Modeling of success factors of using PU coats in concrete construction projects.

Polyurea coatings are well recognized for their remarkable protective properties, making them highly appropriate for practical use in the field of concrete building. The use of polyurea coatings in the concrete building business is currently constrained, despite its prevalent application in industrialized nations. The limited use may be ascribed to ambiguities about the determinants of effective implementation in this particular setting, as well as the dearth of extensive study in the realm of new building materials. The primary objective of this research is to assess and conceptualize the key determinants linked to the use of polyurea coatings in concrete building endeavors. Utilizing a quantitative research approach, a comprehensive literature analysis was conducted to identify a total of 21 probable success variables. The reliability of the questionnaire was established by the administration of a pilot survey, and afterwards, an exploratory factor analysis (EFA) was performed to enhance the clarity and precision of the underlying components. The researchers used structural modeling (SEM) approaches to develop a robust model using the primary data obtained from the questionnaire survey. The EFA revealed the presence of five unique constructs that have an impact on the effectiveness of polyurea coatings in concrete building projects. These constructions comprise several characteristics, including environmental considerations, functional requirements, protective properties, execution processes, and creative elements. The significance and relevance of this research are shown by the validation of the study's results using SEM. The study makes a valuable contribution towards the progression of polyurea coating use within the concrete building sector.

An analysis of LULC changes for understanding the impact of anthropogenic activities on food security: a case study of Dudhganga watershed, India.

Although the Dudhganga watershed is the primary water and food resource of the Kashmir Valley, it has undergone significant changes in food resources and strategies due to rampant urbanization in the area over the past 20 years. This urbanization has had a profound impact on the watershed and has also affected land use and land cover (LULC) patterns and environmental changes. The objective of this study is to investigate the effects of urban development on food security parameters in the Dudhganga watershed area, India, from 2000 to 2020, by evaluating LULC changes. Additionally, the study aims to examine the relationship between climate changes and LULC indices, such as the Modified Normalized Difference Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI). The results indicate a 21.66% increase in barren areas, at the expense of snow-covered lands, during the 2000-2020 period. The primary land cover transition observed is towards barren areas. The predictions for LULC in 2030 highlight the need for careful management of land use and climate changes in the study area. This study can assist local government officials in reassessing food strategies by identifying areas where urban expansion should be controlled and climate impacts minimized, to prevent local hunger and ecological degradation. Therefore, the development of systematic urban planning approaches and mitigation of climate change sources are crucial. Furthermore, the adoption of advanced agricultural technology should be considered to mitigate the impact of urban expansion.

Hybrid Deep Learning Approach for Stress Detection Using Decomposed EEG Signals.

Stress has an impact, not only on a person's physical health, but also on the ability to perform at the workplace in daily life. The well-established relation between psychological stress and its pathogeneses highlights the need for detecting psychological stress early, in order to prevent disease advancement and to save human lives. Electroencephalography (EEG) signal recording tools are widely used to collect these psychological signals/brain rhythms in the form of electric waves. The aim of the current research was to apply automatic feature extraction to decomposed multichannel EEG recordings, in order to efficiently detect psychological stress. The traditional deep learning techniques, namely the convolution neural network (CNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU) and recurrent neural network (RNN) models, have been frequently used for stress detection. A hybrid combination of these techniques may provide improved performance, and can handle long-term dependencies in non-linear brain signals. Therefore, this study proposed an integration of deep learning models, called DWT-based CNN, BiLSTM, and two layers of a GRU network, to extract features and classify stress levels. Discrete wavelet transform (DWT) analysis was used to remove the non-linearity and non-stationarity from multi-channel (14 channel) EEG recordings, and to decompose them into different frequency bands. The decomposed signals were utilized for automatic feature extraction using the CNN, and the stress levels were classified using BiLSTM and two layers of GRU. This study compared five combinations of the CNN, LSTM, BiLSTM, GRU and RNN models with the proposed model. The proposed hybrid model performed better in classification accuracy compared to the other models. Therefore, hybrid combinations are appropriate for the clinical intervention and prevention of mental and physical problems.

