Synergistic effects of hybrid microfibers on mechanical, thermal, and microstructural characterization of nanocomposites.

The use of geopolymers (GP) in cementitious composites provides a solution to reduce the significant carbon emissions associated with conventional cement production, thereby advancing environmentally friendly concrete construction practices. The promise of hybrid fiber-reinforced fly ash (FA)-based GP (HFGP) composites that combine microfibers and nanoparticles has not yet been fully comprehended. This research aims to enhance the mechanical and microstructural properties of HFGP blends by varying the proportion of nano calcium carbonate ( ).

Heat and mass transfer analysis of assisting and opposing radiative flow conveying ternary hybrid nanofluid over an exponentially stretching surface.

Access to dependable and environmentally friendly energy sources is critical to a country's economic growth and long-term development. As countries seek greener energy alternatives, the interaction of environmental elements, temperature, and sunlight becomes more critical in utilizing renewable energy sources such as wind and bioenergy. Solar power has received much attention due to extraordinary efficiency advances.

A multi-criteria evaluation and optimization of sustainable fiber-reinforced concrete developed with nylon waste fibers and micro-silica.

Nylon waste fibers similar to new nylon fibers possess high tensile strength and toughness; hence, they can be used as an eco-friendly discrete reinforcement in high-strength concrete. This study aimed to analyze the mechanical and permeability characteristics and life cycle impact of high-strength concrete with varying amounts of nylon waste fiber and micro-silica. The results proved that nylon waste fiber was highly beneficial to the tensile and flexural strength of concrete.

Review of Vibration Control Strategies of High-Rise Buildings.

Since the early ages of human existence on Earth, humans have fought against natural hazards for survival. Over time, the most dangerous hazards humanity has faced are earthquakes and strong winds. Since then and till nowadays, the challenges are ongoing to construct higher buildings that can withstand the forces of nature.

Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence.

Research has focused on creating new methodologies such as supervised machine learning algorithms that can easily calculate the mechanical properties of fiber-reinforced concrete. This research aims to forecast the flexural strength (FS) of steel fiber-reinforced concrete (SFRC) using computational approaches essential for quick and cost-effective analysis. For this purpose, the SFRC flexural data were collected from literature reviews to create a database.

Glass Fibers Reinforced Concrete: Overview on Mechanical, Durability and Microstructure Analysis.

Prior studies in the literature show promising results regarding the improvements in strength and durability of concrete upon incorporation of glass fibers into concrete formulations. However, the knowledge regarding glass fiber usage in concrete is scattered. Moreover, this makes it challenging to understand the behavior of glass fiber-reinforced concrete.

Compressive Strength of Steel Fiber-Reinforced Concrete Employing Supervised Machine Learning Techniques.

Recently, research has centered on developing new approaches, such as supervised machine learning techniques, that can compute the mechanical characteristics of materials without investing much effort, time, or money in experimentation. To predict the 28-day compressive strength of steel fiber-reinforced concrete (SFRC), machine learning techniques, i.e.

Integrated approach to evaluate unstable rocky slopes: case study of Aqabat Al-Sulbat road in Aseer Province, Saudi Arabia.

In this applied research work, the risk of rock instability in the Aqabat Al-Sulbat road section located in the north-west area of Aseer Province in Saudi Arabia was evaluated, and the primary natural trigger factors of rock slope instability on further environmental components (rock slope stability, road network, and urban areas) were estimated using satellite images (Landsat8), digital terrain models, and geoprocessing in geographical information systems software (classification, overlapping algorithms and production thematic mapping in Arctoolbox). Additionally, field geotechnical investigations testing and over-coring drilling sampling allowed the characterization of the section of road in terms of geological structure and environmental components (geology, morphology, road network, lineaments, and hydrology). As a result, rock slope instability vulnerability mapping was simulated using satellite imagery and geographical information systems (GIS) and ranking natural trigger factors using the combined fuzzy Delphi analytical hierarchic process with the technique for order performance by similarity to ideal solution (TOPSIS) as multiple-criteria decision-making (MCDM) techniques.