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Alanjari, P, RazaviAlavi, S and AbouRizk, S (2015) Hybrid Genetic Algorithm-Simulation Optimization Method for Proactively Planning Layout of Material Yard Laydown. Journal of Construction Engineering and Management, 141(10).
de Athayde Prata, B, Pitombeira-Neto, A R and de Moraes Sales, C J (2015) An Integer Linear Programming Model for the Multiperiod Production Planning of Precast Concrete Beams. Journal of Construction Engineering and Management, 141(10).
de Oliveira, A L and Prudêncio, L R (2015) Evaluation of the Superficial Texture of Concrete Pavers Using Digital Image Processing. Journal of Construction Engineering and Management, 141(10).
- Type: Journal Article
- Keywords:
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001012
- Abstract:
When concrete paver is chosen as a material for paving, one of the most important requirements of the supplier is its surface texture. Surface texture is determined by both the concrete mix design and the type and setting of vibrocompression equipment used for production, which can generate different amounts of bugholes. Unlike other properties, such as compressive strength, there is currently no available method for evaluating surface texture in pavers. There are currently some proposed methods for the assessment and detection of defects in asphalt and concrete pavements, but there is no consensus on their suitability for being easily used to classify pavers regarding their surface texture. The choice of a supplier is still normally based on the visual assessment of this characteristic in samples provided by the supplier himself, and there is no guarantee that these samples will be representative of the material to be delivered at the job site. The present study proposes a method for the evaluation and classification of pavers’ surface texture based on the standard deviation obtained in the frequency histogram of digital grayscale images of pavers. The study proves that it is possible to use flatbed scanners to evaluate the surface texture if a previous image calibration using reflective standard targets is undertaken. The results obtained using the proposed method for the classification of samples possessing different surface textures correlate very well with the visual assessments of production experts. Additionally, it is a simple, fast, and low-cost method, and the results are not influenced by the operator. Finally, a classification of pavers’ surface texture based on digital image processing (DIP) results is proposed.
Heravi, G and Eslamdoost, E (2015) Applying Artificial Neural Networks for Measuring and Predicting Construction-Labor Productivity. Journal of Construction Engineering and Management, 141(10).
Ma, G, Gu, L and Li, N (2015) Scenario-Based Proactive Robust Optimization for Critical-Chain Project Scheduling. Journal of Construction Engineering and Management, 141(10).
Shealy, T and Klotz, L (2015) Well-Endowed Rating Systems: How Modified Defaults Can Lead to More Sustainable Performance. Journal of Construction Engineering and Management, 141(10).
Song, J, Song, D and Zhang, D (2015) Modeling the Concession Period and Subsidy for BOT Waste-to-Energy Incineration Projects. Journal of Construction Engineering and Management, 141(10).