Assessment of Waste Marble Powder on the Mechanical Properties of High-Strength Concrete and Evaluation of Its Shear Strength.

Currently, the costs of building materials, especially cement, are increasing. Waste marble powder (WMP) could be used as a cement replacement material to produce environmentally friendly concrete to help preserve resources and reduce environmental pollution. The study's goals are (1) to evaluate the effects of using marble powder in place of cement in high-strength concrete (HSC) on the material's mechanical properties and durability characteristics. (2) The study is expanded to assess the effect of using partial WMP on the shear behavior of HSC beams under static loads. Eight half-scale simply supported reinforced beams with and without WMP have been tested. Each beam's cross-section was 120 × 200 mm, and each beam had a total length of 1000 mm. The ratios of the used WMP were 0%, 2.5%, 5%, 7.5% by weight, and two different stirrup ratios, 0% and 0.47%, were used. When applied to HSC beams with and without WMP, the shear strength provisions of two of the most used codes, such as the locally used Egyptian Code (ECP 207) and the internationally used American Concrete Institute's (ACI-2019), were examined. Using the ABAQUS software, the experimental results were compared to the findings of the nonlinear finite element analysis. The results established that partial replacement of cement by WMP led to increases in the concrete's compressive and tensile strengths of about 15% and 16%, respectively. When tested specimens were exposed to acid attack, there were slight losses in weight and compressive strength (1.25% to 2.47%) for both with and without the addition of WMP. Both the concrete with and without WMP showed the same level of water absorption. Additionally, WMP led to an enhancement in the shear capacities for all beams. Increasing the WMP ratio from 0% to 2.5%, 5%, and 7.5% increased the shear capacity by about 13%, 20%, and 28%, respectively, for beams without stirrups, while for beams with stirrups, the shear capacity improved by 12%, 19%, and 25%, respectively. The enhancement in the beams' shear capacities could be attributed to the advanced concrete matrix produced by WMP's extremely small particle size.

Development of Fibre-Reinforced Cementitious Mortar with Mineral Wool and Coconut Fibre.

Globally, as human population and industries grow, so does the creation of agricultural, industrial, and demolition waste. When these wastes are not properly recycled, reused, or disposed of, they pose a threat to the environment. The importance of this study lies in the beneficial use of coconut fibre and mineral wool in the form of fibres in cement mortar production. This study examines the use of coconut and mineral wool fibres in the production of fibre-reinforced mortar. Five different mortar mixtures were prepared, having one control mortar along with four fibre-reinforced mortars. The control mortar is denoted as CM while 1% and 1.5% of mineral wool are incorporated into this mortar mix and denoted as RMM-1.0 and RMM-1.5, respectively. Additionally, the mortar sample configurations contain 1% and 1.5% coconut fibers, designated as RCM-1.0 and RCM-1.5. These samples were subjected to different strength and durability tests to determine their suitability for use in mortar production. The testing findings show that mortar containing 1.5% mineral wool has better compared flexural strength and durability properties. The investigation results will form part of the database for the efficient utilization of natural and waste fibres in the construction and building sectors.

Shear Strength of Nano Silica High-Strength Reinforced Concrete Beams.

