Publications

Great efforts are made to utilize waste in the construction industry for many different reasons, including the limited non-renewable sources of concrete co

Each year, millions of tons of household and industrial waste, including plastic, are responsible for harming the environment through burning, landfilling, or going to dump in open water. Waste plastic disposal is a critical issue faced by municipalities in modern cities. In addition, the development of hundreds of kilometers of roads worldwide leads to the consumption of vast quantities of raw materials and the depletion of valuable natural resources. The high expense of virgin polymers and rising plastic waste have shifted the research focus toward green pavements that utilize waste plastics. Therefore, this research aims to investigate the use of recycled plastic waste as a binder modifier for Stone Mastic Asphalt mix (SMA). The high-temperature performance of the Recycled Polyethylene Modified Binder (RPMB) has been evaluated with various percentage dosages to satisfy the environment of the Gulf region. In addition, the impact of RPMB on the SMA mix was studied through three performance tests, drain down resistance, moisture damage resistance, and rutting performance. The results showed continuous improvement in rheological properties with increased recycled polyethylene (RP) content by up to 6%. The RP enhances the binder properties such as the rutting parameter, non-recoverable compliance, and viscosity to meet the high-temperature performance requirement for heavy traffic (UPG 76 H) of most roads in the gulf environment. However, the results showed that RPMB could not meet the recovery requirement set by AASHTO TP 70. That is due to RP's plastomer nature. Therefore, it is necessary to supplement some proportion of the RP with an elastomeric polymer. Further, the results of SMA mix properties with RPMB showed remarkable improvement over than control mixture in terms of resistance to drain down, moisture damage, and rutting by 55.1%, 52.6%, and 96.4, respectively.

More than half of the CO2 emissions during the manufacturing of ordinary Portland cement (OPC) occur due to the calcination of calcium carbonate in additio

This paper uses a quasi-3D refined shear deformation plate theory to investigate the buckling response of supported functionally graded (FG) porous sandwich (SW) plates. The material properties of FG sandwich porous plates (SWPPs) are defined by a modified mixing rule, supplemented by an additional term representing the porosity in the thickness of the FG layer. By incorporating indefinite integral variables, the number of unknowns and governing equations of the current theory is reduced, making it easier to use. Three common types of sandwich porous plates are considered in the analysis: an isotropic FG porous plate, an FG face plate with a homogeneous core, and a homogeneous face plate with an FG core. The current method considers both normal and shear deformations and satisfies the transverse shear stress-free boundary conditions at the top and bottom surfaces of the plate. The equilibrium equations are derived using the principle of virtual displacements. These equations are then solved using the Navier-type method. The influence of thickness strain and several parameters, including mechanical loads, porosity coefficients, layer thickness ratios, geometric parameters, and gradient indices, are studied. The numerical results of the present theory are similar to the predictions of quasi-3D theories with more unknowns, in addition to being more accurate than those obtained by higher-order shear deformation theories. The findings support the idea that the suggested theory offers an easy and effective method for predicting the buckling behavior of FG porous sandwich plates. The results show that, without taking into account transverse normal deformations, the plate is softer and the critical buckling load decreases. On the other hand, with transverse normal deformations, the plate is stiffer, which increases the critical buckling load.

Landslide data and implementation of a hybrid clustering - neural network algorithm for landslide susceptibility assessment in Matlab.

This study investigates the use of machine learning techniques to predict the unconfined compressive strength (UCS) of both stabilized and unstabilized soils. This research focuses on analyzing key soil parameters that significantly impact the strength of earth materials, such as grain size distribution and Atterberg limits. Machine learning models, specifically Support Vector Regression (SVR) and Decision Trees (DT), were employed to predict UCS. Model performance was evaluated using key metrics, including the Pearson coefficient of correlation (r2), coefficient of determination (R2), mean absolute error, and root mean square error. The findings reveal that, for unstabilized soils, both SVR and DT models exhibit remarkable performance with r2 values of 0.9948 and 0.9947, respectively, with the DT model surpassing the SVR model in estimating UCS. Validation was conducted using data from four types of locally available soils in the Najd region of Saudi Arabia, although some disparities were noted between actual and predicted results due to limitations in the training data. The analysis indicates that, for unstabilized soil, grain size distribution and moisture content during testing are primary influencers of strength, whereas, for stabilized soil, factors such as stabilizer type and content, as well as density and moisture during testing, are pivotal. This research demonstrates the potential of machine learning for developing a robust classification system to enhance earth material utilization.

This study proposes efficient exterior concrete walls (consisting of blocks, mortars, and plasters) to reduce the heat transfer to or from buildings for minimizing energy consumption due to air conditioning or heating processes. Different blocks were treated by partially or totally replacing coarse and fine aggregates with insulation materials. Mortars and plasters were also treated by partially replacing sand with certain insulation materials. A control block (without treatment) was also produced with three-row rectangular cavities to reduce the effective thermal conductivity. Experimental tests were performed to determine the thermal conductivity of blocks, mortars, and plasters, and to validate a developed three-dimensional model (using ANSYS-Fluent program). The model results showed a substantial increase in model accuracy (about 15%) by involving the estimation of radiation heat transfer in the block cavities. The thermal and economic performance of treated masonry walls showed that the optimal performance was obtained from a wall having volcanic scoria-treated blocks (200-mm thick) and 15% vermiculite-treated mortar (10-mm thick). The thermal performance was enhanced by 15% and 318% (for thermal resistance increase) and 13% and 76% (for heat flux reduction) more than those of the control and market walls, respectively. Adding 15% vermiculite-treated plaster (2 × 15-mm thick) to the wall enhanced the thermal performance remarkably. The total cost of the improved wall (having the highest thermal resistance) was reduced by 1.39 and 3.67 times over the control and market walls (Dhahran, Saudi Arabia), respectively. This improved wall is a promising candidate for exterior walls in residential and industrial buildings.

No abstract is provided for this article.

The incorporation of graphene-based materials into cement composites is one of many interesting nano-reinforcement techniques. However, the conventional production of graphene materials usually requires large quantities of solvent with energy-intensive mixing, which in turn restricts their commercial viability in cement and concrete applications. In this study, an approach for production of flash graphene (FG) from motor oil soot and diesel particulate using a Joule heating system was developed. A high-quality FG was obtained, as evident from Raman spectroscopy analysis. The FG product was added to a mixture to reinforce its microstructure. The results of the mechanical tests conducted on the cement mortar reinforced by admixing 0.1 wt % FG showed an increase in compressive, tensile, and flexural strengths and modulus of elasticity by 38%, 27%, 27%, and 34%, respectively, after curing for 28 days. The durability characteristics in terms of water absorption showed a slightly higher resistance of the FG-reinforced mortar to water penetration. The drying shrinkage of the FG-reinforced mortar was like that of the control mixture. A molecular dynamics simulation was performed on the cured FG-reinforced cement mortar to find that the Ca–Si interactions in the hydrated cement phase were boosted by the presence of FG, in addition to the strong interaction between the Ca and FG sheets. This study could contribute toward developing strong and sustainable nano-reinforced cementitious composites using graphene materials derived from inexpensive carbon and waste sources.