Annotated Bibliography

1) Formoso, C.,  Soibelman, L., De Cesare, C., & Isatto, E. (July/August 2002). Material Waste in Building Industry: Main Causes and Prevention. Journal of Construction Engineering and Management, 316-325. doi: 10.1061/~ASCE!0733-9364~2002!128:4~316!

The author analyzed two studies carried out in Brazil about waste of materials and concluded that the main solution for this problem is an implementation of control systems, that is able to measure financial and non financial waste and optimize the production process, in construction companies.

2) Ghaboussi, J., Garret Jr., J. H., & Wu, X. (January 1991). Knowledge- Based Modeling of Material Behavior with Neural Networks. Journal of Engineering Mechanics, 117, 132-153. doi: 10.1061/(ASCE)0733-9399(1991)117:1(132)

In this paper, the author analyses the use of neural network to determine the behavior of concrete during the state of plane stress under monotonic biaxial loading and compressive uniaxial cycle loading. The author concluded that, through the use of this device, it is possible a better modeling of materials. 

3) Jaillon, L., Poon, C.S., & Chiang, Y.H. (January 2009). Quantifying the waste reduction potential of using prefabrication in building construction in Hong Kong. Waste Management, 29, 3009-320. DOI: 10.1016/j.wasman.2008.02.015

The author discuss about the amount of building waste generated in Hong Kong and governmental actions to reduce the percentage of waste. The author analyze the significant wastage reduction through the use of prefabricated materials.

4) Peddi, A. (n.d.).  Development of Human Pose Analyzing Algorithms for the Determination of Construction Productivity in Real-time. Retrieved from http://kuscholarworks.ku.edu/dspace/bitstream/1808/4283/1/umi-ku-2739_1.pdf

The author analyzes an automated on-site productivity measurement system which, through the capturing of a sequence of images, can provide instant feedback about the productivity of the construction. The author shows that it is possible to increase the productivity from this analysis.