During offshore wind installation piles can start to sink uncontrolled. This is termed pile run or pile drop-fall and may occur when a soft soil layer is reached during pile driving where the soil has insufficient capacity to support the weight and energy imposed at the pile and thereby causes vertical movement of the pile. Pile run is a safety risk for the installation crew, it may cause the hammer to be lost, serious damages to the tools and vessel, and finally it can result in a pile installation which is out of tolerance. With larger and heavier piles in more challenging ground conditions used for offshore wind foundations, namely large diameter monopile structures, the risk for pile run increases. This paper summarizes the geotechnical background and presents innovative measurements against pile runs.
The paper analyses the actual bridge management (BM) process, showing the disadvantages of some of the actual criteria adopted in deciding maintenance interventions. Among them, the fixed time intervals for inspections and the use of a single rating, such as the condition index, to capture the condition of an element/bridge as the main indicator to define the intervention strategy. The management decisions based on this practice (condition obtained at fixed intervals) may then result in sub-optimal allocation of resources. Therefore, the paper also justifies why a performance-based BM is the optimal solution, explaining the main objectives of a performance-based BM, which are the key performance indicators (KPIs) and how they should be optimally balanced in a decision- making based on several aspects of performance (multi-attribute decision making). Finally, the paper presents some guidance on how a performance-based BM can be implemented in the actual context, led by the objective of sustainability and taking advantage of using the new digitalization tools.
In existing infrastructure management, bias between practice and scientific developments could be decreased by proper communication between practitioners and academia. IABSE TG5.4 aimed at evidencing this gap. This work focuses on the decision-making complexity for Bridge Management, to point out recent trends related to research fields, extending an initial analysis related to a survey on implemented decision-making models. The results and conclusions of this work are derived from the interviews conducted by TG5.4 experts, composed by academics, owners/operators, consultants, with country-specific infrastructure stakeholders from Europe, the USA, and South America. Knowledge and limitations on bridge management and decision-making related to technical, economic, environmental, social, political issues and fragmentation of the sector are addressed. Strengths/weaknesses of digitalization and areas for future works are identified.
Transportation Asset Management (TAM) is defined by the American Association of State Highway Transportation Officials (AASHTO) as a strategic and systematic process focused on business and engineering practices for allocating resources to assets throughout their lifecycles. Asset management encompasses the full set of business processes related to the management of physical assets. This paper will list the key elements that offer the greatest opportunity to improve an agency's AM efforts and hence inspire training of practitioners. This paper will also discuss the different existing frameworks and guidance on AM and identify which AM subjects that are suitable for inclusion in the academic curricula.
Over the past decade, underwater noise from pile driving for offshore wind turbine installations has raised concerns due to its impact on wildlife. Research has focused on predicting this noise, recognizing that impact loads create stress waves in steel piles. These waves reflect between the pile ends and radiate as high-pressure waves into the water due to pile expansion. Finite element analysis (FEA) has been commonly applied to model this phenomenon and has demonstrated good agreement with measurement data.This paper proposes an alternative approach to reduce the high computational costs of conventional FEA models for long ranges. The method utilizes an equivalent line array of sources along the pile, combining near-field results from FEA with Greens function from the Wavenumber Integration fast field program to calculate the source spectrum of the line array, which regenerates the noise pressure field. The alternative model's accuracy is validated against the FEA model, and parametric studies on key parameters of the equivalent source method, such as source numbers, receiver count, and coupling distance, are conducted. A recommended guideline for parameter selection is generated by highlighting their physical implications.
