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This paper presents a model for equipment selection in earthmoving operations, utilizing multi‐attribute utility theory, analytical hierarchy process and computer simulation. Fleet configurations in the developed model are generated randomly from predefined fleet scenarios within a specified range. Simulation experiments are conducted for these generated configurations. The performance of these configurations is obtained from simulation experiments in the form of four measures which represent loader utilization, hauler utilization, project duration and project total cost. The utility values which represent the degree of satisfaction with those measures are estimated. These utility values are multiplied by their corresponding measures’ weights, calculated utilizing the analytical hierarchy process, in order to estimate the expected utility for each configured fleet. The fleet configuration that has the largest utility value is selected as the optimum fleet for the case at hand. A numerical example is presented to illustrate the different features of the developed model.

There have been numerous efforts to tackle the problem of accumulated construction and demolition wastes worldwide. In this regard, this study develops a model for identifying the optimum fleet required for waste transportation. The proposed model is validated through a case study from the construction sector in New Cairo, Egypt.

Various fleet combinations are assessed against the time, cost, energy and emissions generated from waste transportation. Genetic algorithm optimization is performed to select the near-optimum solutions. Complex proportional assessment and operational competitiveness rating analysis decision-making techniques are applied to rank Pareto frontier solutions. These rankings are aggregated using an ensemble approach based on the half-quadratic theory. Finally, a sensitivity analysis is implemented to determine the most sensitive attribute.

The results reveal that the optimum fleet required for construction and demolition wastes (CDW) transportation consists of one wheel loader of bucket capacity 2.5 cubic meters and nine trucks of capacity 22 cubic meters. Furthermore, consensus index and trust level of 0.999 are obtained for the final ranking. This indicates that there is a high level of agreement between the rankings. Moreover, the most sensitive criterion (i.e. energy) is identified using a sensitivity analysis.

This study proposes an efficient and effective construction and demolition waste transportation strategy that will lead to economic gains and protect the environment. It aims to select the optimum fleet required for waste transportation based on economic, social and environmental aspects. The usefulness of this study is establishing a consensual decision through the aggregation of conflicting decision makers' preferences in waste transportation and management.

The present study aims to focus on the possibility of developing new eco-friendly azo dyes with good colouristic application properties, exhibiting biological and pharmacological activities.

Coupling of 4-hydroxycoumarin with a variety of aromatic diazonium salts of 2-aminothiazole, 2-aminobenzothiazole, 4-aminoantipyrine, 4-aminoacetophenone, adenine sulphate, a-naphthylamine and sulphadimidine to produce novel azo dyes. The compounds were fully characterised using spectroscopic and analytical methods. All of the compounds were tested for their antimicrobial, anticancer and antioxidant activities. The prepared dyestuffs were dyed on polyester fabrics and subsequently their dyeing properties, light, washing, perspiration, rubbing and sublimation fastness were determined.

The spectroscopic data of the synthesised compounds have provided decisive evidence that such compounds exist in the solid state as the azo-dike to form C and in solution in equilibrium tautomer forms A, B and D. The prepared dyestuffs are suitable for either heat transfer printing or traditional printing on polyester and nylon 6 fabrics. The prints obtained from the dyes possess high colour strength, as well as good overall fastness properties. Also the synthesised compounds exhibit good biological and pharmacology activity.

Synthesis of these seven azo dyes for textile dyeing had never been reported previously.

The dyestuffs derived from 4-hydroxycoumarin are reasonable azo disperse dyestuffs giving good all round fastness properties on polyester fabrics.

Production of less expensive and new eco-friendly dyes exhibit antimicrobial and anticancer activity.

It provided a potentially simple way to synthesize novel coumarin azo-dyes exhibit good biological and pharmacology activity and also exhibit good overall fastness properties.

Most decision-making problems in construction are complex and difficult to solve, as they involve multiple criteria and multiple decision makers in addition to subjective uncertainties, imprecisions and vagueness surrounding the decision-making process. In many instances, the decision-making process is based on linguistic terms rather than numerical values. Hence, structured fuzzy consensus-reaching processes and fuzzy aggregation methods are instrumental in multi-criteria group decision-making (MCGDM) problems for capturing the point of view of a group of experts. This chapter outlines different fuzzy consensus-reaching processes and fuzzy aggregation methods. It presents the background of the basic theory and formulation of these processes and methods, as well as numerical examples that illustrate their theory and formulation. Application areas of fuzzy consensus reaching and fuzzy aggregation in the construction domain are identified, and an overview of previously developed frameworks for fuzzy consensus reaching and fuzzy aggregation is provided. Finally, areas for future work are presented that highlight emerging trends and the imminent needs of fuzzy consensus reaching and fuzzy aggregation in the construction domain.

The construction sector is a major contributor to the Egyptian economy and gross domestic products, plus considered as one of its fastest-growing sectors. Various deficiencies such as low productivity, delays, cost overrun, poor quality, etc. have plagued the construction sector, leading to undesirable project performance across Egypt and several other countries. One of the best methods to measure and improve performance evaluation and consequently a step toward industry improvement is key performance indicators (KPIs). The paper aims to discuss this issue.

A questionnaire survey was conducted to identify the importance index for 35 KPIs. The KPIs were identified and selected based on an extensive literature review and one on one interview with expert construction professionals. Moreover, exploratory factor analysis method has been used to analyze and determine the inter-relationships between the set of indicators.

Seven indicators that have the highest importance indicator were selected to create project overall performance indicator assessment models to consistently measure the performance of different construction projects. Seven equations are introduced reflecting the output of the research while considering both organization size and project type.

The proposed evaluation assessment models can be used to: evaluate the relative success of projects; indicate the areas of strengths and weaknesses in performance and establish company benchmarking. Three projects were selected to validate the performance evaluation models.

