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This paper presents a Monotonic Unbounded Schemes Transformer (MUST) approach to bound/monotonize (remove undershoots and overshoots) unbounded spatial differencing schemes automatically, and naturally. Automatically means the approach (1) captures the critical cell Peclet number when an unbounded scheme starts to produce physically unrealistic solution automatically, and (2) removes the undershoots and overshoots as part of the formulation without requiring human interventions. Naturally implies, all the terms in the discretization equation of the unbounded spatial differencing scheme are retained.

The authors do not formulate new higher-order scheme. MUST transforms an unbounded higher-order scheme into a bounded higher-order scheme.

The solutions obtained with MUST are identical to those without MUST when the cell Peclet number is smaller than the critical cell Peclet number. For cell Peclet numbers larger than the critical cell Peclet numbers, MUST sets the nodal values to the limiter value which can be derived for the problem at-hand. The authors propose a way to derive the limiter value. The authors tested MUST on the central differencing scheme, the second-order upwind differencing scheme and the QUICK differencing scheme. In all cases tested, MUST is able to (1) capture the critical cell Peclet numbers; the exact locations when overshoots and undershoots occur, and (2) limit the nodal value to the value of the limiter values. These are achieved by retaining all the discretization terms of the respective differencing schemes naturally and accomplished automatically as part of the discretization process. The authors demonstrated MUST using one-dimensional problems. Results for a two-dimensional convection–diffusion problem are shown in Appendix to show generality of MUST.

The authors present an original approach to convert any unbounded scheme to bounded scheme while retaining all the terms in the original discretization equation.

Persisting construction performance shortcomings in the Saudi Arabian construction industry requires engendering project management competencies in construction professionals’ roles and responsibilities. The purpose of this study was to determine significant construction project management competencies for Saudi Arabian construction professionals.

A cross-sectional questionnaire survey used project managers’ competencies from the literature. Construction professionals participated in the survey. Normalisation was used to determine the critical individual competencies. Statistically significant differences due to designations and organisations were analysed. Factor analysis revealed the relationships among significant competencies.

The most critical individual competencies included communication, team building and leadership skills. However, Project managers and all other construction professionals had statistically significant differences in insights on some of the critical competencies. The results revealed seven components: behaviour and attitude-related competencies; fundamental project management-related competencies; pro-active, knowledge and creativity-related competencies; political and organisational-related competencies; stakeholder management and experience-related competencies; management-related competencies; and confidence, commitment and negotiation-related competencies.

Evidently, inculcating project management competencies is essential for construction professionals to enhance project performance. The failure to get insights from the National Project Management Office officials was a limitation; however, views from construction professionals were sufficient as they are the most affected stakeholders.

The study determined project management competencies necessary for Saudi Arabian construction professionals to improve their project delivery performance.

This paper aims to identify the most suitable location for a university campus in Egypt based on governorates’ social needs by employing the analytic hierarchy process (AHP). The paper, then, reflects the findings retrieved from the Egyptian context on the Japanese context to reveal how different countries deal with the location-allocation decision problem for university campuses.

The AHP is employed to evaluate and rank Egyptian governorates based on 13 distinct criteria obtained from governmental open-source databases. These criteria measure the social needs of each governorate, guiding the decision on the location of new university campuses.

The results expose a disparity between Egypt's current campus development plan and recommendations derived from AHP analysis. The location-allocation decision for new university campuses appears to be influenced by subjective assessments, indicating a gap between planned developments and identified social needs. Additionally, contextual social and cultural differences between developing and developed countries impact the identification and fulfilment of the demand for a new university campus.

This paper contributes by offering decision-makers a robust location-allocation framework. It serves as a valuable tool for policy formulation in establishing new public universities in both developing and developed countries. Comparative analysis with the Japanese context enriches the understanding of how countries address the location-allocation decision problem for university campuses, emphasising the significance of context-specific considerations in such decisions.

India should hold a privileged position in maritime transportation due to its extensive coastline and advantageous location. However, the country heavily relies on other maritime nations like Singapore and Colombo for transshipment due to insufficient infrastructure and policy framework, and this has created disadvantage to Indian goods in the international market. The government has launched a significant drive to promote three transshipment ports in southern India with top-notch amenities in response to this worrying circumstance. It is anticipated that these ports would compete with the existing transshipment ports, particularly with Colombo, and divert the transshipped goods back to India. The move is expected to make Indian exports more competitive besides making India less prone to geopolitical and economic disturbances in the region. However, these initiatives have been met with many challenges. In fact, the first attempt of the Indian government to set up an International Container Transshipment Terminal (ICTT) at Vallarpadam in Cochin Port has met with failure despite its best infrastructure and connectivity. High-cost structure seems to deter the competitiveness of this port. Furthermore, the development of Vizhinjam into a transshipment hub in the same region has put additional pressure on Vallarpadam. This chapter draws attention to certain factors that might be considered to enhance the competitiveness of Cochin Port. Also, the study highlights the snags that could have well been avoided while implementing the project and might as well be avoided while implementing other projects in line.

Total risk score (TRS) is considered one of the main indicators for risk evaluation. Several studies attempted to employ different types of risk indices for the evaluation of cost overrun causes. Hence, this study aims at performing a comparative analysis to evaluate the efficiency of three different approaches for TRS calculation.

Thirty-eight unique causes of cost overrun in urban-related construction projects were identified and a survey was conducted among construction professionals in Iran. The TRS for each cost overrun cause is calculated using single-attribute (SA), double-attribute (DA), and multiple-attribute (MA) approaches, and eventually, causes were ranked. Furthermore, principal component analysis (PCA), logistic regression analysis (LRA), and K-means clustering are utilized to compare the differences in the generated TRS using different approaches.

