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This research examines the drivers of electric vehicle (EV) acceptance in the Kingdom of Saudi Arabia (KSA) by applying the unified theory of acceptance and use of technology (UTAUT) model, contextualized for the EV setting. The study aims to provide insights supporting the transition to sustainable transportation and identifying consumer perceptions and behavioral intentions toward EV adoption.

Based on survey data from a convenience sample collected from undergraduate and MBA students in a major university of KSA, the authors use seemingly unrelated regressions to provide novel insights on electric vehicle acceptance.

The study shows UTAUT constructs influence purchase intentions and attitudinal outcomes. Results indicate that perceived EV sustainability plays an important role in the relationship between UTAUT constructs and purchase intention alongside attitudes toward EV technology. Technological innovativeness enhances the impact of EV attitude and weakens the effect of perceived EV sustainability on purchase intention.

The study benefits researchers on sustainable technology acceptance and stakeholders facilitating sustainable transportation shifts. The insights guide the promotion of eco-friendly transportation solutions.

The research contextualizes and extends the UTAUT model constructs to understand drivers of EV acceptance. The study contributes to understanding sustainable innovation acceptance, considering the mediating role of perceptions of EV sustainability and the moderating role of technological innovativeness in driving purchase intentions.

This paper aims to analyse the research contributions of the Kingdom of Saudi Arabia in the field of applied linguistics (AL) indexed in the Web of Science core collection for the period between 2011 and 2020.

The author searched key terms in the Social Science Citation Index and Science Citation Index Expanded categories that publish documents in AL. The author compiled the data, classified these documents according to their research focus and investigated different metrics such as keywords analysis, citation analysis, overseas collaboration and productivity over authors, institutions and sources by using VOSviewer and Excel sheet.

Results found that publications in Saudi Arabia have tremendously increased around three times in the years 2016–2020 than before. As unexpected, highly cited researchers, sources and institutions for the social science and arts and humanities disciplines were higher than the scientific disciplines that investigated linguistic issues such as neurology, audiology and computer science. The area of language teaching and learning was the most researched area in which the highly cited author, journals and keywords analysis metrics occurred within its scope. The highly cited articles were those that collaborated with the world contributing authors and acted as corresponding authors.

The study contributes to the body of literature of AL which shares other categories that investigated language as a central issue. The study provides a fine-grained picture about the research productivity of AL in scientific and social science categories in Saudi Arabia.

Currently, many experimental studies on the properties and behavior of rubberized concrete are available in the literature. These findings have motivated scholars to propose models for estimating some properties of rubberized concrete using traditional and advanced techniques. However, with the advancement of computational techniques and new estimation models, selecting a model that best estimates concrete's property is becoming challenging.

In this study, over 1,000 different experimental findings were obtained from the literature and used to investigate the capabilities of ten different machine learning algorithms in modeling the hardened density, compressive, splitting tensile, and flexural strengths, static and dynamic moduli, and damping ratio of rubberized concrete through adopting three different prediction approaches with respect to the inputs of the model.

In general, the study's findings have shown that XGBoosting and FFBP models result in the best performances compared to other techniques.

Previous studies have focused on the compressive strength of rubberized concrete as the main parameter to be estimated and rarely went into other characteristics of the material. In this study, the capabilities of different machine learning algorithms in predicting the properties of rubberized concrete were investigated and compared. Additionally, most of the studies adopted the direct estimation approach in which the concrete constituent materials are used as inputs to the prediction model. In contrast, this study evaluates three different prediction approaches based on the input parameters used, referred to as direct, generalized, and nondestructive methods.

Durability of concrete can be enhanced by reducing the pore size/volume of pores or by entrapping the pores. This can be achieved by adding concrete admixtures that have particle size finer than cement. In this study, GNP, having particle size much smaller than cement, has been introduced/added to concrete mix to control the pore size in concrete to tape out the contribution of GNP in the durability enhancement of concrete.

