in rail traffic management.
[34]	2024	Seismic Risk in Railways	Computational Model	Proposed a new method to assess seismic risk influenced by track constraints.
[36]	2024	Railway Timetabling	Distributionally Robust Model	Developed a model to minimize delays and improve service quality.
[38]	2024	Urban Railway Optimization	Two-Objective Approach	Reduced energy consumption and passenger waiting times through optimized train operation.
[50]	2024	Developing Circular Economy	MILP	A mathematical model and algorithm from an Iranian knowledge-based company are validated with data and accuracy is determined by sensitivity analysis.
[51]	2024	Decision Support for Sustainable Welfare	Computational Model	Using natural resources sustainably, investing in technology, attracting foreign direct investment, and growing capital are all required for sustainable development.

.

3. Methods and Materials

3.1. Variables and Parameters

This study aims to analyze and improve cost management strategies in railway construction projects by identifying key financial and operational factors. Utilizing both the AHP and regression analysis, this research seeks to evaluate the impact of these factors on overall project costs. Resilience is a key component of railway construction project management, especially considering the size and complexity of the problems these projects present. This study integrates theoretical frameworks with real-world applications by using a collection of important indicators to efficiently analyze and control expenditures. The variables that have been selected are Safety Inspections, Emergency Preparedness, Equipment Maintenance, Budgeted Cost, Actual Cost, Variance, and Variance Percentage. These variables are essential to comprehending the dynamics of cost management in the demanding world of railway construction. These factors have been carefully chosen to represent crucial facets of project management that have a big impact on overall project success and cost control. As a crucial indicator of regulatory compliance and attention to safety procedures, the proportion of planned safety inspections that are performed is used to measure Safety Inspections. High completion rates are a sign of proactive risk mitigation in the construction industry, which may lower unanticipated expenses from accidents and fines from the authorities. Lower completion rates, on the other hand, might indicate a chance of unforeseen costs that could blow the entire budget.

The project’s ability to manage emergencies is measured using a readiness level %, which is used to assess Emergency Preparedness. This statistic evaluates the readiness of systems and people for unforeseen circumstances that can cause delays in projects or extra expenses. A strong degree of preparedness denotes efficient emergency procedures that reduce downtime and the financial impact of interruptions, hence strengthening the project’s ability to withstand unforeseen costs. A maintenance index that indicates the present condition of equipment maintenance is used to track Equipment Maintenance. Maintaining the project properly is essential to preventing malfunctions that might cause delays and cost overruns. By keeping the construction machinery and equipment in excellent operating condition, you can make sure that the project stays on schedule, save money on repairs or replacements, and guard against unanticipated changes to the project’s budget.

During the study’s analysis of cost management, two critical Financial Metrics are examined: the budgeted cost and the actual cost. As a financial roadmap for expected expenditures, the term “budgeted cost” refers to the projected costs that are outlined prior to the start of each phase of the project. As opposed to this, the real cost refers to the actual expenses incurred during the course of a project. Several factors may contribute to this, including labor costs, material pricing, and unforeseen expenses. By analyzing the difference between the Budgeted and Actual costs, the Variance and Variance Percentage can be calculated between these two measurements and is used to compute variance, which gives a precise indication of financial deviance. This number is important since it shows if the project is spending more or less than its allocated budget. The Variance Percentage provides an insight into the degree of financial overspend or underspend by presenting it as a percentage of the Budgeted Cost (Figure 2).

Using these financial measurements, you can gauge the effectiveness of the current cost control techniques by comparing the project expenses with the original budgetary assumptions. This study aims to shed light on the delicate balance needed for efficient cost control in railway building projects by carefully selecting and examining these factors. By examining how operational procedures such as Safety Inspections, Emergency Preparedness, and Equipment Maintenance interact with one another and affect the financial health of projects, this study aims to offer important insights. In light of the above observations, project management methodologies can be improved and cost containment capabilities enhanced, ensuring the economic viability and successful completion of significant railway infrastructure projects.

