ce/papers最新文献

This paper proposes a quantitative framework for optimizing the construction method mix between onsite and offsite (prefabrication) construction under an Enterprise Risk Management (ERM) framework. Based on expert interviews, the study models loss events using Poisson (frequency) and Pareto (severity) distributions for each method's risk factors. Monte Carlo simulations are employed to compute risk indices, which are then used to define an optimal construction mix aligned with management's risk tolerance and aversion. A penalty function, derived from management input, ensures practical allocations by penalizing excessive deviations from the existing mix. The model identifies a 54% Onsite allocation as optimal under defined constraints. This approach offers a systematic, risk-aligned method for strategic construction planning.

Assessing the impact of climate change on the corrosion of reinforced concrete (RC) buildings in major European cities is crucial for developing effective adaptation strategies for both existing and future building stock in Europe. This study presents a quantitative evaluation of climate-induced carbonation of RC structures using a simplified probabilistic model. The model estimates carbonation depths under baseline conditions and future climate scenarios, offering insights into the necessary revisions of design standards and safety provisions to ensure long-term serviceability, safety, and durability of European buildings. Results reveal significant increases in carbonation depth over a 60-year period (by 2085), with projected growth ranging from 25% to 45% under an intermediate risk scenario and 58% to 89% under a high-risk scenario. Additionally, the paper discusses key sources of uncertainty that influence lifetime assessments, emphasizing the importance of addressing these uncertainties to improve the climate resilience of the existing and future building stock. The main findings highlight the urgency of climate-resilient measures to safeguard Europe's building infrastructure in the face of a changing climate.

Quality control has a favourable effect on material properties since quality requirements, as conformity control criteria, encourage producers to deliver high quality products. Previous studies indicate that this effect influences the stochastic models of building material properties — and, ultimately, the safety level of structures. Typically, for every sampling plan an operation characteristics curve (OC-curve) can be derived showing how the plan performs as lots of different quality levels are submitted to it. An OC-curve plots the discriminating capacity of conformity control criteria by establishing a relationship between the probability of accepting a lot and the percent defectives. In structural engineering, the utilisation of OC-curves is well established for the control of certain variables as concrete compressive strength. However, their utilisation for the control of attributes as geometric properties is less common and often neglected. This paper investigates how OC-curves can be derived for the conformity control of geometric properties and further utilisation in structural reliability assessments. This investigation is critical to unlock the potential offered by quality control in the identification and utilisation of existing safety margins during structural design.

This paper discusses a recently proposed artificial neural network-aided aimed multilevel sampling optimization method. The method is particularly effective in the optimization of engineering problems where the emphasis is on minimizing the number of evaluations of the objective function, such as structural damage detection using FEM model updating. The proposed method uses sequential sampling to train a machine learning model, which is used to identify the optimal sample. Depending on the complexity of the analyzed problem, a certain number of samples are required to train the model so that the optimization process does not get stuck in local minima. The improvement of the proposed method lies in finding a suitable structure of the training dataset. The emphasis is on maintaining a balance between the complexity of the training data and thus the generality of the surrogate model on the one hand, and the localization of the training data due to fast targeting on the other hand. A suitably adjusted training set should contain a combination of a certain amount of general data together with localized data. This ratio changes gradually during the optimization process. A study of improved method is performed on the problem of bridge structure damage detection.

This article explores innovative approaches in the pedagogy of mathematical statistics within civil engineering education, emphasizing the integration of STEM principles and service-learning methodologies. It argues for a practical, context-driven educational model that enhances the understanding and application of statistical methods under uncertainty in engineering projects. By incorporating STEM, the curriculum not only covers theoretical aspects but also applies these concepts to solve practical engineering problems, enhancing students' ability to handle real-world complexities. Service-learning is further integrated, promoting an experiential learning environment where students engage in community-based projects that require statistical solutions. These projects help students develop critical thinking and analytical skills by applying their knowledge in tangible settings, thereby bridging the gap between theoretical statistics and practical application. This approach not only enriches learning outcomes but also prepares students for professional challenges by providing them with a robust toolkit to address the probabilistic and statistical needs of modern engineering tasks. The article will delve into various case studies that demonstrate the successful implementation of these methodologies and discuss the potential for scalability and adaptation in different educational settings. Through this innovative pedagogical framework, the article showcases how modern educational strategies can significantly enhance the teaching and learning of mathematical statistics in civil engineering.