In this study, the shear strength of sixteen full-scale over-reinforced concrete beams with and without nano silica (NS), constructed from high-strength concrete (HSC), was investigated both experimentally and analytically. Nano silica was used as a partial replacement for Portland cement. According to the NS ratio, the tested beams were divided into four groups: 0%, 1%, 2%, and 3%. Shear span to effective depth (a/d) ratios of 1.5 and 2.5 were used in each group, and two different stirrups ratios (ρv) were employed as 0% and 0.38%. The shear strength provisions used by some international codes, such as the American Concrete Institute (ACI-2019), the Eurocode 2 (EC-2), and the Egyptian Code (ECP 207), were examined when applied to HSC beams with and without NS. The most important factors to consider were the effect of using NS on the shear span to effective depth (a/d) ratio and the shear strength of the beams with and without stirrups. The experimental results were validated using a nonlinear finite element analysis using the computer program ABAQUS. The experimental results showed that increasing the NS ratio reduced the number of cracks, and increased the cracks spacing, as well as reducing crack width. In specimens without stirrups, these effects were more obvious. A rise in the (a/d) ratio increased the number of cracks along the beam length, notably in the mid-span region. For specimens without stirrups and with an (a/d) of 1.5, raising NS from 0% to 1%, 2%, and 3% increased the ultimate load by 13%, 30%, and 39%, respectively, whereas for specimens with an (a/d) of 2.5, the ultimate load increased with approximately the same increase as that in beams with an (a/d) of 1.5 due to using NS. Additionally, the addition of NS to concrete boosted the contribution of the concrete to the shear strength, as shown by the results of beams without stirrups. For specimens with stirrups and an (a/d) of 1.5, raising NS from 0% to 1%, 2%, and 3% increased the ultimate load by 8%, 21%, and 30%, respectively. Additionally, for specimens with stirrups and an (a/d) of 2.5, the ultimate load increased with approximately the same increase as that in beams with stirrups and an (a/d) of 1.5 due to using NS. The test findings indicate that the shear strength calculated using the equations of the ACI 318-19 is more conservative than EC-2 and ECP 207 for NS concrete beams. The finite element program ABAQUS may be successfully used to predict the shear strength of NS concrete beams.

Microstructural Evolution, Tensile Failure, Fatigue Behavior and Wear Properties of Al2O3 Reinforced Al2014 Alloy T6 Heat Treated Metal Composites.

The paper focused on an experimental study on the microstructural, mechanical, and wear characteristics of 15 wt.% alumina (Al2O3) particulates with an average particle size of 20 µm, reinforced in Al2014 alloy matrix composite as-cast and heat-treated samples. The metal matrix composite (MMC)samples were produced via a novel two-stage stir-casting technique. The fabricated composite samples were subjected to evaluate hardness, tensile strength, fatigue behavior and wear properties for both as cast and T6 heat-treated test samples. The Al2014 alloy and Al2014-15 wt.% Al2O3 MMCs were in solution for 1 h at a temperature of 525 °C, quenched instantly in cold water, and then artificially aged for 10 h at a temperature of 175 °C. SEM and X-ray diffraction analyses were used to investigate the microstructure and dispersion of the reinforced Al2O3 particles in the composite and the base alloy Al2014. The obtained results indicated that the hardness, tensile and fatigue strength and wear resistance increased when an amount of Al2O3 particles was added, compared to the as-cast Al2014 alloy and it was observed that after subjecting the same composite samples to heat treatment, there was further enhancement in the mechanical and wear properties in the Al2014 matrix alloy and Al2014-15 wt.% Al2O3 composite samples.

Experimental Study on Recentering Behavior of Precompressed Polyurethane Springs.

Traditional seismic design has a limitation in that its performance is reduced by significant permanent deformation after plastic behavior under an external load. The recentering characteristics of smart materials are considered to be a means to supplement the limitations of conventional seismic design. In general, the recentering characteristics of smart materials are determined by their physical properties, whereas polyurethane springs can regulate the recentering characteristics by controlling the precompression strain. Therefore, in this study, 160 polyurethane spring specimens were fabricated with compressive stiffness, specimen size, and precompression strain as design variables. The compression behavior and precompression behavior were studied by performing cyclic loading tests on a polyurethane spring. The maximum stress and maximum strain of the polyurethane spring showed a linear relationship. Precompression and recentering forces have an almost perfect linear relationship, and the optimal level of precompression at which residual strain does not occur was derived through regression analysis. Additionally, a prediction model for predicting recentering force based on the linear relationship between precompression and recentering force was presented.