3.2. Methods

To establish a comprehensive framework for understanding and controlling project expenses effectively, this study utilizes quantitative techniques and equations to evaluate cost management in railway engineering projects. In this approach, grounded theory is combined with a statistical analysis in order to develop a hybrid model specifically suited to the complexities of large construction projects. Through the integration of these methodologies, data collected during the project lifecycle can be analyzed and interpreted in a better manner, providing valuable insights into project economics. A key component of our methodology is the application of cost variance analysis, a conventional economic tool used to calculate the differences between planned mining and the degree of cost deviations, whether excesses or savings, serving as a definitive measure of financial management proficiency. In this context, Variance is computed by deducting the Budgeted Cost from the Actual Cost, providing a straightforward assessment of budget performance and indicating whether the project’s financial health is on target or requires adjustments. By situating the Variance within the context of the planned budget, the Variance Percentage further refines our financial analysis. As a percentage, the Variance is divided by the Budgeted Cost and multiplied by 100. In addition to indicating the existence of a variance, this percentage also indicates the magnitude of the Variance in comparison to the total budget, thereby allowing stakeholders to evaluate the financial effectiveness of the project in a proportional manner.

Additionally, statistical methods are used to examine trends and patterns in cost data. A snapshot of the project’s financial condition can be obtained by using descriptive statistics, including mean, median, mode, and standard deviation. For accurate forecasting and budgeting, these statistical tools are essential for understanding the primary trends and fluctuations within the project’s financial data. Furthermore, a regression analysis is used to investigate the correlation between various project management practices and their costs. As a result of this analysis, it is possible to identify which factors have a significant impact on project expenses and to what extent. This research offers predictive insights that can be used to refine cost management strategies by modeling the relationships between activities such as Safety Inspections, Emergency Preparedness, and Equipment Maintenance and their financial implications. A fundamental aspect of our methodology is the application of grounded theory principles, particularly during the initial stages of a project. In this approach, data are gathered and analyzed in iterative steps to develop theories based on the data rather than testing a predetermined hypotheses. By using this method, new insights and theories are generated based on actual project experiences, which is particularly useful in railway construction environments that are complex and dynamic. Through the amalgamation of these diverse methodologies—variance analysis, statistical trend analysis, regression modeling, and grounded theory—an analytical framework is created that is comprehensive and adaptable. By implementing this framework, not only are meticulous financial reporting and tracking supported, but informed management decisions can also be made. The purpose of this is to facilitate the development of flexible strategies that can be adapted to a project’s changing needs and conditions, thus enhancing overall financial governance and project outcomes. The purpose of this analysis is to analyze the interconnections between various operational metrics and their effects on project cost variation using a regression analysis. Based on operational practices and metrics, the regression model provides a quantitative structure for forecasting cost outcomes. The regression formula used is as follows:

$$Y={\beta }_0+{\beta }_1{X}_1+{\beta }_2{X}_2+{\beta }_3{X}_3+ϵ$$

where:

• Y: This includes all direct financial expenditures related to a project, including labor, materials, machinery, and all other direct expenses. During a given period, this cost represents all expenses related to the project;
• ${\beta }_0$: The intercept of the regression line, representing the estimated Actual Cost when all independent variables $\left(X\right)$ are zero;
• ${\beta }_1, {\beta }_2$, and ${\beta }_3$: the coefficients of the independent variables $X_1, X_2$, and $X_3$, respectively, measuring the change in the dependent variable for each unit change in the independent variables;
• $X_1$: This variable measures how well safety measures and checks are implemented throughout the project lifecycle. A higher completion rate of inspections is generally associated with fewer accidents and regulatory complications, thereby helping to control costs;
• $X_2$: To maintain alignment with the project’s planned timeline and budget, comprehensive plans and resources must be ready to mitigate time and cost impacts from unexpected events. This readiness level percentage evaluates the project’s ability to respond to and manage unexpected emergencies;
• $X_3$: By performing appropriate and timely maintenance on project equipment, the likelihood of malfunctions and their associated costs can be reduced. This variable uses an index to determine the state of upkeep of project equipment, which affects operational efficiency and the probability of unforeseen repairs or replacements;
• $ϵ$: the error term representing the residual effects on the Actual Cost that are not explained by the independent variables.

