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This paper aims to identify the main aspects requiring immediate attention in the post-disaster reconstruction of road infrastructure, thereby providing a major synthesis, which advances the understanding in this important area.

Literature and empirical evidence obtained from documents and semi-structured interviews with 28 respondents of three case study districts in Aceh, Indonesia was analysed using NVivo 10. The findings were validated through triangulation with the literature and consultation with the experts in the field of disaster management and road infrastructure.

The authors propose a framework for the reconstruction of road infrastructure, which respond to the peculiarities of road projects in a post-disaster setting. The framework comprises various components requiring detailed attention in the reconstruction process and describes their position in the road project and disaster management cycle.

The framework fills the gap in the body of knowledge with regard to road infrastructure reconstruction in a post-disaster context. For the first time, this paper recognises the importance of local government capacity in the Aceh Province with regard to the sustainability of the post-disaster reconstruction assets.

It is well recognized that disasters, whether naturally occurring or the result of human invention, affect a region on many levels. Not only are disasters felt within the painful context of human tragedy, loss of life, and physical suffering, but disasters can also destroy the immediate socio-economic fabric of the affected population as well as the ability of a region to sustain itself during the slow process of recovery and reconstruction. As Newton (1997) notes, “disasters are not isolated from the social structure within which they occur; rather, they are social phenomena” (p. 219).

In the aftermath of hurricanes, the damage levied on transportation infrastructures increases the timeliness of emergency responses and recovery procedures, making it essential that they are reconstructed as quickly as possible – on time and within budget. The aim of this study was to determine significant performance indicators (PIs) that considerably affect cost and schedule performance as well as reworks in post–hurricane reconstruction of transportation infrastructure including highways, bridges, roadways, etc. Additionally, the determined PIs were clustered to investigate key components.

The root causes of reconstruction projects' poor performance were identified through the existing literature, and 30 transportation infrastructure case studies were analyzed to determine the significant PIs that corresponded to cost, schedule performance and reworks. The factor analysis method was used to cluster the significant PIs and determine the key components affecting them.

Eight key components were found for cost, eight for schedule performance and six for reworks. The key components of cost performance are shortage of resources, information management, coordination, safety, location, quality of materials, quality of resources and project complexity. The key components of reconstruction schedule performance are human resources, risk management, work suspension, material resources, productivity, on-site inspections, geometrical characteristics and level of reconstruction complexity. The six key components of reconstruction reworks are logistic management, pace of decision-making, accommodation for staff, environmental issues, available temporary paths and volume of debris.

The outcomes of this research will assist authorities and decision makers in identifying and evaluating the critical root causes of poor cost performance, poor schedule performance and reworks and will enable them to facilitate the timely and effective allocation of resources.

The interface between two conducting fluids in a magnetic fluid dynamics (MFD) problem was identified by means of external magnetic field measurements. Genetic algorithms (GA) were applied to solve the inverse problem.The principal component analysis (PCA) was used to speed up the process of interface reconstruction.

With respect to the experimental results we have designed a general technique for mode identification and/or interface reconstruction. Two main procedures are available to solve the inverse problem, the full interface reconstruction and the principle component analysis (PCA) mode. In the case of full reconstruction, it can be decided whether an algorithm for fast identification of the dominant modes applying a FFT module should be performed or not. The full interface reconstruction applies stochastic optimization methods ((GA) or evolution strategies (ES)) for the estimation of the interface shape characteristics. The main goal of the PCA mode is to find the dominant mode of the interface shape and its amplitude. The PCA mode is realized by means of stochastic optimization methods (GA, ES) and a simple direct searching (DS) using the golden section technique.

PCA with GA procedure enables the identification of the dominant mode of the interface shape between two conducting fluids with sufficient accuracy for simulated magnetic fields. Time of identification is strongly reduced due to a redefinition of the genotype representations in the PCA mode. Accuracy of reconstruction depends on the noise level, i.e. signal to noise ratio and a geometrical model used in the reconstruction phase. The correlation between the noise level and values of cost function for identified modes has been found if a proper geometry modelling is applied.

