The Role of Artificial Intelligence in Quantity Surveying

ABSTRACT

This study Identify the roles of artificial intelligence in Quantity Surveying. Faced with the problem of relatively non-exploration of AI’s specific applications and implications in the field of Quantity Surveying, the study was guided by the following objectives; To identify the various applications of AI in Quantity Surveying, To Access the benefits of AI implementation in Quantity Surveying The study employed descriptive and explanatory design, questionnaires in addition to library research were applied in order to collect data. Primary and secondary data sources were used and data was analyzed using statistical package which was presented in frequency tables and percentage. The respondents under the study were 30 employees of Quantity Surveyors firm. The study findings revealed that there is a significant impact [the Roles of AI] has on [Quantity Surveying practices]. Linear regression significant at 0.05 level (2-tailed), 0.00 indicates the significance of interaction between two variables an indication that the significant is under the range of 0.0 and 0.05. Using the above findings, it implied that there is a strong relationship between [the application of AI] and [Quantity Surveying practices]. Key recommendations from the study are; As AI is a rapidly evolving field, and its successful integration into Quantity Surveying practices requires a deep understanding of its capabilities and limitations, comprehensive training should be provided to professionals to empower them to leverage AI tools effectively. The Training should encompass understanding AI algorithms, data pre-processing, model interpretation, and the ethical considerations associated with AI-driven decisions. While there are general-purpose AI tools available, customizing AI solutions to align with the nuances of Quantity Surveying processes can yield more accurate results. Quantity Surveyors should collaborate with AI experts to ensure that the tools are tailored to address the specific challenges faced in the industry. This partnership can lead to the creation of specialized algorithms for tasks such as cost estimation, risk assessment, and resource allocation.
AI algorithms require high-quality, well-organized data for effective functioning. a data-driven infrastructure should be established, one which involves capturing data from various sources such as historical projects, cost databases, material suppliers, and project management tools. This data can then be used to train AI models, generate accurate predictions, and offer data-driven insights for decision-making. A data-driven approach ensures that AI applications are based on real-world information, leading to more accurate predictions, estimations, and risk assessment.