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Payam Shafigh

Researcher at University of Malaya

Publications -  98
Citations -  4618

Payam Shafigh is an academic researcher from University of Malaya. The author has contributed to research in topics: Compressive strength & Aggregate (composite). The author has an hindex of 31, co-authored 96 publications receiving 3199 citations. Previous affiliations of Payam Shafigh include Islamic Azad University.

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A new sustainable composite column using an agricultural solid waste as aggregate

TL;DR: In this article, a sustainable composite column by using of lightweight oil palm shell (OPS) concrete instead of conventional aggregate concrete is presented, which is about 15% lighter than a normal CFT column, with significantly higher specific energy absorption, structural efficiency and flexibility.
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Performance of High Strength Concrete Subjected to Elevated Temperatures: A Review

TL;DR: In this paper, the effect of elevated temperatures on the properties of high strength concrete (HSC) is reviewed and a number of different design standards for preparation of HSC are also presented and compared.
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Production of high-strength lightweight concrete using waste lightweight oil-palm-boiler-clinker and limestone powder

TL;DR: In this paper, an agricultural solid waste from the incineration process of solid wastes in the palm oil industry is used to make oil-palm-boiler clinker (OPBC).
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Thermophysical properties of sustainable cement mortar containing oil palm boiler clinker (OPBC) as a fine aggregate

TL;DR: In this article, the authors investigated the thermophysical properties in terms of thermal conductivity, heat capacity, thermal diffusivity, density, compressive strength, water absorption, and sorptivity of cement mortars containing different percentages of oil palm boiler clinker (OPBC).
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Appropriate drying shrinkage prediction models for lightweight concrete containing coarse agro-waste aggregate

TL;DR: In this paper, drying shrinkage strain behavior of normal-weight and lightweight aggregate concretes was compared with prediction models, and the results indicated that the EN1992 was the most precise model at early-ages as well as GL2000 at long-term ages.