Different concrete mixes, at various water–cement ratios and amounts of graphene, have been manufactured to produce concrete containing three different %ages of GNP, i.e. 0%, 0.05% and 0.1%. To demonstrate the effect on durability of the concrete through the addition of GNP, these concrete samples have been subjected to repeated Freeze-Thaw cycles. Followed by testing after 28 days of curing, including weight loss, water absorption and strength, which are directly related to the durability aspect of concrete.

It has been observed that the addition of GNP to concrete mixes reduces the weight loss and pore size distribution and enhances tensile and compressive strength of concrete, thereby increasing the durability of concrete in unfavorable circumstances like freeze-thaw i.e. alternate hot and cold weather conditions.

This investigation presents original piece of experimental work conducted on modified concrete (GNP-based concrete). The aim is to construct the civil infrastructure in deep-cold region with increased life span and better performance.

Implementing building information modeling (BIM) in construction projects offers many benefits. However, the use of BIM in project cost management is still limited. This study aims to review the current trends in the application of BIM in project cost management.

This study systematically reviews the literature on the application of BIM in project cost management. A total of 46 related articles were identified and analyzed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method.

Eighteen approaches to applying BIM in project cost management were identified. The approaches can be grouped into cost control and cost estimation. Also, BIM can be applied independently or integrated with other techniques. The integrated approaches for cost control include integration with genetic algorithms, Monte Carlo simulation, lean construction, integrated project delivery, neural network and value engineering. On the contrary, integrated approaches for cost estimation include integration with cost-plus pricing, discrepancy analysis, construction progress curves, estimation standards, algorithms, declarative mappings, life cycle sustainability assessment, ontology, Web-based frameworks and structured query language.

To the best of the authors’ knowledge, this study is the first to systematically review prior literature on the application of BIM in project cost management. As a result, the study provides a comprehensive understanding of the current state of the art and fills the literature gap. Researchers and industry professionals can use the study findings to increase the benefits of implementing BIM in construction projects.

Low-carbon concrete represents a new direction in mitigating the global warming effects caused by clinker manufacturing. Utilizing Saudi agro-industrial by-products as an alternative to cement is a key support in reducing clinker production and promoting innovation in infrastructure and circular economy concepts, toward decarbonization in the construction industry. The use of fly ash (FA) as a cement alternative has been researched and proven effective in enhancing the durability of FA-based concrete, especially at lower replacement levels. However, at higher replacement levels, a noticeable impediment in mechanical strength indicators limits the use of this material.

In this study, low-carbon concrete mixes were designed by replacing 50% of the cement with FA. Varying ratios of nano-sized glass powder (4 and 6% of cement weight) were used as nanomaterial additives to enhance the mechanical properties and durability of the designed concrete. In addition, a 10% of the mixing water was replaced with EMs dosage.

The results obtained showed a significant positive impact on resistance and durability properties when replacing 10% of the mixing water with effective microorganisms (EMs) broth and incorporating nanomaterial additives. The optimal mix ratios were those designed with 10% EMs and 4–6% nano-sized glass powder additives. However, it can be concluded that advancements in eco-friendly concrete additive technologies have made significant contributions to the development of sophisticated concrete varieties.

This study focused at developing nanomaterial additives from Saudi industrial wastes and at presenting a cost-effective and feasible solution for enhancing the properties of FA-based concrete. It has also been found that the inclusion of EMs contributes effectively to enhancing the concrete's resistance properties.

The purpose of this study is to experimentally determine whether mechanical properties of concrete can be improved by using olive pomace aggregates (OPA) as a substitute for natural sand. Two types of OPA were tested by replacing an equivalent amount of natural sand. The first type was OPA mixed with olive mill wastewater (OMW), and the second type was OPA not mixed with OMW. For each type, two series of concrete were produced using OPA in both dry and saturated states. The percentage of partial substitution of natural sand by OPA varied from 0% to 15%.

The addition of OPA leads to a reduction in the dry density of hardened concrete, causing a 5.69% decrease in density when compared to the reference concrete. After 28 days, ultrasonic pulse velocity tests indicated that the resulting material is of good quality, with a velocity of 4.45 km/s. To understand the mechanism of resistance development, microstructural analysis was conducted to observe the arrangement of OPA and calcium silicate hydrates within the cementitious matrix. The analysis revealed that there is a low level of adhesion between the cement matrix and OPA at interfacial transition zone level, which was subsequently validated by further microstructural analysis.