This equation represents ‘Actual Cost’, the actual expenditure incurred during the project, as a dependent variable. During a given period, this cost represents all expenses related to the project. When all independent variables (Xs) are zero, the intercept of the regression line represents the estimated Actual Cost. By providing a baseline, it is possible to measure the influence of the independent variables. The coefficients of the independent variables measure the change in the dependent variable for each unit of change in the independent variables, as long as other variables remain the same. It represents the variable ‘Safety Inspections’ as a percentage of completed safety inspections. A safety assessment indicates whether a project meets safety standards and if risk management is effective. A readiness level percentage serves as a measure of the variable ‘Emergency Preparedness’. In this metric, the project is evaluated for its ability to respond and manage unexpected emergencies, potentially mitigating costs. A maintenance index represents the ‘Equipment Maintenance’ variable. Using this index, one can determine the state of upkeep of project equipment, which affects operational efficiency and the probability of unforeseen repairs or replacements affecting the budget. All other factors that affect the Actual Cost but are not included in the model are captured by the error term. Random shocks and unobserved heterogeneity are included in this.

3.3. Variable Descriptions

3.3.1. Actual Cost (Y)

This includes all direct financial expenditures related to a project, including labor, materials, machinery, and all other direct expenses. Actual Cost represents the total cost incurred during the project lifecycle. Monitoring and management of this variable are crucial for staying within budget and ensuring financial efficiency.

3.3.2. Safety Inspections (X1)

Throughout the project lifecycle, this variable measures how well safety measures and checks are implemented. In general, a higher completion rate of inspections is associated with fewer accidents and regulatory complications, thereby helping to control costs. Regular safety inspections ensure compliance with standards and regulations, which minimizes the risk of project delays and additional expenses due to safety violations.

3.3.3. Emergency Preparedness (X2)

To maintain alignment with the project’s planned timeline and budget, comprehensive plans and resources must be ready to mitigate time and cost impacts from unexpected events. This variable measures the project’s readiness level for handling emergencies. Effective Emergency Preparedness can significantly reduce the financial impact of unforeseen incidents by ensuring quick and efficient responses to mitigate potential disruptions.

3.3.4. Equipment Maintenance (X3)

By performing appropriate and timely maintenance on project equipment, the likelihood of malfunctions and their associated costs can be reduced. This variable uses an index to determine the state of upkeep of project equipment. Proper maintenance practices enhance operational efficiency and extend the lifespan of the equipment, which helps avoid costly repairs and replacements. Regular maintenance checks also contribute to the overall safety and reliability of the equipment used in the project.

4. Results and Discussion

A detailed analysis of operational metrics and their relationships with cost variance in railway construction projects is provided in this study’s graphs and statistical summaries. This study provides illuminating insights into the effectiveness of project management practices and their financial outcomes by analyzing the variables of Safety Inspections, Emergency Preparedness, and Equipment Maintenance over a 24-month period, in combination with the calculations of Variance and Variance Percentage. As a result of this comprehensive analysis, key insights are revealed into how well-implemented management strategies can influence financial performance. Figure 3 illustrates the trend of Safety Inspections over time. The graph shows fluctuations in the percentage of completed safety inspections, indicating varying compliance rates throughout the project lifecycle. There are dips in compliance rates, particularly in the latter months, which may be due to lapses in safety protocols or logistical issues affecting the scheduling of inspections. These dips correlate with increased unforeseen expenses due to accidents or regulatory fines (Figure 3).

Figure 4 depicts the Emergency Preparedness levels over time. The graph highlights dramatic peaks and troughs in readiness levels, reflecting the project’s response to recent incidents or audits. Inconsistencies in readiness can expose the project to financial risks, leading to possible emergency expenditures. A proactive approach to emergency management is essential to mitigate these risks. Figure 5 shows the Equipment Maintenance Index over time. The cyclic pattern of significant variations may reflect the timing of maintenance activities. A performance review or equipment failure exacerbates these issues, leading to higher operational downtime and increased costs. Regular and timely maintenance is crucial to prevent substantial financial burdens and ensure operational efficiency.