The paper describes a new, fast technique for solving an inverse field problem of a MFD problem where the interface between two conducting fluids has to be identified using a magnetic field tomography measuring system.

Refurbishing may be the most practical approach under the low volume production. This effort aims to achieve robotic laser cladding with the main purpose of achieving maximum processing flexibility, predictably high quality, lower maintenance and operating costs. This study aims to focus on online measurement and cladding path generation toward automatic laser cladding.

Based on the specific requirements of automatic laser cladding, an approach was proposed toward an automatic laser cladding with powder injection for the refurbishment of components with free‐form surfaces. This study assessed the feasibility of integrating a non‐contact free‐form surface measurement system, an industrial robot, and an algorithm for generating cladding tool paths seamlessly.

3D laser scanning and laser cladding systems can be embedded into an existing robot motion control system. Online measurement based 3D surface reconstruction is a practical approach toward cladding tool path generation for on‐site refurbishment.

Robotic laser cladding may be a potential application by integrating other measurement devices, such as temperature sensor based monitoring system.

Refurbishing worn‐out components could have significant economic benefits. This study indicates that robotic laser cladding may potentially facilitate improved refurbishment of oversized components.

We consider what happened in the initial reconstruction interventions following the 6 April 2009 earthquake in L'Aquila (Italy). Using the disaster risk reduction and resilience paradigm, we discuss the cognitive and interactional failures of top-down approaches, and we analyse the main constraints to enacting inclusive social learning and socially-sustainable transformation and building back better more resilient communities in post-disaster reconstruction.

Our evidence comes from participant observation, action anthropology and analytic auto-ethnography conducted during the reconstruction phase following the L'Aquila earthquake. Findings were triangulated with document analysis, media analysis and retrospective interviewing conducted in 2013 and 2017.

The shift from civil defence to civil protection did not bring any advance in disaster management and development practice in terms of DRR and resilience. The militaristic command-and-control approach, which is still in vogue among civil protection systems, means that local political leaders become the civil protection authorities in a disaster area. As in the L'Aquila case, this exacerbates local social and environmental risks and impacts, inhibits local communities from learning and restricts them from participating in post-disaster interventions.

Most previous commentary on disaster recovery and reconstruction following the L'Aquila earthquake has focussed on the top-down approach carried out by the national government and the Italian Department of Civil Protection (DCP). This paper is unique in that it sheds light on how the command-and-control approach was also implemented by local authority figures and on how this undermined building back better more resilient communities.

Cities lying within conflict zones have continually faced hardships of both war aftermath and long-term sustainable reconstruction. Challenges have surpassed the typical question of recovery from post-conflict trauma, preserving urban heritage and iconic elements of the built environment, to face issues of critical decision making, rebuilding effectiveness and funding mechanisms, leading to time-consuming processes that lack adequate consistent long-term management. Some approaches have explored methods of effective long-term city reconstruction management but have not fully developed comprehensive approaches that alleviate the management of such complex processes. The paper aims to discuss these issues.

The authors devise an approach for the smart management of post-conflict city reconstruction. The authors focus on evaluation, strategic planning, reconstruction projects and implementation. The authors integrate building information modeling and geographic/geospatial information systems in a platform that allows for real-time analysis, reporting, strategic planning and decision making for managing reconstruction operations and projects among involved stakeholders including government agencies, funding organizations, city managers and public participants.

The approach suggested a smart management system for the reconstruction process of post-conflict cities. Implementing this system was shown to provide a multi-objective solution for post-conflict city reconstruction based on its interlinked modules.

Results may lack generalizability and require testing on several cases to provide rigorous findings for different case studies.

Implications include developing smart management systems for use by city managers and government authorities in post-conflict zones, as well as bottom-up decision making by including participant citizens especially populations in the diaspora.