Seismic Performance Evaluation According to HSS and CFST Columns of 3D Frame Buildings with Rubber Friction Bearing (RFB).

This study has been conducted to observe nonlinear time history analysis of a 3D-office building frame where performance has been examined in the presence of base isolation and a bracing system. This steel structure has an underground story surrounded by stiff well-graded sand and is assumed to be located in an intense seismic area. The static and dynamic experimental performance of a Rubber Friction Bearing (RFB) has been considered, and an equivalent numerical model has been used in finite element software, which provides a satisfactory relationship between experimental and numerical prediction. The results show that the story drift and post-earthquake damage of the frame reduced significantly due to the presence of RFB devices. These isolators are most effective in moderate earthquakes. The presence of a minimum number of Steel Buckling Restrained Braces (BRBs) systems improve structural performance under moderate and strong ground motions by reducing story drift and residual damage. Hollow Steel Section (HSS) and Concrete-Filled Steel Tube (CFST) sections have been used in the simulation process, and it was found that the HSS system is susceptible to damage even if both seismic protection systems have been considered. The findings provide important conclusions to select suitable seismic protection for this type of structure, which is limited by simulation study due to the absence of experimental observation.

Phase-Change Materials in Concrete: Opportunities and Challenges for Sustainable Construction and Building Materials.

The use of phase-change materials (PCM) in concrete has revealed promising results in terms of clean energy storage. However, the negative impact of the interaction between PCM and concrete on the mechanical and durability properties limits field applications, leading to a shift of the research to incorporate PCM into concrete using different techniques to overcome these issues. The storage of clean energy via PCM significantly supports the UN SDG 7 target of affordable and clean energy. Therefore, the present study focuses on three aspects: PCM type, the effect of PCM on concrete properties, and connecting the outcome of PCM concrete composite to the United Nations sustainable development goals (UN SDGs). The compensation of reduction in strength of PCM-contained concrete is possible up to some extent with the use of nanomaterials and supplementary cementitious materials. As PCM-incorporated concrete is categorized a type of building material, the large-scale use of this material will affect the different stages associated with building lifetimes. Therefore, in the present study, the possible amendments of the different associated stages of building lifetimes after the use of PCM-incorporated concrete are discussed and mapped in consideration of the UN SDGs 7, 11, and 12. The current challenges in the widespread use of PCM are lower thermal conductivity, the trade-off between concrete strength and PCM, and absence of the link between the outcome of PCM-concrete composite and UN SDGs. The global prospects of PCM-incorporated concrete as part of the effort to attain the UN SDGs as studied here will motivate architects, designers, practicing engineers, and researchers to accelerate their efforts to promote the consideration of PCM-containing concrete ultimately to attain net zero carbon emissions from building infrastructure for a sustainable future.

Punching Shear Behavior of Slabs Made from Different Types of Concrete Internally Reinforced with SHCC-Filled Steel Tubes.

The punching shear failure of reinforced concrete (RC) flat slabs is an undesirable type of failure, as it is sudden and brittle. This paper presents an experimental and numerical study to explore the behavior of flat slabs made of different types of concrete under the influence of punching shear. Experimental tests were carried out on four groups of flat slabs, each group representing a different type of concrete: ordinary normal concrete (NC), high-strength concrete (HSC), strain-hardening cementitious composite concrete (SHCC), and ultra-high-performance fiber concrete (UHPFC). Each group consisted of six slabs, one representing an unreinforced control slab other than the reinforcement of the bottom mesh, and the others representing slabs internally reinforced with SHCC-filled steel tubes and high-strength bolts. An analytical equation was used to predict the punching shear capacity of slabs internally reinforced using steel assemblies. A numerical model was proposed using the ABAQUS program, and was validated by comparing its results with our experimental results. Finally, a case study was performed on large-scale slabs. The results showed that using steel assemblies inside NC slabs increased the slab's punching shear capacity but does not completely prevent punching shear failure. Internally unreinforced slabs made of UHPFC and SHCC were able to avoid punching shear failure and collapse in a ductile bending pattern due to the high compressive and tensile strength of these types of concrete. The proposed analytical method succeeded in predicting the collapse load of slabs reinforced with steel assemblies with a difference not exceeding 9%.

