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The objective of this research is to explore integration and transition activities in large industrial projects. The purpose is to (a) obtain a better understanding of the integration and transition activities between the project front-end (FE) and project initiation phases (PIPs), (b) explore what, how and when these integrations and transitions occur, and (c) explore what the integration and transition activities mean to project practitioners.

A qualitative research design methodology is followed, based on interviews using open-ended questions. An expert panel is used to provide responses to questions pertaining to the integration and transition between the project FE and PIP. The research is focused on managing large projects in the South African electrical engineering industrial projects industry. A literature review combined with empirical analysis reflects the importance of integrating and transitioning in project business.

The findings provide guidance to researchers and practitioners on integration and transition mechanisms, how and when these occur. It highlights the benefits of integration and transition activities. Important lessons for researchers and practitioners are provided together with areas for future research.

This is an interpretative analysis of expert opinion. Expert panel members are experienced at senior decision-making level, and their expertise was accessed based on experience, education and knowledge. This extensive experience is shared in this paper providing insights into their opinions, experiences, success and failures. These inputs together with the literature review provide interesting implications for both a theoretical foundation as well as practical implications for practitioners.

This research addresses the diverse characteristics of existing railway steel bridges in China, including variations in construction age, design standards, structural types, manufacturing processes, materials and service conditions. It also focuses on prominent defects and challenges related to heavy transportation conditions, particularly low live haul reserves and severe fatigue problems.

The study encompasses three key aspects: (1) Adaptability assessment: It begins with assessing the suitability of existing railway steel bridges for heavy-haul operations through comprehensive analyses, experiments and engineering applications. (2) Strengthening: To combat frequent crack defects in the vertical stiffener end structure of girder webs, fatigue performance tests and reinforcement scheme experiments were conducted. These experiments included the development of a hot-spot stress S-N curve for this structure, validating the effectiveness of methods like crack stop holes, ultrasonic hammering and flange angle steel. (3) Service life extension: Research on the cruciform welded joint structure (non-fusion transfer type) focused on fatigue performance over the long life cycle. This led to the establishment of a fatigue S-N curve, enhancing Chinese design codes.

The research achieved several significant outcomes: (1) Successful implementation of strengthening and retrofitting measures on a 64-m single-span double-track railway steel truss girder on an existing heavy-duty line. (2) Post-reinforcement, a substantial 26% to 32% reduction in live haul stress on bridge members was achieved. (3) The strengthening and retrofitting efforts met design expectations, enabling the bridge to accommodate vehicles with a 30-ton axle haul on the railway line.

This research systematically tackles challenges and defects associated with Chinese existing railway steel bridges, providing valuable insights into adaptability assessment, strengthening techniques and service life extension methods. Furthermore, the development of fatigue S-N curves and the successful implementation of bridge enhancements have practical implications for improving the resilience and operational capacity of railway steel bridges in China.

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.

Managing megaprojects is challenging due to their inherent complexity and uncertainty. Collaborative project delivery models have been introduced as an alternative to traditional project management in public infrastructure megaprojects and are often realized through collaborative contracts. These project organizations act as institutional arenas for logic interaction as actors with differing institutional backgrounds interact within the project. This paper aims to study the delivery phase of three megaprojects through an institutional lens, investigating the institutional interaction and alignment of logics therein.

A multiple case study was employed to reach deep insight into the phenomenon. Sixty-one interviews were conducted over 3 cases with representatives from all levels of the project hierarchy. Respondents were selected through snowball sampling. In two cases, observations of the shared project office were conducted. Data analysis built on first-order codes and second-order themes, collected into a theoretical framework.

The empirical evidence demonstrates the dynamics shaping institutional logics and gives evidence for changing logics in projects with a well-applied collaborative contract. However, there is a risk of resistance and a return to traditional logics since institutional change is slow and an unsuitably applied collaborative contract can lead to adherence to the conventional way of work.

Current research has focused on the regulatory framework and procurement phase of such models, but little attention has been given to the delivery phase and the interaction of conflicting logics. This paper can serve as an exemplar of the different logics found within public infrastructure projects and their interaction and alignment. Contributions include a heightened emphasis on the start of the project as a meeting point for differing institutional logics and the role change necessary when using a collaborative contract.