This paper provides mathematical assessment models for evaluating overall construction project performance in Egypt, taking into consideration organization size and project type. Previously published research works on the subject matter are quite limited, and frequently deal with only one or two selected aspects.

Facility management gained profound importance due to the increasing complexity of different systems and the cost of operation and maintenance. However, due to the increasing complexity of different systems, facility managers may suffer from a lack of information. The purpose of this paper is to propose a new facility management approach that links segmented assets to the vital data required for managing facilities.

Automatic point cloud segmentation is one of the most crucial processes required for modelling building facilities. In this research, laser scanning is used for point cloud acquisition. The research utilises region growing algorithm, colour-based region-growing algorithm and Euclidean cluster algorithm.

A case study is worked out to test the accuracy of the considered point cloud segmentation algorithms utilising metrics precision, recall and F-score. The results indicate that Euclidean cluster extraction and region growing algorithm revealed high accuracy for segmentation.

The research presents a comparative approach for selecting the most appropriate segmentation approach required for accurate modelling. As such, the segmented assets can be linked easily with the data required for facility management.

Parameter estimation in complex engineering structures typically necessitates repeated calculations using simulation models, leading to significant computational costs. This paper aims to introduce an adaptive multi-output Gaussian process (MOGP) surrogate model for parameter estimation in time-consuming models.

The MOGP surrogate model is established to replace the computationally expensive finite element method (FEM) analysis during the estimation process. We propose a novel adaptive sampling method for MOGP inspired by the traditional expected improvement (EI) method, aiming to reduce the number of required sample points for building the surrogate model. Two mathematical examples and an application in the back analysis of a concrete arch dam are tested to demonstrate the effectiveness of the proposed method.

The numerical results show that the proposed method requires a relatively small number of sample points to achieve accurate estimates. The proposed adaptive sampling method combined with the MOGP surrogate model shows an obvious advantage in parameter estimation problems involving expensive-to-evaluate models, particularly those with high-dimensional output.

A novel adaptive sampling method for establishing the MOGP surrogate model is proposed to accelerate the procedure of solving large-scale parameter estimation problems. This modified adaptive sampling method, based on the traditional EI method, is better suited for multi-output problems, making it highly valuable for numerous practical engineering applications.

This paper aims to explore the factors influencing customers' attitudes and behaviours, specifically in terms of affective satisfaction, participation behaviour, and word-of-mouth, within the restaurant industry, with a focus on both tangible and intangible elements. Additionally, the study seeks to identify the dual mediating role of customer affective satisfaction in this relationship.

In this exploratory study, SEM-VB was utilized to examine data from 312 valid respondents who completed a face-to-face questionnaire using a quantitative methodology. The respondents were targeted at restaurants that serve comparable food and beverages for a similar socioeconomic class.

The findings reveal that perceived value, physical appearance, and standardization significantly contribute to customer affective satisfaction, which, in turn, positively influences their participation behaviour and word-of-mouth.

Practically, restaurant managers can enhance customer experiences and boost positive word-of-mouth by fostering affective satisfaction and encouraging interactive customer participation.

The novel concept of “affective satisfaction” contributes to restaurant management literature by identifying its tangible and intangible drivers and uncovering its outcomes in participation behaviour and word of mouth. By combining numerous factors and investigating the mediating function of affective satisfaction and based on the theory of Expectancy-Disconfirmation, this study adds to the theoretical understanding of what drives the affective satisfaction and word-of-mouth of casual dining restaurants’ customers.

The purpose of this paper is to develop a building information modelling (BIM)-based multi-objective optimization (MOO) framework for volumetric analysis of buildings during early design stages. The objective is to optimize volumetric spaces (3D) instead of 2D spaces to enhance space utilization, thermal comfort, constructability and rental value of buildings

The integration of two fundamental concepts – BIM and MOO, forms the basis of proposed framework. In the early design phases of a project, BIM is used to generate precise building volume data. The non-sorting genetic algorithm-II, a MOO algorithm, is then used to optimize extracted volume data from 3D BIM models, considering four objectives: space utilization, thermal comfort, rental value and construction cost. The framework is implemented in context of a school of architecture building project.

The findings of case study demonstrate significant improvements resulting from MOO of building volumes. Space utilization increased by 30%, while thermal comfort improved by 20%, and construction costs were reduced by 10%. Furthermore, rental value of the case study building increased by 33%.

The proposed framework offers practical implications by enabling project teams to generate optimal building floor layouts during early design stages, thereby avoiding late costly changes during construction phase of project.

The integration of BIM and MOO in this study provides a unique approach to optimize building volumes considering multiple factors during early design stages of a project

This chapter focuses on the physical work environment, in particular, the possibilities and limitations of biophilic design in hotel office space. Biophilic design has already gained attention in directly influencing the guest experience, but little focus has been given to workspace. Traditionally, the hospitality industry places employees' office spaces in parts of the hotel not appropriate as guest space due to lack of daylight and windows. Many studies have shown that improving the workplace with nature-based features can play a role in increasing employee well-being. Data collected from employees with both extensive managerial and back-of-house hotel experience established that employees would welcome the opportunity to work in such an environment and currently find back-of-house design basic and lacking any aesthetically pleasing elements. An understanding of the obstacles in initiating such changes was shown as well as employee initiatives to improve work spaces. This initial exploratory study concludes that the needs of employees in terms of comfort, wellbeing, motivation and work satisfaction are often overlooked when considering back-of-house spaces. There is a general understanding and expectation that management needs to develop awareness and practical initiatives to address the deficits of the physical work environment. Biophilic design can be influential in promoting a calming and restorative environment at the workplace and thus positively impacting employee motivation and performance.