Structural Retrofitting of Corroded Reinforced Concrete Beams Using Bamboo Fiber Laminate.

Corrosion creates a significant degradation mechanism in reinforced concrete (RC) structures, which would require a high cost of maintenance and repair in affected buildings. However, as the cost of repairing corrosion-damaged structures is high, it is therefore pertinent to develop alternative eco-friendly and sustainable methods. In this study, structural retrofitting of corroded reinforced concrete beams was performed using bamboo fiber laminate. Three reinforced normal weight concrete beams were produced, two of which were exposed to laboratory simulated corrosion medium, and the remaining one sample served as control. Upon completion of the corrosion cycle, one of the two corroded beams was retrofitted externally with a prefabricated bamboo fiber laminate by bonding the laminate to the beam surface with the aid of an epoxy resin. The three beams were subjected to loading on a four-point ultimate testing machine, and the loads with corresponding deflections were recorded through the entire load cycle of the beams. Finally, the mass loss of embedded steel reinforcements was determined to measure the effect of corrosion on the beams and the steel. The result showed that corroded beams strengthened with bamboo laminates increase the bearing capacity. Using a single bamboo laminate in the tensile region of the corroded beam increased the ultimate load capacity of the beam up to 21.1% than the corroded beam without retrofit. It was demonstrated in this study that the use of bamboo fiber polymer for strengthening destressed RC beams is a more sustainable approach than the conventional synthetic fibers.

Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic.

Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.

A Prediction Model for the Calculation of Effective Stiffness Ratios of Reinforced Concrete Columns.

Nonlinear dynamic analyses of reinforced concrete (RC) frame buildings require the use of effective stiffness of members to capture the effect of cracked section stiffness. In the design codes and practices, the effective stiffness of RC sections is given as an empirical fraction of the gross stiffness. However, a more precise estimation of the effective stiffness is important as it affects the distribution of forces and various demands and response parameters in nonlinear dynamic analyses. In this study, an evolutionary computation method called gene expression programming (GEP) was used to predict the effective stiffness ratios of RC columns. Constitutive relationships were obtained by correlating the effective stiffness ratio with the four mechanical and geometrical parameters. The model was developed using a database of 226 samples of nonlinear dynamic analysis results collected from another study by the author. Subsequent parametric and sensitivity analyses were performed and the trends of the results were confirmed. The results indicate that the GEP model provides precise estimations of the effective stiffness ratios of the RC frames.

Optimizing the De-Noise Neural Network Model for GPS Time-Series Monitoring of Structures.

The Global Positioning System (GPS) is recently used widely in structures and other applications. Notwithstanding, the GPS accuracy still suffers from the errors afflicting the measurements, particularly the short-period displacement of structural components. Previously, the multi filter method is utilized to remove the displacement errors. This paper aims at using a novel application for the neural network prediction models to improve the GPS monitoring time series data. Four prediction models for the learning algorithms are applied and used with neural network solutions: back-propagation, Cascade-forward back-propagation, adaptive filter and extended Kalman filter, to estimate which model can be recommended. The noise simulation and bridge's short-period GPS of the monitoring displacement component of one Hz sampling frequency are used to validate the four models and the previous method. The results show that the Adaptive neural networks filter is suggested for de-noising the observations, specifically for the GPS displacement components of structures. Also, this model is expected to have significant influence on the design of structures in the low frequency responses and measurements' contents.

Investigation of Key Parameters of Rock Cracking Using the Expansion of Vermiculite Materia.

