Application Machine Learning in Construction Management

Abstract

Machine Learning is a subset and technology developed in the field of Artificial Intelligence (AI). One of the most widely used machine learning algorithms is the K-Nearest Neighbors (KNN) approach because it is a supervised learning algorithm. This paper applied the K-Nearest Neighbors (KNN) algorithm to predict the construction price index based on Vietnam's socio-economic variables. The data to build the prediction model was from the period 2016 to 2019 based on seven socio-economic variables that impact the construction price index (i.e., industrial production, construction investment capital, Vietnam’s stock price index, consumer price index, foreign exchange rate, total exports, and imports). The research results showed that the construction price index prediction model based on the K-Nearest Neighbors (KNN) regression method has fewer errors than the traditional method.

Explosive utilization efficiency enhancement: An application of machine learning for powder factor prediction using critical rock characteristics

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Lean Construction Strategies Supported by Artificial Intelligence Techniques for Construction Project Management—A Review

“…performance of the sector [3][4][5]; as well as the utilization of machine learning (ML) techniques [6][7][8]. LC's philosophy focuses on identifying and eliminating waste and maintaining a steady flow work.…”

Artificial Intelligence-Powered Computational Strategies in Selecting and Augmenting Data for Early Design of Tall Buildings with Outer Diagrids

“…The growing trend of integrating ML, a key subset of artificial intelligence (AI), into building design, has led to innovative architectural prototypes and enhanced evaluations of structural efficiency [14][15][16][17]. For a broader understanding of the current status, opportunities, and challenges of AI and ML in the construction industry, refer to references [18][19][20]. As mentioned above, advanced tools like parametric design software [21] not only provide insights into structural performance but also empower specialists to generate a multitude of high-rise building geometries using computer-aided design, which can then be translated into structural codes capable of producing comprehensive data regarding the building response, even under complex loading conditions.…”