S
Susanto Teng
Researcher at Nanyang Technological University
Publications - 29
Citations - 806
Susanto Teng is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Silica fume & Properties of concrete. The author has an hindex of 12, co-authored 28 publications receiving 597 citations. Previous affiliations of Susanto Teng include Business International Corporation.
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Durability and mechanical properties of high strength concrete incorporating ultra fine Ground Granulated Blast-furnace Slag
TL;DR: In this paper, two concrete mixes with 450 and 520 kg/m 3 of ordinary Portland cement and two more mixes of equivalent total cementitious materials with 30% UFGGBS replacement were cast.
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Flexural behavior and durability properties of high performance hybrid-fiber-reinforced concrete
TL;DR: In this article, the flexural behavior and durability properties of high performance hybrid-fiber-reinforced concrete were investigated, and the results indicated that the incorporation of fibers notably improved the mechanical strength of the concrete, while the substitution of DHE steel fibers with HE steel fibers or PVA fibers led to a reduction in flexural performance.
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Durability Properties and Microstructure of Ground Granulated Blast Furnace Slag Cement Concrete
TL;DR: In this article, more than 200 samples with various water-cementitious material ratios and various replacement percentages of ground granulated blast-furnace slag (GGBS) were cast.
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Main Tension Steel in High Strength Concrete Deep andShort Beams
TL;DR: In this paper, the main steel ratio of reinforced concrete deep beams with cylinder compressive strength f 1 C generally exceeding 55 MPa (8000 psi) was tested under two-point symmetric top loading.
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Experiments on drying shrinkage and creep of high performance hybrid-fiber-reinforced concrete
TL;DR: In this paper, the authors evaluated the time-dependent creep and shrinkage properties of HPC as well as high performance hybrid-fiber-reinforced concretes (HPHyFRC) through experimental programs and the results will be compared with some common prediction models.