The approach offers an integrated platform that informs city reconstruction decision makers, allowing for strategic planning tools for efficient planning, monitoring tools for continuous management during and after reconstruction, and effective platforms for communication among all stakeholders.

This paper aims to numerically study the compositional flow of two- and three-phase fluids in one-dimensional porous media and to make a comparison between several upwind and central numerical schemes.

Implicit pressure explicit composition (IMPEC) procedure is used for discretization of governing equations. The pressure equation is solved implicitly, whereas the mass conservation equations are solved explicitly using different upwind (UPW) and central (CEN) numerical schemes. These include classical upwind (UPW-CLS), flux-based decomposition upwind (UPW-FLX), variable-based decomposition upwind (UPW-VAR), Roe’s upwind (UPW-ROE), local Lax–Friedrichs (CEN-LLF), dominant wave (CEN-DW), Harten–Lax–van Leer (HLL) and newly proposed modified dominant wave (CEN-MDW) schemes. To achieve higher resolution, high-order data generated by either monotone upstream-centered schemes for conservation laws (MUSCL) or weighted essentially non-oscillatory (WENO) reconstructions are used.

It was found that the new CEN-MDW scheme can accurately solve multiphase compositional flow equations. This scheme uses most of the information in flux function while it has a moderate computational cost as a consequence of using simple algebraic formula for the wave speed approximation. Moreover, numerically calculated wave structure is shown to be used as a tool for a priori estimation of problematic regions, i.e. degenerate, umbilic and elliptic points, which require applying correction procedures to produce physically acceptable (entropy) solutions.

This paper is concerned with one-dimensional study of compositional two- and three-phase flows in porous media. Temperature is assumed constant and the physical model accounts for miscibility and compressibility of fluids, whereas gravity and capillary effects are neglected.

The proposed numerical scheme can be efficiently used for solving two- and three-phase compositional flows in porous media with a low computational cost which is especially useful when the number of chemical species increases.

A new central scheme is proposed that leads to improved accuracy and computational efficiency. Moreover, to the best of authors knowledge, this is the first time that the wave structure of compositional model is investigated numerically to determine the problematic situations during numerical solution and adopt appropriate correction techniques.

This paper applied grey wave forecasting in a decomposition–ensemble forecasting method for modelling the complex and non-linear features in time series data. This application aims to test the advantages of grey wave forecasting method in predicting time series with periodic fluctuations.

The decomposition–ensemble method combines empirical mode decomposition (EMD), component reconstruction technology and grey wave forecasting. More specifically, EMD is used to decompose time series data into different intrinsic mode function (IMF) components in the first step. Permutation entropy and the average of each IMF are checked for component reconstruction. Then the grey wave forecasting model or ARMA is used to predict each IMF according to the characters of each IMF.

In the empirical analysis, the China container freight index (CCFI) is applied in checking prediction performance. Using two different time periods, the results show that the proposed method performs better than random walk and ARMA in multi-step-ahead prediction.

The decomposition–ensemble method based on EMD and grey wave forecasting model expands the application area of the grey system theory and graphic forecasting method. Grey wave forecasting performs better for data set with periodic fluctuations. Forecasting CCFI assists practitioners in the shipping industry in decision-making.

This paper aims to propose a new view planning method which can be used to calculate the next-best-view (NBV) for multiple manipulators simultaneously and build an automated three-dimensional (3D) object reconstruction system, which is based on the proposed method and can adapt to various industrial applications.

The entire 3D space is encoded with octree, which marks the voxels with different tags. A set of candidate viewpoints is generated, filtered and evaluated. The viewpoint with the highest score is selected as the NBV.

The proposed method is able to make the multiple manipulators, equipped with “eye-in-hand” RGB-D sensors, work together to accelerate the object reconstruction process.

Compared to the existed approaches, the proposed method in this paper is fast, computationally efficient, has low memory cost and can be used in actual industrial productions where the multiple different manipulators exist. And, more notably, a new algorithm is designed to speed up the generation and filtration of the candidate viewpoints, which can guarantee both speed and quality.