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The purpose of the paper is to propose a novel approach, based on grey clustering decision, to fill in an omission of quantitative monitoring parameter selection methods.

The basic monitoring parameter selection criteria and the corresponding calculation methods are presented. Then, the grey clustering decision model for monitoring parameter optimization selection is constructed, and an integrated weight determination method based on analytic hierarchy process (AHP) and information entropy is provided.

Basic principle for monitoring parameter selection is proposed and quantitative description is carried out for selection principle in engineering application. Grey clustering decision‐making model for monitoring parameter optimization selection is established. Comprehensive weight ascertainment method based on AHP and information entropy is provided to make the index weight more scientific.

At system design stage, it is of significance to carry out selection and optimization of monitoring parameters. After the optimization of monitoring parameters is confirmed, measurability analysis and design in parallel are carried out for convenience of timely information feedback and system design revision. Therefore, the system integration efficiency is improved and the cost of research and manufacturing is reduced.

Monitoring parameter optimization selection process based on grey clustering decision‐making model is described and the analysis result shows that the proposed method has certain degree of effectiveness, rationality and universality.

The purpose of this paper is to explore the variation law between the clay microstructure and macro external force by using soil scanning electron microscope (SEM) images.

First, SEM images of clay were pre-processed by MATLAB, and quantitative statistical parameters such as directional probability entropy, fractal dimension and shape factor are extracted. Second, the distribution force model was proposed, considering that the microscopic parameters of soil particles were independent of each other, and the distribution coefficient was determined according to the analytic hierarchy process (AHP). Then, the fitted formula of quantitative statistical parameters based on the distribution force model was obtained by taking the macroscopic distribution force as independent variable and the microscopic parameters of soil particles as dependent variable. Finally, the correctness of corresponding fitting formula was verified.

The results showed that the change of external consolidation pressure has great influence on the directional probability entropy and fractal dimension, while the shape factor reflecting the regular degree of soil particle shape is less sensitive to the consolidation pressure. The fitting formula has high accuracy, and mostly the R value can reach more than 0.9. All the data have passed the test, which proves that the distribution force model proposed in this paper is rational.

The model can be used to connect the macroscopic stress of soil with the micro-structure deformation of soil particles through mathematical formula, which can provide reference for engineering practice.

This study aims to examine the potential of two artificial intelligence (AI)-based algorithms, namely, adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP), for indirect estimation of the ultimate bearing capacity (q_ult) of rock foundations, which is a considerable civil and geotechnical engineering problem.

The input-processing-output procedures taking place in ANFIS and GEP are represented for developing predictive models. The great importance of simultaneously considering both qualitative and quantitative parameters for indirect estimation of q_ult is taken into account and explained. This issue can be considered as a remarkable merit of using AI-based approaches. Furthermore, the evaluation procedure of various models from both engineering and accuracy viewpoints is also demonstrated in this study.

A new and explicit formula generated by GEP is proposed for the estimation of the q_ult of rock foundations, which can be used for further engineering aims. It is also presented that although the ANFIS approach can predict the output with a high degree of accuracy, the obtained model might be a black-box. The results of model performance analyses confirm that ANFIS and GEP can be used as alternative and useful approaches over previous methods for modeling and prediction problems.

The superiorities and weaknesses of GEP and ANFIS techniques for the numerical analysis of engineering problems are expressed and the performance of their obtained models is compared to those provided by other approaches in the literature. The findings of this research provide the researchers with a better insight to using AI techniques for resolving complicated problems.

The new Italian National Recovery and Resilience Plan (NRRP) has prioritised a new healthcare model that will establish the additional community healthcare facilities (CHFs). The paper proposes a methodology for supporting decision-making on location of the future facilities according to new parameters that consider how proximity to healthcare benefits communities. Rethinking the spatial parameters for locating future CHFs, focusing on fragile areas, creates a novel decision support system.

The methodology is based on multifactor analysis and on geographic information system (GIS) mapping to simulate the potential and risks associated with the proposed location of CHFs, focusing on territorial contexts of metropolitan cities, medium-sized cities, and Inner Areas, characterized by different fragilities. This method aims to innovate urban planning practices by updating the practice of per-capita urban planning standards and promoting implementation of the 15-minute city model.

The method defines new spatial parameters useful to inform the appropriate location of CHFs in Italy's Inner Areas. This offers a new integrated approach to spatial design mixing urban planning and healthcare dimensions.

The methodology will bring about an integrated urban planning approach, which guides both transformative urban choices and health services' implementation according to the needs of communities.

This paper aims to improve operational efficiency and minimize accident frequency in cold storage facilities through adopting an effective occupational safety and health program. The hidden knowledge can be extracted from the warehousing operations to create the comfortable and safe workplace environment.

A fuzzy association rule-based knowledge management system is developed by integrating fuzzy association rule mining (FARM) and rule-based expert system (RES). FARM is used to extract hidden knowledge from real operations to establish the relationship between safety measurement, personal constitution and key performance index measurement. The extracted knowledge is then stored and adopted in the RES to establish an effective occupational and safety program. Afterwards, a case study is conducted to validate the performance of the proposed system.

