Seismic Demand for Low-Rise Reinforced Concrete Buildings of Islamabad–Rawalpindi Region (Pakistan)

Ahmad, Sohaib; Pilakoutas, Kypros; Khan, Qaiser uz Zaman; Mehboob, Saqib

doi:10.1007/s13369-017-2924-1

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…Some of the relevant studies include designing energy-efficient intelligent buildings [70], solid waste management [64], the impact of urbanization on ground water [71], seismic mapping [72], construction management [73,74], transportation issues [61], and crises in megaprojects [75]. In terms of concrete-related research, some relevant studies are focused on seismic demand for low-rise reinforced concrete buildings [76], the effects of adding coconut fiber to concrete [77], and enhancing the hardened properties of recycled concrete through synergistic incorporation of fiber reinforcement and silica fume [78]. However, a study focused on the prediction of properties of concrete in the region has not been conducted to date.…”

Section: Study Areamentioning

confidence: 99%

Using Multivariate Regression and ANN Models to Predict Properties of Concrete Cured under Hot Weather

et al. 2021

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Concrete is an important construction material. Its characteristics depend on the environmental conditions, construction methods, and mix factors. Working with concrete is particularly tricky in a hot climate. This study predicts the properties of concrete in hot conditions using the case study of Rawalpindi, Pakistan. In this research, variable casting temperatures, design factors, and curing conditions are investigated for their effects on concrete characteristics. For this purpose, water–cement ratio (w/c), in-situ concrete temperature (T), and curing methods of the concrete are varied, and their effects on pulse velocity (PV), compressive strength (fc), depth of water penetration (WP), and split tensile strength (ft) were studied for up to 180 days. Quadratic regression and artificial neural network (ANN) models have been formulated to forecast the properties of concrete in the current study. The results show that T, curing period, and moist curing strongly influence fc, ft, and PV, while WP is adversely affected by T and moist curing. The ANN model shows better results compared to the quadratic regression model. Furthermore, a combined ANN model of fc, ft, and PV was also developed that displayed higher accuracy than the individual ANN models. These models can help construction site engineers select the appropriate concrete parameters when concreting under hot climates to produce durable and long-lasting concrete.

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