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The dielectric constant (E′), dielectric loss (E″) and power factor (tan §) were measured for various excess hydroxyl content resins (polyesteramide, alkyd/polyesteramide and alkyd resins) and also for their corresponding dry films. Measurements were conducted within the frequency band 105 to 107 Hz and temperature range 20–50°C. The various parameters affecting the dielectric behaviour of resin varnishes and their dry films are discussed. Also the effect of ageing at 110°C upon the dielectric behaviour of dry films is another parameter taken into consideration.

The purpose of this paper is to demonstrate the route to digitize the customized mandible implants consisting of image acquisition, processing, implant design, fitting rehearsal and fabrication using fused deposition modeling and electron beam melting methodologies.

Recent advances in the field of rapid prototyping, reverse engineering, medical imaging and image processing have led to new heights in the medical applications of additive manufacturing (AM). AM has gained a lot of attention and interest during recent years because of its high potential in medical fields.

Produced mandible implants using casting, milling and machining are of standard sizes and shapes. As each person’s physique and anatomical bone structure are unique, these commercially produced standard implants are manually bent before surgery using trial and error methodology to custom fit the patient’s jaw. Any mismatch between the actual bone and the implant results in implant failure and psychological stress and pain to the patient.

The novelty in this paper is the construction of the customized mandibular implant from the computed tomography (CT) scan which includes surface reconstruction, implant design with validation and simulation of the mechanical behavior of the design implant using finite element analysis (FEA). There has been few research studies on the design and customization of the implants before surgery, but there had been hardly any study related to customized design implant and evaluating the biomechanical response on the newly designed implant using FEA. Though few studies are related to FEA on the reconstruction plates, but their paper lacks the implant design model and the reconstruction model. In this research study, an integrated framework is developed for the implant design, right from the CT scan of the patient including the softwares involved through out in the study and then performing the biomechanical study on the customized design implant to prove that the designed implant can withstand the biting and loading conditions. The proposed research methodology which includes the interactions between medical practitioners and the implant design engineers can be incorporated to any other reconstruction bone surgeries.

When disasters occur, the Chinese national or local government and their relevant departments (hereinafter referred to as the government) probably need to acquire emergency supplies from suppliers. Before concluding a transaction, the public officials usually negotiate the quality and price of the emergency supplies with the suppliers. They expect to achieve the best relief effect while the suppliers want to maximize their own interests. Therefore, in order to help the government acquire inexpensive emergency supplies with high quality in a short time, the purpose of this paper is to examine the negotiation process and proposes a negotiation principle for the staff.

This paper first elaborates the characteristics and impact factors of emergency supplies requisition negotiation. Then it establishes a model describing the negotiation on price and quality of emergency supplies between the public officials and suppliers. Afterwards, it proposes an algorithm which can estimate the success rate of the negotiation. Finally, the paper employs the conclusion of the model and algorithm to analyze the emergency supplies requisition negotiation process during the China Lushan earthquake.

This paper proposes a “WRAD” principle of emergency supplies requisition negotiation of public officials in disasters. First, they should ensure the requisition price is not too low. Second, they would widen the difference between the high price and low price. Third, it is best for them to follow the principle of “ascending negotiation and descending choice” while selecting multiple suppliers to negotiate.

This paper establishes a model to study the emergency supplies requisition negotiation process between the public officials and suppliers based on evolutionary game theory. The model assumes that both the public officials and suppliers are not fully rational individuals, and they need time to consult with each other to find out the optimal solution. This paper proposes an innovative action principle of the public officials during the negotiation process which can help it to acquire inexpensive, high-quality, emergency supplies within a short period from the suppliers.

Natural hazards occur frequently in Indonesia. When post-disaster recovery is prolonged and complicated, it is necessary to provide accommodations for homeless refugees in disaster areas. Since a transitional housing solution includes planning, design and execution phases, the design phase implements the decisions made in the planning phase and also affects the results of the execution phase. Therefore, the purpose of this paper is to assist communities to effectively deal with various processes during the design phase involving transitional housing solutions.