There are significant ramifications of these fluctuations on project costs; lower rates of inspection completion during certain periods may be correlated with an increase in unforeseen expenses, potentially stemming from accidents or regulatory fines as a result of non-compliance with safety standards. During the course of an emergency preparedness project, dramatic peaks and troughs in readiness levels indicate a reactive approach to emergency management. Whenever unforeseen crises occur while the project is ill-prepared, such inconsistencies in readiness could expose the project to financial risk, leading to possible emergency expenditures. A cyclical pattern of significant variations can be seen in the Equipment Maintenance plot, reflecting the timing of maintenance activities. A performance review or equipment failure may exacerbate these problems, leading to increased costs associated with labor, parts, and operational downtime. In the absence of strategic planning and execution, these activities can result in additional financial burdens on the project, particularly if unexpected maintenance needs arise and require immediate resolution to prevent prolonged downtime or more severe equipment failures. A detailed analysis of operational metrics and their impact on cost variances within railway construction projects is presented through graphs and statistical summaries. This includes analyses of Variance and Variance Percentage over a 24-month evaluation period, covering Safety Inspections, Emergency Preparedness, and Equipment Maintenance. As a result of these analyses, we gain insight into the effectiveness of project management practices and their financial implications. A comprehensive examination of management strategies and their effects on financial performance provides essential insights. Project management plays a crucial role in managing costs and enhancing operational efficiency in large-scale construction projects.

Based on the statistical analysis, the mean variance for costs across the project timeline was 237.78, with a substantial standard deviation of 326.09, indicating a wide range in costs. While some months may closely align with budget expectations, other months may experience significant overruns. The Variance Percentage averages 6.67% with a standard deviation of 9.67%, which highlights the unpredictability and potential risks of cost escalation. The findings of this study have significant implications for improving the management of construction costs in the railway industry’s relationship between operational metrics and cost variances suggests that a more consistent and proactive approach to management practices such as safety inspections and emergency preparedness could help mitigate financial unpredictability. Specifically, enhancing the regularity and thoroughness of safety inspections could not only lower compliance-related costs but also prevent costly delays and accidents. Similarly, maintaining a higher and more consistent level of emergency preparedness could protect the project from sudden financial strains due to unexpected crises. Maintaining equipment in a timely and effective manner is emphasized in the data concerning equipment maintenance. It would be possible to prevent the substantial costs associated with emergency repairs and extended downtime by optimizing these aspects. In addition to improving the efficiency of maintenance resources, predictive maintenance strategies can contribute to a more stable financial profile by smoothing out fluctuations in the maintenance index. In complex railway construction projects, this approach to management could enhance cost control and operational efficiency.

4.1. Prioritizing the Variables with AHP

A comprehensive approach is utilized to systematically rank various operational and economic indicators via the AHP in the study of the management of expenditures in railway engineering projects. Using this technique, a tiered framework of criteria and sub-criteria are established, pairwise evaluations are conducted, and priority vectors are calculated to determine the relative importance of each factor affecting project expenses.

To use the AHP for ranking variables and sub-variables in your research, it is necessary to first construct a tiered framework for the variables, establish pairwise comparison matrices, and then compute priority scores based on the pairwise comparison matrices. Presented below is the methodology along with Python code to conduct the AHP calculations. Figure 6 illustrates the AHP method’s structure. The crossing lines between the third and fourth levels represent the pairwise comparisons between the criteria and the alternatives. Each criterion at the third level is compared with every alternative at the fourth level to determine their relative importance or preference. This comparison process involves evaluating how well each alternative meets the criteria, which is essential for calculating the priority scores that ultimately guide decision making. The multiple arrows indicate the connections and interactions between criteria and alternatives, reflecting the comprehensive and detailed nature of the AHP analysis.

4.2. Hierarchical Structure

The objective of the AHP analysis is at the top (cost control), followed by Operational Metrics as the next level of criteria, and particular variables like Safety Inspections, Emergency Preparedness, and Equipment Maintenance are at the lowest level of sub-criteria.

4.3. Variables and Sub-Variables for AHP

For the AHP, pairwise comparison matrices must be created for each of the main criteria and each of the sub-criteria. On a scale of 1 to 9, these matrices are derived based on subjective evaluations of each element’s relative importance. Cost control is the main objective at the top of the hierarchy, which is divided into two primary categories: Financial Metrics and Operational Metrics (

Table 2. The variables and sub-variables.