The results revealed that the TRS generated through the MA approach demonstrated the highest efficiency in terms of generating correlation between causes and their identified latent constructs, prediction capability, and classification of the influential causes in the same group.

The originality of this study primarily stems from the adoption of statistical approaches in the evaluation of the recently introduced TRS calculation approach in comparison to traditional ones. Additionally, this study proposed a modified application of the relative importance index (RII) for risk prioritization. The results from this study are expected to fulfill the gap in previous literature toward exploring the most efficient TRS calculation approach for those researchers and practitioners who seek to utilize them as a measure to identify the influential cost overrun causes.

Advancements in enhancing regional port connectivity are crucial to fostering global maritime transport. The objective of this paper is to explore the complex relationship between infrastructure connectivity and the regional port of Thailand, specifically within the Regional Comprehensive Economic Partnership (RCEP).

This paper utilised fuzzy logic in exploratory factor analysis and introduced a new factor based on shipping networks, port operations, trade and emerging innovations. This can enhance the regional port and facilitate infrastructure connectivity in the RCEP. The results of this study have been successfully applied in specific contexts involving port authorities and private shipping companies.

The study’s findings indicate key factors for enhancing regional ports in Thailand. These factors include integrating connectivity, creating spare hubs, addressing service issues, optimising logistics and supply chains, considering market components and leveraging the digital market. These factors are also crucial for promoting infrastructure connectivity within the RCEP framework.

This research presents a strategic framework for enhancing regional ports in Thailand and improving international infrastructure. This is the first attempt to examine the influence of infrastructure connectivity on regional ports by applying fuzzy exploratory factor analysis to modernise infrastructure, which is key to unlocking the region’s maritime potential.

The mega railway infrastructure projects are faced with complex environments and multi-level management challenges. Thus, the mega railway infrastructure project management system not only needs to focus on its composition, but also needs to consider changes and impacts of internal and external environment.

This study attempts to introduce the concept of dissipative structure from the perspective of complexity theory and constructs a positive entropy and negentropy flow index system for mega railway infrastructure project management system in order to analyze the factors of management system more deeply. The Brusselator model is used to construct the structure of the mega railway infrastructure project management system, and the entropy method is used to calculate the positive entropy and negentropy values to verify whether the management system is a dissipative structure.

A plateau railway project in China was used as an example for an empirical study, not only its own characteristics are analyzed, but also the role of constraints and facilitation of the internal and external environment. Based on the research results, several effective suggestions are put forward to improve the stability and work efficiency of mega railway infrastructure project management system.

This study demonstrates that mega railway infrastructure project management system has the characteristics of dissipative structure. It can provide theoretical support for the development of mega railway infrastructure project management system from disorderly state to orderly state.

Rapid industrialization and construction generate substantial concrete waste, leading to significant environmental issues. Nearly 10 billion metric tonnes of concrete waste are produced globally per year. In addition, concrete also accelerates the consumption of natural resources, leading to the depletion of these natural resources. Therefore, this study uses artificial intelligence (AI) to examine the utilization of recycled concrete aggregate (RCA) in concrete.

An extensive database of 583 data points are collected from the literature for predictive modeling. Four machine learning algorithms, namely artificial neural network (ANN), random forest (RF), ridge regression (RR) and least adjacent shrinkage and selection operator (LASSO) regression (LR), in predicting simultaneously concrete compressive and tensile strength were evaluated. The dataset contains 10 independent variables and two dependent variables. Statistical parameters, including coefficient of determination (R²), mean square error (MSE), mean absolute error (MAE) and root mean square error (RMSE), were employed to assess the accuracy of the algorithms. In addition, K-fold cross-validation was employed to validate the obtained results, and SHapley Additive exPlanations (SHAP) analysis was applied to identify the most sensitive parameters out of the 10 input parameters.

The results indicate that the RF prediction model performance is better and more satisfactory than other algorithms. Furthermore, the ANN algorithm ranks as the second most accurate algorithm. However, RR and LR exhibit poor findings with low accuracy. K-fold cross-validation was successfully applied to validate the obtained results and SHAP analysis indicates that cement content and recycled aggregate percentages are the effective input parameter. Therefore, special attention should be given to sensitive parameters to enhance the concrete performance.

This study uniquely applies AI to optimize the use of RCA in concrete production. By evaluating four machine learning algorithms, ANN, RF, RR and LR on a comprehensive dataset, this study identities the most effective predictive models for concrete compressive and tensile strength. The use of SHAP analysis to determine key input parameters and K-fold cross-validation for result validation adds to the study robustness. The findings highlight the superior performance of the RF model and provide actionable insights into enhancing concrete performance with RCA, contributing to sustainable construction practice.

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.

This paper aims to reduce the cost of experiments required to test the efficiency of materials suitable for artificial tissue ablation by increasing efficiency and accurately forecasting heating properties.

A two-step numerical analysis is used to develop and simulate a bioheat model using improved finite element method and deep learning algorithms, systematically regulating temperature distributions within the hydrogel artificial tissue during radiofrequency ablation (RFA). The model connects supervised learning and finite element analysis data to optimize electrode configurations, ensuring precise heat application while protecting surrounding hydrogel integrity.

The model accurately predicts a range of thermal changes critical for optimizing RFA, thereby enhancing treatment precision and minimizing impact on surrounding hydrogel materials. This computational approach not only advances the understanding of thermal dynamics but also provides a robust framework for improving therapeutic outcomes.

A computational predictive bioheat model, incorporating deep learning to optimize electrode configurations and minimize collateral tissue damage, represents a pioneering approach in interventional research. This method offers efficient evaluation of thermal strategies with reduced computational overhead compared to traditional numerical methods.