technologies such as Artificial Intelligence (AI), Building Information Modeling (BIM), and data analytics.

Among these innovations, generative artificial intelligence has emerged as a powerful tool capable of improving

decision-making processes and automating complex analytical tasks. One of the major challenges in construction

project management is achieving accurate cost estimation and maintaining effective budget control throughout

the project lifecycle. Traditional cost estimation methods mainly depend on historical data, manual calculations,

and professional experience, which can sometimes result in inaccurate forecasts and financial inefficiencies.

Generative AI offers new possibilities for construction management by analysing large datasets, identifying

hidden patterns, and generating predictive models that assist project managers in making informed financial

decisions. This research paper examines the potential role of generative AI in improving construction cost

requires reliable digital infrastructure, high-quality datasets, and trained professionals with expertise in AI

technologies. The study concludes that generative AI has the potential to transform construction cost

management practices and contribute to more efficient and sustainable project execution.

Management, Digital Construction.

The construction industry plays an essential role in the economic and infrastructural development of nations. It

provides critical infrastructure such as residential buildings, transportation networks, commercial facilities, and

location, and market fluctuations. Traditional estimation approaches rely heavily on historical project data and

expert judgment. According to Charles Eastman, Paul Teicholz, Rafael Sacks, and Kathleen Liston, the

integration of digital technologies such as Building Information Modeling (BIM) has significantly improved the

way project information is managed and analysed for cost estimation and planning.

Despite these advancements, construction projects frequently face budget overruns and financial inefficiencies.

Factors such as unexpected price changes, design modifications, supply chain disruptions, and inaccurate

predictions can affect project budgets. These challenges highlight the need for advanced technological solutions

that can enhance cost estimation accuracy and improve financial decision-making.

Generative AI is an advanced form of artificial intelligence that can produce new outputs based on patterns

identified in existing data. These systems can generate simulations, forecasts, and optimized solutions that

support decision-making processes. In the context of construction management, generative AI can analyse

project data, predict cost variations, and suggest optimized budgeting strategies.

The objective of this research paper is to examine how generative AI can enhance construction cost estimation

and improve budget optimization in construction projects.

Research by Rafael Sacks and Lauri Koskela emphasized the integration of BIM with lean construction

principles to improve productivity and reduce waste in construction projects.

Artificial intelligence technologies have also been applied to construction management tasks. Studies conducted

by Jian Zhang and Nader El-Gohary demonstrated that AI algorithms can analyse construction documents and

extract valuable information for project planning and cost forecasting.

Automation technologies have also been investigated in construction research. Thomas Bock and Thomas Linner

studied the application of robotics and intelligent systems in construction processes and highlighted their

potential to improve productivity and reduce operational costs.

Further research by Xi Chen, Rui Jin, and Li Yang indicates that predictive analytics methods can help

construction organizations estimate project costs more efficiently.

In addition, Tobias Oesterreich and Frank Teuteberg emphasized that digital transformation is reshaping the

construction industry and improving operational efficiency.

However, despite the growing interest in AI technologies, the practical implementation of generative AI in

construction cost management is still in its early stages.

The main objectives of this research are:

This research adopts a qualitative approach based on secondary data analysis. Academic journals, conference

papers, books, and industry reports related to artificial intelligence and construction management were reviewed.

Previous studies conducted by Peng Wu, Jian Wang, and Xiangyu Wang highlight the importance of data-driven

analytical approaches for evaluating construction technologies. Similarly, research by Xi Chen, Rui Jin, and Li

Yang demonstrated the effectiveness of machine learning models in predicting construction costs.

The methodology consists of the following steps:

Generative AI can analyse digital construction drawings and automatically calculate quantities of materials such

as concrete, steel, and bricks. This process significantly reduces manual effort and improves estimation accuracy.

AI algorithms can analyse historical project data and develop predictive models for estimating construction costs

based on project parameters such as building size and design complexity.

Generative artificial intelligence offers innovative solutions by analysing large datasets and generating predictive

insights for improved cost estimation and financial planning. Although several challenges exist, including data

availability and technological integration, the potential benefits of generative AI are substantial.

As digital technologies continue to evolve, generative AI is expected to play a significant role in transforming

construction cost management and supporting more efficient project execution.