Labor productivity modeling with neural networks

1996-01-01
Rowings Jr., James E.
Sönmez, Rifat
Regression analysis has been the common tool used in construction productivity studies, but in recent years, neural networks have been a successful alternative to regression analysis for other problems similar to construction labor productivity modeling. However, the potential capabilities of neural networks for construction labor productivity modeling have not been examined. This paper discusses the development of multivariate productivity models for concrete pouring by regression analysis and neural networks.

Suggestions

Labor market institutions and industrial performance: an evolutionary study
Kilicaslan, Yilmaz; Taymaz, Erol (2008-08-01)
This study investigates the impact of labor market institutions on industrial performance from a Schumpeterian perspective. We suggest that labor market institutions play a very important role in the process of creative destruction, because they may create an environment that encourages and enforces innovation, and help to reallocate resources, most importantly labor, through swift elimination of weak performers. We specifically look at the effects of the quantity of labor market regulations and inter-indus...
Prediction of Nonlinear Drift Demands for Buildings with Recurrent Neural Networks
Kocamaz, Korhan; Binici, Barış; Tuncay, Kağan (2021-09-08)
Application of deep learning algorithms to the problems of structural engineering is an emerging research field. Inthis study, a deep learning algorithm, namely recurrent neural network (RNN), is applied to tackle a problemrelated to the assessment of reinforced concrete buildings. Inter-storey drift ratio profile of a structure is a quiteimportant parameter while conducting assessment procedures. In general, procedures require a series of timeconsuming nonlinear dynamic analysis. In this study, an extensiv...
Adaptive evolution strategies in structural optimization: Enhancing their computational performance with applications to large-scale structures
Hasançebi, Oğuzhan (2008-01-01)
In this study the computational performance of adaptive evolution strategies (ESs) in large-scale structural optimization is mainly investigated to achieve the following objectives: (i) to present an ESs based solution algorithm for efficient optimum design of large structural systems consisting of continuous, discrete and mixed design variables; (ii) to integrate new parameters and methodologies into adaptive ESs to improve the computational performance of the algorithm; and (iii) to assess successful self...
Neural networks with piecewise constant argument and impact activation
Yılmaz, Enes; Akhmet, Marat; Department of Scientific Computing (2011)
This dissertation addresses the new models in mathematical neuroscience: artificial neural networks, which have many similarities with the structure of human brain and the functions of cells by electronic circuits. The networks have been investigated due to their extensive applications in classification of patterns, associative memories, image processing, artificial intelligence, signal processing and optimization problems. These applications depend crucially on the dynamical behaviors of the networks. In t...
Mass customizing the relations of design constraints for designer-built computational models
Ercan, Selen; Özkar, Mine; Department of Architecture (2010)
The starting motivation of this study is to develop an intuitively strong approach to addressing architectural design problems through computational models. Within the scope of the thesis, the complexity of an architectural design problem is modeled computationally by translating the design reasoning into parameters, constraints and the relations between these. Such a model can easily become deterministic and defy its purpose, if it is customized with pre-defined and unchangeable relations between the const...
Citation Formats
J. E. Rowings Jr. and R. Sönmez, “Labor productivity modeling with neural networks,” 1996, Accessed: 00, 2021. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=0029698397&origin=inward.