Level	Variable	Description
Criteria	Operational Metrics	Metrics that directly impact project costs
Sub-Criteria	Safety Inspections	Percentage of completed planned safety inspections
Sub-Criteria	Emergency Preparedness	Readiness level percentage for handling emergencies
Sub-Criteria	Equipment Maintenance	Maintenance index reflecting the state of equipment upkeep

).

Budgeted Cost, Actual Cost, Variance, and Variance Percentage are the sub-criteria under Financial Metrics. Each of these aspects of financial management within the project is unique. A Budgeted Cost sets a standard for comparing actual expenses at various stages of a project. Providing a concrete basis for financial analysis, Actual Cost is the genuine expenditure incurred. It provides a direct measure of budget fidelity, as it indicates the discrepancy between budgeted and actual costs. As a standardized measure of financial performance, Variance Percentage examines this disparity in relation to budgeted costs. In order to maintain project efficiency and safety, Operational Metrics address aspects that indirectly affect financial results. In order to prevent cost overruns due to accidents or compliance penalties, Safety Inspections gauge adherence to safety standards. An evaluation of Emergency Preparedness involves assessing the ability to cope with unexpected incidents, which can have financial repercussions if they are not handled properly. It is important to evaluate the condition of project machinery and equipment, both in terms of operational efficiency and expenses, as inadequate maintenance can result in costly delays and failures. These variables are systematically prioritized using the AHP methodology. Based on their influence on the primary goal, pairwise comparison matrices are developed for each hierarchical level. Whether they are based on expert opinion or factual information, these assessments are subjective. It is possible to assign a higher priority to Actual Cost in the Financial Metrics matrix if the immediate impact of expenditures is considered more significant than deviations from the budget in the context of the project.

By using the principal eigenvector method, priority vectors are calculated from these matrices. An eigenvalue-based consistency ratio is used to verify the consistency of these comparisons. An evaluation with a consistency ratio under 10% can usually be considered reliable for decision-making purposes since it indicates that the evaluations are sufficiently consistent. AHP results from this investigation provide a quantitative framework for prioritizing financial and operational metrics in railway engineering cost control for both criteria categories—Financial Metrics and Operational Metrics—and their corresponding sub-criteria, providing definitive insights into which aspects are most critical for managing costs (

Table 3. Description of variables used in AHP analysis for railway project cost control.

Level	Variable	Description
Criteria	Financial Metrics	Metrics that directly reflect financial performance
Sub-Criteria	Budgeted Cost	Planned financial outlay for project phases
Sub-Criteria	Actual Cost	Real expenditure recorded during the project
Sub-Criteria	Variance	Difference between budgeted and actual costs
Sub-Criteria	Variance Percentage	Variance expressed as a percentage of the budgeted cost
Sub-Criteria	Equipment Maintenance	Maintenance index reflecting state of equipment upkeep

). At the criteria level, the priority vector reveals a preference for Financial Metrics, assigned a weight of approximately 0.667, in contrast to Operational Metrics with a weight of 0.333. The outcome emphasizes the importance of direct financial management in cost containment by emphasizing the dominant role that financial aspects play within the project. As can be seen from the higher weighting of Financial Metrics, effective cost control is primarily viewed and managed through the scrutiny of budget variances and direct expenditures.

Table 4. Results of AHP for railway project cost control.

Category	Criteria/Sub-Criteria	Priority Vector Explanation	Priority Vector	Consistency Ratio Explanation	Consistency Ratio
Overall Criteria		Proportion of importance between Financial and Operational Metrics.		Measures how consistent the judgements have been across the evaluations for these criteria.
	Financial Metrics	Weight of Financial Metrics in influencing project cost control.	0.6667	Consistency of comparisons within the Financial Metrics.	0.0
	Operational Metrics	Weight of Operational Metrics in influencing project cost control.	0.3333	Consistency of comparisons within the Operational Metrics.	0.0
Financial Metrics		Proportion of importance among the financial aspects listed below.		Measures how consistent the judgements have been within the Financial Metrics.	0.0433
	Budgeted Cost	Importance of planning budgets for financial control.	0.2622
	Actual Cost	Importance of tracking actual expenditures for financial control.	0.5650
	Variance	Importance of understanding budget deviations for financial control.	0.0553
	Variance Percentage	Importance of percentage deviation from budget for financial control.	0.1175
Operational Metrics		Proportion of importance among the operational aspects listed below.		Measures how consistent the judgements have been within the Operational Metrics.	0.0079
	Safety Inspections	Weight of safety compliance in operational control.	0.2970
	Emergency Preparedness	Weight of readiness for emergencies in operational control.	0.5396
	Equipment Maintenance	Importance of maintaining equipment for operational efficiency.	0.1634

