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The purpose of this paper is to critically analyse the role of housing reconstruction projects in post conflict and post tsunami Sri Lanka, and to discuss their implications on conflict prevention.

Using four housing reconstruction projects in Batticaloa, Kilinochchi and Jaffna Districts, Sri Lanka, as case studies, and a novel methodological framework, the study explores the causal relations among the independent variables associated with housing reconstruction and dependent variables related to conflict prevention. The data, gathered from interviews and project reports, were analysed using propositions from a literature review, adopting a thematic analytical approach.

This study finds that reconstruction has created new forms of conflicts and tensions for the people who came to live in the newly constructed houses. The hostile relations that existed among different ethnic groups during the conflict were continued, and to some extent, exacerbated by the reconstruction undertaken after the war.

The study identifies causal relations among the independent variables associated with housing reconstruction and dependent variables related to conflict prevention, which can be used to inform physical reconstruction programmes after conflict.

The research presents a novel methodological framework. The results reveal concerns in housing and infrastructure development that have implications for future research and practice in post conflict environments.

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.

The purpose of this paper is to investigate the feasibility of an end-to-end simplified and automated reconstruction pipeline for digital building assets using the design science research approach. Current methods to create digital assets by capturing the state of existing buildings can provide high accuracy but are time-consuming, expensive and difficult.

Using design science research, this research identifies the need for a crowdsourced and cloud-based approach to reconstruct digital building assets. The research then develops and tests a fully functional smartphone application prototype. The proposed end-to-end smartphone workflow begins with data capture and ends with user applications.

The resulting implementation can achieve a realistic three-dimensional (3D) model characterized by different typologies, minimal trade-off in accuracy and low processing costs. By crowdsourcing the images, the proposed approach can reduce costs for asset reconstruction by an estimated 93% compared to manual modeling and 80% compared to locally processed reconstruction algorithms.

The resulting implementation achieves “good enough” reconstruction of as-is 3D models with minimal tradeoffs in accuracy compared to automated approaches and 15× cost savings compared to a manual approach. Potential facility management use cases include the issue and information tracking, 3D mark-up and multi-model configurators.

Through user engagement, development, testing and validation, this work demonstrates the feasibility and impact of a novel crowdsourced and cloud-based approach for the reconstruction of digital building assets.

The purpose of this paper is to investigate how public/private hybrid and ambiguous organizations played pivotal roles in a governmental programme of housing reconstruction following a major earthquake in central Italy in 2009. Venturing beyond the boundaries of institutional isomorphism and using a Foucauldian approach, the longitudinal analysis seeks to illuminate accounting and performance challenges and provide insights to the calculative techniques associated with evacuee housing.

In “act 1” this paper investigates the role of a consortium created during the recovery stage of the disaster to construct temporary housing. In “act 2” attention shifts to consortia established for the reconstruction of buildings in devastated communities. The total observation period is 11 years. 31 semi-structured interviews were conducted with 15 key-actors. A broad range of official documents was also consulted.

In the immediate aftermath of the earthquake a comprehensive reporting system was established to facilitate the construction of 19 new towns for 15,000 evacuees. The mix of accountants, engineers and architects who developed the system and a building prototype evidences the assembly of diverse calculative techniques by different experts and the de-territorialization of subject disciplines. During reconstruction technologies of government included the introduction of standardised systems and vocabularies that homogenised administrative procedures among diverse experts.

This paper provides academics and policymakers with insights to accounting, performance management and accountability in hybrid organizations in the largely unexplored realm of post-disaster evacuee housing. Further studies are needed to examine the politics of calculation in similar contexts.

This paper fills a gap in the literature by exploring the role played by individual experts working for hybrid organizations. Further, by exploring actual practices over an extended period of post-disaster recovery and reconstruction, the study highlights how experts intervened to solve problems at the meso-political level and at the micro-organizational level.

Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode physical laws into neural networks, is a less data-demanding approach for flow field reconstruction. However, when the fluid physics is complex, it is tricky to obtain accurate solutions under the PINN framework. This study aims to propose a physics-based data-driven approach for time-averaged flow field reconstruction which can overcome the hurdles of the above methods.

A multifidelity strategy leveraging PINN and a nonlinear information fusion (NIF) algorithm is proposed. Plentiful low-fidelity data are generated from the predictions of a PINN which is constructed purely using Reynold-averaged Navier–Stokes equations, while sparse high-fidelity data are obtained by field or experimental measurements. The NIF algorithm is performed to elicit a multifidelity model, which blends the nonlinear cross-correlation information between low- and high-fidelity data.

Two experimental cases are used to verify the capability and efficacy of the proposed strategy through comparison with other widely used strategies. It is revealed that the missing flow information within the whole computational domain can be favorably recovered by the proposed multifidelity strategy with use of sparse measurement/experimental data. The elicited multifidelity model inherits the underlying physics inherent in low-fidelity PINN predictions and rectifies the low-fidelity predictions over the whole computational domain. The proposed strategy is much superior to other contrastive strategies in terms of the accuracy of reconstruction.

In this study, a physics-informed data-driven strategy for time-averaged flow field reconstruction is proposed which extends the applicability of the PINN framework. In addition, embedding physical laws when training the multifidelity model leads to less data demand for model development compared to purely data-driven methods for flow field reconstruction.

The purpose of this paper is to discuss policy-relevant findings regarding strategies for mainstreaming gender in achieving sustainable post-disaster reconstruction (PDR).

An exploratory case study was used to explore the implementation of gender mainstreaming strategies and the link to sustainable PDR. The Bantul and Sleman regencies of Yogyakarta province provide a unique site for researching PDR as they are located in a region that is more strongly affected by earthquakes than nearly any other in Indonesia. Data were collected through interviews with 17 key stakeholders and 26 beneficiaries who were involved during and after the earthquake. To support the interview findings, surveys involving 50 policy makers and 150 beneficiaries were conducted. Content analysis and t-statistics were used in analyzing the data.