The results indicate that the aforementioned relationship can be built in the form of IF-THEN rules. An appropriate safety and health program can be developed and applied to all workers, so that they can follow instructions to prevent cold induced injuries and also improve the productivity.

Because of the increasing public consciousness of occupational safety and health, it is important for the workers in cold storage facilities where the ambient temperature is at/below 10°C. The proposed system can address the social problem and promote the importance of occupational safety and health in the society.

This study contributes to the knowledge management system for improving the occupational safety and operational efficiency in the cold storage facilities.

The study aims to present the hybrid approach of multiple MCDM techniques with strategic perspective to assist the vendor ranking process.

Multiple MCDM techniques such as fuzzy QFD, mathematical modelling and ANP (analytical network process) are integrated in the model for vendor ranking. Multiple phases in vendor ranking such as pre‐qualification and final selection are dealt with using the above techniques.

Compared to individual approaches, the proposed hybrid model effectively assists the vendor ranking process. The efficacy of the proposed approach is evident from the case study of an automotive components manufacturer involving 20 vendors comprising pre‐qualification by fuzzy QFD and final selection by ANP. This set of potential vendors is evaluated based on three main criteria and eight sub criteria.

Fuzzy QFD is employed for qualifying supplier to form a supplier pool, as it is helpful in converting qualitative information into quantitative parameters. This data is then combined with other quantitative data to form a mathematical model. The mathematical model is solved by the method of integer programming, using TORA. ANP with BOCR (benefits, opportunities, costs, and risks) is proposed for evaluating and selecting appropriate supplier. ANP model is solved using Super Decision package.

Previous research has little specific guidance on how to improve large infrastructures’ risk analysis. This paper aims to propose a practical risk analysis framework across the project lifecycle with Bayesian Networks (BNs).

The framework includes three phases. In the qualitative phase, primary risks were identified by literature reviews and interviews; questionnaires were used to determine key risks at each project stage and causal relationships between stage-related risks. In the quantitation, brainstorming and questionnaires, and techniques of ranked nodes/paths, risk map and Bayesian truth serum were adopted. Then, a BN-based risk assessment model was developed, and risk analysis was conducted with AgenaRisk software.

Twenty key risks across the lifecycle were determined: some risks were recurring and different risks emerged at various stages with the construction and feasibility most risky. Results showed that previous stages’ risks significantly amplified subsequent stages’ risks. Based on the causality of stage-related risks, a qualitative model was easily constructed. Ranked nodes/paths facilitated the quantification by requiring less statistical knowledge and fewer parameters than traditional BNs. As articulated by a case, this model yielded very simple and easy-to-understand representations of risks and risk propagation pathways.

Rare research has developed a BN risk assessment model from the perspective of project stages. A structured model, a propagation network among individual risks, stage-related risks, and the final adverse consequence, has been designed. This research provides practitioners with a realistic risk assessment approach and further understanding of dynamic and stage-related risks throughout large infrastructures’ lifecycle. The framework can be modified and used in other real-world risk analysis where risks are complex and develop in stages.

Based on the use of simple and relevant systems, several applications of UV spectrophotometry have recently been developed for the quality control of water, wastewater, air and contaminated soils for environmental, urban or industrial needs. These are outlined in this paper.

Reverse factoring (RF) is one of the most prevalent supply chain finance (SCF) solutions. This study challenges the view that suppliers accept financially attractive reverse factoring offers (RFOs) and reject financially unattractive ones. Specifically, it focuses on small and medium enterprise (SME) suppliers and how transaction cost economics (TCE) factors affect their decision.

The authors study eight cases of RFOs, interviewing suppliers, buyers and financial service providers (FSPs) and using several sources of private and publicly available secondary data.

In five out of eight RFOs, suppliers either accepted unattractive offers or rejected attractive ones. Bounded rationality and opportunism seem to explain such misalignment, while asset specificity and frequency play a minor role in decisions.

The study shows the need for further investigation linking analytical assessment of SCF benefits with qualitative factors.

SME suppliers cannot assume an RFO will benefit them. They must critically evaluate their buyers' offers, ideally with self-awareness towards how the abovementioned factors might affect their decisions. For buyers and banks, this study gives clear insights on how to approach SME suppliers to avoid rejection of financially attractive RFOs.

This contribution analyses financial attractiveness of RFOs in conjunction with qualitative factors, including rejected RFOs and without assuming that RFOs are financially attractive for suppliers. This is original and relevant for both research and practice, since it extends the understanding of the supplier response to RFOs, thanks to the consideration of TCE factors.

The characteristic requirements for flight information displays for the Public and for airport staff are essentially different while the actual information is the same. The Public search for information about a single flight on a one at a time basis. However, airport staff need to be aware of all pertinent information at all times in order to carry out their jobs.