illustrates the results of the AHP used to prioritize financial and operational metrics in railway engineering projects. It provides a comprehensive comparison of the relative significance and consistency of the assessments conducted during the study in terms of priority vectors and consistency ratios for each set of criteria and sub-criteria.

As part of the Financial Metrics, the priority vector places a high emphasis on Actual Cost, which has a weight of 0.565. Accordingly, actual expenditures are regarded as the most important element of the financial management of the project since they are tangible indicators of financial outflows. Budgeted Cost, assigned a weight of 0.262, highlights the importance of budget planning in the establishment of benchmarks for financial performance. The Variance and Variance Percentage, with weights of 0.055 and 0.117, are still significant because they provide insight into the effectiveness of budgetary control by measuring the divergence between actual expenditures and planned expenditures. As indicated by the highest weight (0540), Emergency Preparedness within Operational Metrics is considered the most important sub-criteria. A keen awareness of the financial consequences of emergencies underscores the need for adequate preparedness to prevent cost overruns. While safety protocols are crucial, their direct perceived impact on costs is slightly less important than emergency management, whose weight is 0.297. Maintaining operational efficiency is the most important way to prevent costly breakdowns and delays, even though Equipment Maintenance is given the smallest weight at 0.163.

Pairwise comparisons conducted during the AHP analysis are deemed reliable because of the consistency ratios calculated for the criteria and sub-criteria matrices. The criteria consistency ratio 0.0 indicates perfect consistency of judgments. Accordingly, 0.1 is the acceptable threshold for Financial Metrics and 0.043 for Operational Metrics, respectively, which reinforces the logical consistency and coherence of the evaluations made by the experts. AHP results provide quantitative information on the relative importance of various cost control measures, which helps guide decision making. It is possible to remain within budget while addressing both direct and indirect cost influences by focusing more on the higher-weighted factors, allowing project managers to better allocate resources and attention. In addition to aiding in strategic planning, prioritization also ensures that the most critical aspects affecting cost are monitored and controlled throughout the lifecycle of the project.

5. Conclusions

An important part of this study is the establishment of a solid foundation for the adoption of a more structured approach to cost control in railway construction projects. By incorporating insights obtained from the AHP analysis into daily project management practices, it is possible to ensure that the most impactful factors receive the attention they deserve. The results of the AHP analysis indicated a clear preference for Financial Metrics, with Actual Cost being the most influential, and Operational Metrics, with Emergency Preparedness, as the most significant factor. A regression analysis further validated these findings by showing significant correlations between these metrics and cost variances. Additionally, the research illustrates how predictive analytics and advanced data-driven techniques can be used to improve the accuracy of cost forecasts and budgeting processes. The mean variance for costs was found to be 237.78, with a substantial standard deviation of 326.09, highlighting the importance of consistent and proactive management practices to mitigate financial unpredictability. This research makes several key contributions to the field of construction management and engineering:

By integrating AHP and regression analysis, this study provides a robust framework for identifying and prioritizing key cost drivers in railway construction projects, leading to more effective cost management practices. This study demonstrates the potential of using predictive analytics and machine learning models to forecast cost deviations, providing project managers with tools to reduce financial risks and improve budgeting accuracy. This research suggests incorporating sustainability metrics into the AHP framework to align cost management goals with environmental and community considerations, promoting more sustainable construction practices. Expanding the AHP framework to include a broader range of criteria and sub-criteria, such as stakeholder satisfaction, contractor performance, and supply chain efficiency, offers a more holistic understanding of project management challenges. The proposed expanded framework could lead to the development of a project management dashboard that emphasizes quality, safety, and timeliness in addition to cost, enhancing overall project oversight and control. This study paves the way for more sophisticated, analytics-driven project management approaches that can lead to more successful, cost-effective, and timely project completions, contributing significantly to the broader field of construction management and engineering.