Gender mainstreaming strategies within sustainable reconstruction should incorporate strategies for protecting against gender vulnerabilities and for promoting gender capacities. Both are fundamental to the achievement of sustainable PDR.

The paper establishes comprehensive strategies for mainstreaming gender under three pillars (i.e. economic, social and environmental) of sustainable development. The findings benefit relevant policy makers by improving the policy performance of gender mainstreaming in the affected communities in enhancing sustainable PDR.

The purpose of this paper is to reduce the artifacts in fast Bayesian reconstruction images in electrical tomography. This is in particular important with respect to object detection in electrical tomography applications.

The authors suggest to apply the Box-Cox transformation in Bayesian linear minimum mean square error (BMMSE) reconstruction to better accommodate the non-linear relation between the capacitance matrix and the permittivity distribution. The authors compare the results of the original algorithm with the modified algorithm and with the ground truth in both, simulation and experiments.

The results show a reduction of 50 percent of the mean square error caused by artifacts in low permittivity regions. Furthermore, the algorithm does not increase the computational complexity significantly such that the hard real time constraints can still be met. The authors demonstrate that the algorithm also works with limited observations angles. This allows for object detection in real time, e.g., in robot collision avoidance.

This paper shows that the extension of BMMSE by applying the Box-Cox transformation leads to a significant improvement of the quality of the reconstruction image while hard real time constraints are still met.

At airport security checkpoints, baggage screening is aimed to prevent transportation of prohibited and potentially dangerous items. Observing the projection images generated by X-rays scanner is a critical method. However, when multiple objects are stacked on top of each other, distinguishing objects only by a two-dimensional picture is difficult, which prompts the demand for more precise imaging technology to be investigated for use. Reconstructing from 2D X-ray images to 3D-computed tomography (CT) volumes is a reliable solution.

To more accurately distinguish the specific contour shape of items when stacked, multi-information fusion network (MFCT-GAN) based on generative adversarial network (GAN) and U-like network (U-NET) is proposed to reconstruct from two biplanar orthogonal X-ray projections into 3D CT volumes. The authors use three modules to enhance the reconstruction qualitative and quantitative effects, compared with the original network. The skip connection modification (SCM) and multi-channels residual dense block (MRDB) enable the network to extract more feature information and learn deeper with high efficiency; the introduction of subjective loss enables the network to focus on the structural similarity (SSIM) of images during training.

On account of the fusion of multiple information, MFCT-GAN can significantly improve the value of quantitative indexes and distinguish contour explicitly between different targets. In particular, SCM enables features more reasonable and accurate when expanded into three dimensions. The appliance of MRDB can alleviate problem of slow optimization during the late training period, as well as reduce the computational cost. The introduction of subjective loss guides network to retain more high-frequency information, which makes the rendered CT volumes clearer in details.

The authors' proposed MFCT-GAN is able to restore the 3D shapes of different objects greatly based on biplanar projections. This is helpful in security check places, where X-ray images of stacked objects need to be distinguished from the presence of prohibited objects. The authors adopt three new modules, SCM, MRDB and subjective loss, as well as analyze the role the modules play in 3D reconstruction. Results show a significant improvement on the reconstruction both in objective and subjective effects.

This paper aims to present an approach based on electrical impedance tomography spectroscopy (EITS) for the determination of water and ice fraction in low-power applications such as autarkic wireless sensors, which require a low computational complexity reconstruction approach and a low number of electrodes. This paper also investigates how the electrode design can affect the reconstruction results in tomography.

EITS is performed by using a non-iterative method called optimal first order approximation. In addition to that, a planar electrode geometry is used instead of the traditional circular electrode geometry. Such a structure allows the system to identify materials placed on the region above the sensor, which do not need to be confined in a pipe. For the optimization, the mean squared error (MSE) between the reference images and the obtained reconstructed images was calculated.

The authors demonstrate that even with a low number of four electrodes and a low complexity reconstruction algorithm, a reasonable reconstruction of water and ice fractions is possible. Furthermore, it is shown that an optimal distribution of the sensor electrodes can help to reduce the MSE without any costs in terms of computational complexity or power consumption.

This paper shows through simulations that the reconstruction of ice and water mixtures is possible and that the electrode design is a topic of great importance, as they can significantly affect the reconstruction results.

After tsunami 2004, it was estimated that more than 98,000 permanent houses had to be rebuilt. However, ten years on, as communities, are they satisfied in their new homes? What are the indicators affecting the long-term satisfaction of resettled communities in relation to their new permanent houses. The purpose of this paper is to qualitatively evaluate the level of long-term satisfaction of two tsunami affected resettled communities in Sri Lanka in a bid to identify the indicators affecting the long-term satisfaction of post disaster resettled communities in relation to permanent housing.

In addition to the thorough literature review conducted to evaluate the state of the art in the subject area, a series of interviews were conducted with experts and tsunami affected communities in Sri Lanka to gather primary data for this research. The literature review is used to establish the initial list of indicators of long-term satisfaction of resettlements. The expert interviews and the community interviews were used to verify and refine the initially identified indicators.

A sustainable resettlement programme is just not merely reconstruction of a set of houses. A resettlement programme should re-establish the socio-economic and cultural life of people. Reconstruction of a house does not solve the housing issue; it is vital to look in to the indicators which can convert a house into a home and the surrounding into a neighbourhood.

This paper makes a significant contribution in terms of identifying indicators affecting the long-term community satisfaction with resettlement programmes taking into account economic, social and cultural factors with a special emphasis on post tsunami resettlements in Sri Lanka.