Based on eight factors associated with “building back better” and ten principles of transitional housing, the paper identified three problems in the design phase: inappropriate selection of settlement sites, improper representation of housing facilities and ineffective scheduling of construction projects. To resolve these problems, this study integrated a geographic information system (GIS), three-dimensional (3D) building models and construction project management tools to assess settlement sites, confirm housing facilities and configure construction projects, respectively.

After this study tested conventional methods (e.g. paper-based maps, drawings, reports) and the proposed approach, the results revealed that communities can appropriately determine settlement sites based on the GIS. The 3D building models enabled the communities to understand the external and internal layouts of housing facilities. Through construction project management, the communities could consider construction activities immediately when preparing the execution phase for transitional housing solutions.

This study offers a useful reference for similar applications in post-disaster reconstruction and management.

Finding an appropriate place for temporary housing after an earthquake is one of the main challenges of disaster risk management, especially in developing countries. Therefore, it is necessary to create pre-disaster location plans for the homeless population. This study aims to systematically find safe places and select suitable sites according to influential factors.

The research methodology used is a descriptive–analytical method. A field survey with a quantitative–qualitative approach is applied to recognize physical vulnerabilities and select suitable sites for temporary settlements. Due to the occurrence of several earthquakes in recent decades around the city of Isfahan, Iran, this area has been studied. Fuzzy analytic hierarchy process, geographic information system and rapid visual screening have been used for data analysis.

According to the site selection and vulnerability criteria and their prioritization, the findings indicate that 60% of the study area is vulnerable. Moreover, vacant lots, stadiums and public green spaces that can be used as multi-purpose sites are the most appropriate options for the temporary settlement.

The research criteria are generalizable and can be used for decision-making, concerning urban fabric vulnerability and site selection of temporary housing in cities exposed to earthquake risk.

Cultural features, accessibility, land conditions, the slope and type of land, availability and construction materials were addressed in locating temporary settlements. In addition to vacant lots and open spaces, safe buildings were also identified for temporary housing, and religious minorities and similar communities were considered.

The humanitarian supply chain (HSC) area is rich with conceptual frameworks with a focus on the importance of information and digital technology (IDT) applications. These frameworks have a limited scope in investigating and prioritizing barriers to IDT adoption in HSCs. The present study thus identifies and prioritizes the barriers to IDT adoption in organizations involved in HSCs.

By using a literature review allied with expert discussions and a fuzzy analytic hierarchy process (F-AHP), the study identifies and prioritizes a comprehensive set of barriers that organizations involved in HSCs may consider to improve IDT adoption.

The study investigates five main barriers (strategic, organizational, technological, financial and human) interlocked with 25 sub-barriers impacting the level of IDT adoption in organizations involved in HSCs. The findings indicate that strategic barriers (SBs) are of greatest importance, followed by organizational, technological, financial and human barriers. The findings indicate the difference in ranking barriers influencing the adoption of IDTs in HSCs compared to the commercial supply chain.

Although a three-step method adopted for this study is rigorous in terms of the way this research is conducted, it is essential to report that prioritization is based on the subjective opinions of the experts.

The findings aim to assist policymakers and practitioners in developing effective strategies to improve IDT adoption in organizations engaged in HSCs. Moreover, the prioritization of barriers provides a systematic way to overcome any barriers to improve HSC performance.

This study is first of its kind that investigates and prioritizes the barriers to IDT adoption in HSCs.

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 paper aims to use a data-driven approach towards optimizing construction operations. To this extent, it presents a machine learning (ML)-aided optimization approach, wherein the construction cost is predicted as a function of time, resources and environmental impact, which is further used as a surrogate model for cost optimization.

Taking a dataset from literature, the paper has applied various ML algorithms, namely, simple and regularized linear regression, random forest, gradient boosted trees, neural network and Gaussian process regression (GPR) to predict the construction cost as a function of time, resources and environmental impact. Further, the trained models were used to optimize the construction cost applying single-objective (with and without constraints) and multi-objective optimizations, employing Bayesian optimization, particle swarm optimization (PSO) and non-dominated sorted genetic algorithm.