The laboratory mechanical tests indicated that the OPCD_OPW (5) sample, containing 5% of OPA, in a dry state and mixed with OMW, demonstrated the best mechanical performance compared to the reference concrete. After 28 days of curing, this sample exhibited a compressive strength (R_c) of 25 MPa. Furthermore, it demonstrated a tensile strength of 4.61 MPa and a dynamic modulus of elasticity of 44.39 GPa, with rebound values of 27 MPa. The slump of the specimens ranged from 5 cm to 9 cm, falling within the acceptable range of consistency (Class S2). Based on these findings, the OPCD_OPW (5) formulation is considered optimal for use in concrete production.

This research paper provides a valuable contribution to the management of OPA and OMW (OPA_OMW) generated from the olive processing industry, which is known to have significant negative environmental impacts. The paper presents an intriguing approach to recycling these materials for use in civil engineering applications.

Recycling construction waste is a promising way towards sustainable development in construction. Recycled aggregate (RA) is obtained from demolished concrete structures, laboratory crushed concrete, concrete waste at a ready mix concrete plant and the concrete made from RA is known as RA concrete. The purpose of this study is to apply multiple linear regressions (MLRs) and artificial neural network (ANN) to predict the mechanical properties, such as compressive strength (CS), flexural strength (FS) and split tensile strength (STS) of concrete at the age of 28 days curing made completely from the recycled coarse aggregate (RCA).

MLR and ANN are used to develop a prediction model. The model was developed in the training phase by using data from a previously published research study and a developed model was further tested by obtaining data from laboratory experiments.

ANN shows more accuracy than MLR with an R²-value of more than 0.8 in the training phase and 0.9 in a testing phase. The high R²-value indicates strong relation between the actual and predicted values of mechanical properties of RCA concrete. These models will help construction professionals to save their time and cost in predicting the mechanical properties of RCA concrete at 28 days of curing.

ANN with rectified linear unit transfer function and backpropagation algorithm for training is used to develop a prediction model. The outcome of this study is the prediction model for CS, FS and STS of concrete at 28 days of curing.

The purpose of this study is to determine the effect of business intelligence on job satisfaction in the Saudi Electricity Company in the Asir. It aims to evaluate employee job satisfaction after adopting a BI system for job practices.

A mixed-method including descriptive, questionnaire for data collection and analytical approach was used. The random sample population consists of 354 employees out of 3,000.

It is found that the implementation of a BI system and the associated practices have a statistically significant effect on employee job satisfaction. The study recommends the adoption of BI systems for organizational activities. Such organizations must consider the latest BI tools for solving business problems.

To the best of authors' knowledge, the current study is the first attempt to evaluate the performance of BI practices in the region and in context of employee job satisfaction. This will contribute to understanding the job satisfaction in similar organizational environment.

Inefficient waste disposal technique and cement production methodology caused significant environmental impacts, leading to global warming. The purpose of the research was to invent an effective, sustainable technology to use the wastes and alternate for cement in concrete. Geopolymer technology could be the most desirable solution to use the wastes into an effective product.

The wood waste ash derived from nearby tea shops was used as an alternate binder for fly ash. The replacement of WWA with FA was varied from 0 to 100% at 10% intervals. In this research, setting and mechanical features of Geopolymer Concrete (GPC) along with Waste wood ash (WWA) was carried out. The influence of wood waste ash in the microstructure of the GPC was also assessed using scanning electron microscope and X-ray diffraction analysis.

The findings revealed that 30% replacement of wood waste ash was performed higher in all measured features. Besides, the formation of different phases was also observed with the inclusion of wood waste ash.

The demand for fly ash was increased in recent years, and the fly-based GPC has required more alkaline solution and temperature curing. Hence, there was a research gap on finding an alternative binder for fly ash.

The research novelty was to use the wood waste ash, which has inbuilt alkaline compounds on the production of sustainable geopolymer. The finding showed that the wood waste ash could be alternate fly ash that eliminates the environmental impacts and economic thrust.