The demand for the development of underground spaces has been sharply increased in lieu of saturated ground spaces because the residents of cities have steadily increased since the 1980s. The traditional widely used excavation methods (i.e., explosion and shield) have caused many problems, such as noise, vibration, extended schedule, and increased costs. The vibration-free (and explosion-free) excavation method has currently attracted attention in the construction site because of the advantage of definitively solving these issues. For such reason, a new excavation method that utilizes the expansion of vermiculite with relatively fewer defects is proposed in this study. In general, vermiculite materials are rapidly expanded in volume when they receive thermal energy. Expansion pressure can be produced by thermal expansion of vermiculite in a steel tube, and measured by laboratory tests. The experimental tests are performed with various influencing parameters in an effort to seek the optimal condition to effectively increase expansion pressure at the same temperature. Then, calibrated expansion pressure is estimated, and compared to each model. After analyzing test results for expansion pressure, it is verified that vermiculite expanded by heat can provide enough internal pressure to break hard rock during tunneling work.

Engineering Behavior and Characteristics of Wood Ash and Sugarcane Bagasse Ash.

Biomasses are organic materials that are derived from any living or recently-living structure. Plenty of biomasses are produced nationwide. Biomasses are mostly combusted and usually discarded or disposed of without treatment as biomass ashes, which include wood and sugarcane bagasse ashes. Thus, recycling or treatment of biomass ashes leads to utilizing the natural materials as an economical and environmental alternative. This study is intended to provide an environmental solution for uncontrolled disposal of biomass ashes by way of recycling the biomass ash and replacing the soils in geotechnical engineering projects. Therefore, in this study, characteristic tests of wood and sugarcane bagasse ashes that are considered the most common biomass ashes are conducted. The test of chemical compositions of biomass ashes is conducted using energy dispersive X-ray spectroscopy (EDS), and Scanning Electron Microscope (SEM), and heavy metal analysis is also conducted. Engineering behaviors including hydraulic conductivity, constrained modulus and shear modulus are examined. Also, coal fly ash Class C is used in this study for comparison with biomass ashes, and Ottawa 20/30 sands containing biomass ashes are examined to identify the soil replacement effect of biomass ashes. The results show that the particle sizes of biomass ashes are halfway between coal fly ash Class C and Ottawa 20/30 sand, and biomass ashes consist of a heterogeneous mixture of different particle sizes and shapes. Also, all heavy metal concentrations were found to be below the US Environmental Protection Agency (EPA) maximum limit. Hydraulic conductivity values of Ottawa 20/30 sand decrease significantly when replacing them with only 1%-2% of biomass ashes. While both the constrained modulus and shear modulus of biomass ashes are lower than Ottawa 20/30 sand, those of mixtures containing up to 10% biomass ashes are little affected by replacing the soils with biomass ashes.

Investigation on the Cyclic Response of Superelastic Shape Memory Alloy (SMA) Slit Damper Devices Simulated by Quasi-Static Finite Element (FE) Analyses.

In this paper, the superelastic shape memory alloy (SMA) slit damper system as an alternative design approach for steel structures is intended to be evaluated with respect to inelastic behavior simulated by refined finite element (FE) analyses. Although the steel slit dampers conventionally used for aseismic design are able to dissipate a considerable amount of energy generated by the plastic yielding of the base materials, large permanent deformation may occur in the entire structure. After strong seismic events, extra damage repair costs are required to restore the original configuration and to replace defective devices with new ones. Innovative slit dampers fabricated by superelastic SMAs that automatically recover their initial conditions only by the removal of stresses without heat treatment are introduced with a view toward mitigating the problem of permanent deformation. The cyclically tested FE models are calibrated to experimental results for the purpose of predicting accurate behavior. This study also focuses on the material constitutive model that is able to reproduce the inherent behavior of superelastic SMA materials by taking phase transformation between austenite and martensite into consideration. The responses of SMA slit dampers are compared to those of steel slit dampers. Axial stress and strain components are also investigated on the FE models under cyclic loading in an effort to validate the adequacy of FE modeling and then to compare between two slit damper systems. It can be shown that SMA slit dampers exhibit many structural advantages in terms of ultimate strength, moderate energy dissipation and recentering capability.