The results presented in the paper demonstrate that the ensemble methods, such as gradient boosted trees, exhibit the best performance for construction cost prediction. Further, it shows that multi-objective optimization can be used to develop a Pareto front for two competing variables, such as cost and environmental impact, which directly allows a practitioner to make a rational decision.

Note that the sequential nature of events which dictates the scheduling is not considered in the present work. This aspect could be incorporated in the future to develop a robust scheme that can optimize the scheduling dynamically.

The paper demonstrates that a ML approach coupled with optimization could enable the development of an efficient and economic strategy to plan the construction operations.

The amalgamation of integrated project delivery (IPD) and building information modelling (BIM) is highly recommended for successful project delivery. However, IPD lacks an accurate cost estimation methodology at the “front-end” of projects, when little project information is available. This study aims to tackle this issue, through presenting analytical aspects, theoretical grounds and practical steps/procedures for integrating target value design (TVD), activity-based costing (ABC) and Monte Carlo simulation into the IPD cost structure, within a BIM-enabled platform.

A critical review was conducted to study the status of cost estimation within IPD, as well as exploring methods and tools that can enhance the cost estimation process for IPD. Thereafter, a framework is developed to present the proposed methodology of cost estimation for IPD throughout its entire stages. A case project is used to validate the practicality of the developed solution through comparing the profit-at-risk percentage for each party, using both traditional cost estimation and the proposed solution.

After applying the proposed IPD's cost estimation framework, on a real-life case project, the findings demonstrated significant deviations in the profit-at-risk value for various work packages of the project (approximately 100% of the finishing package and 22% of openings package). By providing a precise allocation of overhead costs, the solution can be used in real-life projects to change the entire IPD cost structure and ensure a fair sharing of risk–rewards among the involved parties in IPD projects.

Using the proposed methodology of cost estimation for IPD can enhance the relationship among IPD's core team members; all revealed financial deficiencies will be considered (i.e. compensation structure, profit pooling), hence enhancing the IPD performance.

This paper presents a comprehensive solution for integrating BIM and IPD in terms of cost estimation, offering three main contributions: (1) an innovate approach to utilise five-dimensional (5D) BIM capabilities with Monte Carlo simulation, hence providing reliable cost estimating during the conceptual TVD stage; (2) mathematical models that are developed through integrating ABC into the detailed 5D BIM to determine the three IPD's cost structure limbs; and (3) a novel mechanism of managing cost saving (rewards) through distinguishing between saved resources from organisation level, to daily task level, to increase trust among parties.

The purpose of this paper is to develop a qualitative framework for the selection of the most appropriate optimization algorithm for the multi-objective trade-off problem (MOTP) in construction projects based on the predefined performance parameters.

A total of 6 optimization algorithms and 13 performance parameters were identified through literature review. The experts were asked to indicate their preferences between each pair of optimization algorithms and performance parameters. A multi-criteria decision-making tool, namely, consistent fuzzy preference relation was applied to analyze the responses of the experts. The results from the analysis were applied to evaluate their relative weights which were used to provide a ranking to the algorithms.

This study provided a qualitative framework which can be used to identify the most appropriate optimization algorithm for the MOTP beforehand. The outcome suggested that non-dominated sorting genetic algorithm (NSGA) was the most appropriate algorithm whereas linear programming was found to be the least appropriate for MOTPs.

The devised framework may provide a useful insight for the construction practitioners to choose an effective optimization algorithm tool for preparing an efficient project schedule aiming toward the desired optimal improvement in achieving the various objectives. Identification of the absolute best optimization algorithm is very difficult to attain due to various problems such as the inherent complexities and intricacies of the algorithm and different class of problems. However, the devised framework offers a primary insight into the selection of the most appropriate alternative among the available algorithms.