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. This paper is a detailed review of various vibration control strategies used to enhance the dynamical response of high-rise buildings. Hence, different control strategies studied and used in civil engineering are presented with illustrations of real applications if existing. The main aim of this review paper is to provide a reference-rich document for all the contributors to the vibration control of structures. This paper will clarify the applicability of specific control strategies for high-rise buildings. It is worth noting that not all the studied and investigated methods are applicable to high-rise buildings; a few of them remain limited by many parameters such as cost-effectiveness and engineering-wise installation and maintenance.

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 ( n - C a C O 3 ). The production of HFGP involved the use of two types of fibers: 1% carbon fibers and 0.5% basalt fibers. To achieve HFGP blends with a consistent fiber ratio, we incorporated four different levels of n - C a C O 3 , comprising 1%, 2%, 3%, and 4% of the mixture. The analysis of fractured samples encompassed microstructural and mineralogical characterization, which was conducted using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD) analysis. The results unveiled that the HFGP blend containing 3% n - C a C O 3 exhibited the highest levels of hardness, compressive strength, toughness modulus, and flexural strength while the use of 2% n - C a C O 3 produced the highest results for fracture toughness and impact strength. SEM analysis illustrated that n - C a C O 3 had a significant positive impact on the microstructure of GP. A considerable rise in hump intensity between 20 and 40 °C ( 2 θ ) was also seen in the XRD examination, indicating that calcium silicate hydrate (CSH) had formed after the primary binder, such as sodium aluminosilicate hydrate (NASH), had been present. The stretching of O-H bonds in water molecules was also seen in the HFGP spectra at 3399, 3436, 3436, and 3438 cm-1. Due to the higher water content in the HFGP network, which may influence the material's strength, these bands were more apparent and larger in specimens with additions of nanoparticles and hybrid fibers.

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. The incorporation of a 1% volume of nylon waste fiber caused net improvements of 50% in the flexural strength of concrete. At the combined addition of 0.5% volume fraction of nylon fiber and 7.5% micro-silica, splitting tensile and flexural strength of high-strength concrete experienced net improvements of 49% and 55%, respectively. Nylon fiber-reinforced concrete exhibited a ductile response and high flexural toughness and residual strength compared to plain concrete. A low volume fraction of waste fibers was beneficial to the permeability resistance of high-strength concrete against water absorption and chloride permeability, while a high volume (1% by volume fraction) of fiber was harmful to the permeability-resistance of concrete. For the best mechanical performance of high-strength concrete, 0.5% nylon waste fiber can be used with 7.5% micro-silica. The use of micro-silica minimized the negative effect of the high volume of fibers on the permeability resistance of high-strength concrete. The addition of nylon waste fibers (at 0.25% and 0.5% volume) and micro-silica also reduced carbon emissions per unit strength of 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., individual and ensemble models, were considered. For this study, two ensemble approaches (SVR AdaBoost and SVR bagging) and one individual technique (support vector regression (SVR)) were used. Coefficient of determination (R2), statistical assessment, and k-fold cross validation were carried out to scrutinize the efficiency of each approach used. In addition, a sensitivity technique was used to assess the influence of parameters on the prediction results. It was discovered that all of the approaches used performed better in terms of forecasting the outcomes. The SVR AdaBoost method was the most precise, with R2 = 0.96, as opposed to SVR bagging and support vector regression, which had R2 values of 0.87 and 0.81, respectively. Furthermore, based on the lowered error values (MAE = 4.4 MPa, RMSE = 8 MPa), statistical and k-fold cross validation tests verified the optimum performance of SVR AdaBoost. The forecast performance of the SVR bagging models, on the other hand, was equally satisfactory. In order to predict the mechanical characteristics of other construction materials, these ensemble machine learning approaches can be applied.

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. Three ensembled models, i.e., Gradient Boosting (GB), Random Forest (RF), and Extreme Gradient Boosting (XGB) of machine learning techniques, were considered to predict the 28-day flexural strength of steel fiber-reinforced concrete. The efficiency of each method was assessed using the coefficient of determination (R2), statistical evaluation, and k-fold cross-validation. A sensitivity approach was also used to analyze the impact of factors on predicting results. The analysis showed that the GB and RF models performed well, and the XGB approach was in the acceptable range. Gradient Boosting showed the highest precision with an R2 of 0.96, compared to Random Forest (RF) and Extreme Gradient Boosting (XGB), which had R2 values of 0.94 and 0.86, respectively. Moreover, statistical and k-fold cross-validation studies confirmed that Gradient Boosting was the best performer, followed by Random Forest (RF), based on reduced error levels. The Extreme Gradient Boosting model performance was satisfactory. These ensemble machine learning algorithms can benefit the construction sector by providing fast and better analysis of material properties, especially for fiber-reinforced concrete.

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. Additionally, many rock layer discontinuity stations were implemented to evaluate rock slope instabilities, and these were visualized using the Dips program and combined with modeling using 3DEC software to predict rock slope failure based on the distinct element method (DEM) at a small scale. Thereafter, safety factors were computed depending on these previous geospatial data. Finally, vulnerability index mapping was combined with rock instability risk mapping for the Aqabat Al-Sulbat road. Within the framework of sustainable development, these results can be used to inform the urban planning of the municipality of Aseer Province.