Abstract
Productivity is described as the quantitative measure between the number of resources used and the output produced, generally referred to man‐hours required to produce the final product in comparison to planned man‐hours. Productivity is a key element in determining the success and failure of any construction project. Construction as a labour‐driven industry is a major contributor to the gross domestic product of an economy and variations in labour productivity have a significant impact on the economy. Attaining a holistic view of labour productivity is not an easy task because productivity is a function of manageable and unmanageable factors. Compound irregularity is a significant issue in modeling construction labour productivity. Artificial Neural Network (ANN) techniques that use supervised learning algorithms have proved to be more useful than statistical regression techniques considering factors like modeling ease and prediction accuracy. In this study, the expected productivity considering environmental and operational variables was modeled. Various ANN techniques were used including General Regression Neural Network (GRNN), Backpropagation Neural Network (BNN), Radial Base Function Neural Network (RBFNN), and Adaptive Neuro‐Fuzzy Inference System (ANFIS) to compare their respective results in order to choose the best method for estimating expected productivity. Results show that BNN outperforms other techniques for modeling construction labour productivity.
Highlights
Artificial Intelligence (AI) has been a powerful tool in the construction industry over the past decade
Circulating capital is any kind of capital that will be depleted during the course of a project, such as material and operating expenses, whereas fixed capital refers to any kind of capital that is not exhausted during the course of a project
Adaptive Neuro-Fuzzy Inference System (ANFIS) utilizes a hybrid learning algorithm which can model the relationship between predictor variables and respond variables based on expert knowledge by using neural network capabilities
Summary
Artificial Intelligence (AI) has been a powerful tool in the construction industry over the past decade. Total factor productivity is the most common construction productivity measurement technique where the output is measured against all inputs. In AI modeling, ANN models are the most common for developing labour construction productivity. AbouRizk et al [4] developed a two-stage ANN model for predicting labour productivity rates. Us, ANN techniques as compared to other conventional practices are more appropriate in modeling construction industry problems that demand analogy-based resolutions [17]. Because of ANN’s capability to draw the relationships between input and output provided via a training dataset, ANN is suitable for nonlinear problems where vague information, subjective judgment, experience, and surrounding conditions are key features, and traditional approaches are insufficient to calculate the complex input-output relationship necessary for predicting construction labour productivity. Development of each model and their results is presented
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