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This study aims to conduct an experimental study and finite element model (FEM) to investigate the flexural behavior of heat-damaged beams strengthened/repaired by hybrid fiber-reinforced polymers (HFRP).

Two groups of beams of (150 × 250 × 1,200) mm were cast, strengthened and repaired using different configurations of HFRP and tested under four-point loadings. The first group was kept at room temperature, while the second group was exposed to a temperature of 400°C.

It was found that using multiple layers of carbon fiber-reinforced polymer (CFRP) and glass fiber-reinforced polymer (GFRP) enhanced the strength more than a single layer. Also, the order of two layers of FRP showed no effect on flexural behavior of beams. Using a three-layer scheme (attaching the GFRP first and followed by two layers of CFRP) exhibited increase in ultimate load more than the scheme attached by CFRP first. Furthermore, the scheme HGC (heated beam repaired with glass and carbon, in sequence) allowed to achieve residual flexural capacity of specimen exposed to 400°C. Typical flexural failure was observed in control and heat-damaged beams, whereas the strengthened/repaired beams failed by cover separation and FRP debonding, however, specimen repaired with two layers of GFRP failed by FRP rupture. The FEM results showed good agreement with experimental results.

Few researchers have studied the effects of HFRP on strengthening and repair of heated, damaged reinforced concrete (RC) beams. This paper investigates, both experimentally and analytically, the performance of externally strengthened and repaired RC beams, in flexure, with different FRP configurations of CFRP and GFRP.

Heavy equipment residual value forecasting is dynamic as it relies on the age, type, brand and model of the equipment, ranking condition, place of sale, operating hours and other macroeconomic gauges. The main objective of this study is to predict the residual value of the main types of heavy construction equipment. The residual value of heavy construction equipment is predicted via deep learning (DL) and machine learning (ML) approaches.

Based on deep and machine learning regression network integrated with data mining, random forest (RF), decision tree (DT), deep neural network (DNN) and linear regression (LR)-based modeling decision support models are developed. This research aims to forecast the residual value for different types of heavy construction equipment. A comprehensive investigation of publicly accessible auction data related to various types and categories of construction equipment was utilized to generate the model's training and testing datasets. In total, four performance metrics (i.e. the mean absolute error (MAE), mean squared error (MSE), the mean absolute percentage error (MAPE) and coefficient of determination (R2)) were used to measure and compare the developed algorithms' accuracy.

The developed algorithm's efficiency has been demonstrated by comparing the deep and machine learning predictions with real residual value. The accuracy of the results obtained by different proposed modeling techniques was comparable based on the performance evaluation metrics. DT shows the highest accuracy of 0.9111 versus RF with an accuracy of 0.8123, followed by DNN with an accuracy of 0.7755 and the linear regression with an accuracy of 0.5967.

The proposed novel model is designed as a supportive tool for construction project managers for equipment selling, purchasing, overhauling, repairing, disposing and replacing decisions.

Due to the lack of awareness and poor cybersecurity practices that pose cyber threats during COVID-19 time, this research aims to explore user's attitude toward engaging in proactive cybersecurity awareness behavior.

Based on the theory of planned behavior, the relationship between multiple factors and their influence on the attitude is explored. A survey-based approach was utilized to collect responses and a model was proposed and tested on 229 respondents from the University of Petra-Jordan.

The attitude was significantly influenced by peers' influence and the individuals' cybersecurity threats awareness, especially threats that emerged during the COVID-19 time.

The research benefits decision makers in educational institutions who intend to develop cybersecurity awareness programs and helps them to assess user cybersecurity background weaknesses.

The research is the first to explore users' knowledge dimensions including organizational, information systems and social media as well as peers' influence on cybersecurity awareness. Also, it sheds light on the users’ perception of major cybersecurity hazards in COVID-19 time.

Efficient management of earthmoving equipment is critical for decision-makers in construction engineering management. Thus, the purpose of this paper is to prudently identify, select, manage and optimize the associated decision variables (e.g. capacity, number and speed) for trucks and loaders equipment to minimize cost and time objectives.

This paper addresses an innovative multiobjective and multivariable mathematical optimization model to generate a Pareto-optimality set of solutions that offers insights of optimal tradeoffs between minimizing earthmoving activity’s cost and time. The proposed model has three major stages: first, define all related decision variables for trucks and loaders and detect all related constraints that affect the optimization model; second, derive the mathematical optimization model and apply the multiobjective genetic algorithms and classify all inputs and outputs related to the mathematical model; and third, model validation.

The efficiency of the proposed optimization model has been validated using a case study of earthmoving activities based on data collected from the real-world construction site. The outputs of the conducted optimization process promise the model’s originality and efficiency in generating optimal solutions for optimal time and cost objectives.

This model provides the decision-maker with an efficient tool to select the optimal design variables to minimize the activity's time and cost.

This study aims to develop cause-and-effect relationships among the critical success factors (CSFs) of fintech adoption and rank these CSFs based on their importance in the model.

To achieve the objectives of the study, the Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL) approach was used. The data was collected from 16 experts using a questionnaire.

The findings demonstrated the interrelationships among the CSFs. In total, 16 critical factors were recognized as causal factors, and the remaining eight were considered effect factors. The CSFs were ranked based on their importance in fintech adoption.

This study is novel as it investigates CSFs of fintech adoption using FDEMATEL, and it contributes to understanding the nature of these factors and how they affect fintech adoption. The findings propose a significant basis to deepen fintech adoption and deliver a clue to design a practical framework for fintech adoption.

Effectively handling knowledge is crucial for any organization to survive and prosper in the turbulent environments of the modern era. Leadership is a central element for knowledge creation, acquisition, utilization and integration processes. Based on these considerations, this study aims to offer an overview of the evolution of the literature regarding the knowledge management-leadership relationship published over the past 20 years.

A bibliometric analysis coupled with a systematic literature review were performed over a data set of 488 peer-reviewed articles published from 1990 to 2018.

The authors discovered the existence of four well-polarized clusters with the following thematic focusses: human and relational aspects, systematic and performance aspects, contextual and contingent aspects and cultural and learning aspects. The authors then investigated each thematic cluster by reviewing the most relevant contributions within them.

Based on the bibliometric analysis and the systematic literature review, the authors developed an interpretative framework aimed at uncovering several promising and little explored research areas, thus suggesting an agenda for future knowledge management-leadership research. Some steps of the paper selection process may have been biased by the interpretation of the researcher. The authors addressed this concern by performing a multiple human subject reading process whose reliability was confirmed by a Krippendorf’s alpha coefficient value >0.80.

To the best knowledge, this is the first study to map, systematize and discuss the literature concerned to the topic of the knowledge management-leadership relationship.