Using Hybrid Artificial Intelligence Optimization Method to Predict Construction Labour Productivity - Efekan, Efkan; Çelik, Tolga; Tokdemir, Onur Behzat (Springer Science and Business Media Deutschland GmbH, 2023)
Precise estimation of productivity is crucial for construction managers in making timely decisions. The aim is to propose a novel theoretical approach for predicting and optimizing Construction Labour Productivity (CLP). Numerous factors affecting CLP are the main challenge in modeling labor productivity. Accurate CLP prediction is required for effective decision-making before and during project execution. This article will explain the importance of artificial intelligence-based inference models on construction productivity and why hybrid embedded feature selection models, a new approach to increasing construction productivity, should be used more in integration with AI-based inference models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
International Symposium of the International Federation for Structural Concrete, fib Symposium 2023 -- 2023-06-05 through 2023-06-07 -- Istanbul -- 296169
Keywords: Artificial Intelligence; Construction Productivity; Hybrid Embedded Feature Selection; Optimization