AI Predicts Independent Construction Safety Outcomes from Universal Attributes

These pro-3 grams rely on patterns and inference, rather than explicit instructions, to achieve their aims [5]. ML in construction has been developed significantly since 1991 when [6] first discussed the potential of neural networks in construction engineering and management. Early examples of ML in construction include applications such as [7] where the AQ15 algorithm was applied to automatically learn the mapping between constructability (poor, good, excellent) and 7 predictors from a collection of 31 training examples; and [8] who applied decision trees and neural networks to a construction management database to identify the causes of delays. Many subsequent prediction applications applied support vector machines (SVMs), owing to their consistently high accuracy. These applications include [9], who accurately forecasted contractor prequalification using input variables such as financial strength and current workload; [10], who estimated building cost and loss risk from ten input variables; and [11], who detected concrete structural components in color images from actual construction sites. In the last 5 years, use of ML in construction has become far more widespread and the methods and applications used are far more diverse.