Globalization and networking are constantly increasing, making cooperation between different institutions more crucial for continuous development. Collaboration remains a key success factor, especially in tackling complex tasks. A close link between theory and practice enables cooperation more efficiently by applying scientific findings in practice and, conversely, by incorporating practice-related requirements into research.

Therefore, this paper uses a literature review to analyze the specific features of the partnership relationship between industry and universities and the associated challenges and opportunities. Conflicts of interest, diverging objectives, and structural differences are analyzed. On this basis, an integrative project plan is methodically developed, including base durations, costs, uncertainties, and risks. Continuous collaboration with the project plan intends to meet the requirements of theory and practice more closely and reduce conflicts during collaboration. This is exemplified by a single case study related to developing a new combustion concept for sustainable engines.

The result is a probabilistic project plan that reduces conflicts of interest and represents scientific-technical cooperation, enabling knowledge transfer for industrial projects in the building sector.

Dynamic Risk Assessment (DRA) which could be based on various methods might be key component to improve the navigation safety of the vessel. This paper is focused on review of various DRA methodologies applicable to Ro-Pax vessels. A literature review examined different key DRA techniques, including Fuzzy Logic, Bayesian Networks, Real-time Data Analytics, Machine Learning, IoT and Situational Awareness, evaluating their ability to manage support decision-making model. The integration of advanced techniques enhances the accuracy and responsiveness of risk assessments. Fuzzy Logic allows to effectively manage imprecise data, while Bayesian Networks enable dynamic updates based on real-time inputs. Machine Learning improves operational efficiency by predicting threats from navigational data patterns. Continuous evolution and integration of different DRA methods can significantly improve safety of navigation for all types of vessels. Adopting these approaches helps ship's operators enhance risk assessment. Future research should focus on developing hybrid models that combine multiple DRA techniques to enhance decision-making in navigation safety.

Robustness analyses aims at preventing a structural system from failing after the occurrence of damage, exceptional/unexpected events, or operational malfunctions. This paper reviews the key aspects of robustness in structures, and associated concepts such as redundancy and resilience. Several impact scenarios are discussed and approaches for assessing and quantifying robustness summarized. The paper also includes a section on robustness in seismic engineering. Lastly, it provides a general overview of conceptual robustness assessment and quantification across micro, meso, and macro levels.

As part of the Second Generation of Eurocodes, the newly-revised an amended Annex A of EN 1992-1-1:2023 offers a method to adjust partial safety factors for the resistance side. The new method is based on enhanced stochastic knowledge of material and geometric properties in combination to stringent quality control requirements conformed both for the building process as well as for the production of building materials. Complementary to the method, probabilistic models are also offered for the stochastic characterisation of basic variables. The proposed method is entrenched on the principles of Annex D in EN 1990 and the roots of the Mean Value First Order Second Moment method. While there is certainly merit in the new Annex A and its provisions, the method to adjust the partial safety factors has drawbacks. In this study, the method proposed in the Annex A is analysed and the practical implications for the design of new structures are discussed. This investigation shows that the resulting partial safety factors might lead to overconservative designs — and, thus, material and resources inefficiencies — or might even compromise the safety of structural members. Finally, this investigation offers several new avenues to spur advances and overcome the identified challenges in future work.

Normally, quality control strategies are used to verify whether a produced structural building material complies with the specified properties. In general, quality control has a favourable effect on the outgoing distributions of material and geometric properties of incoming inspected batches. Previous studies concerning concrete compressive strength demonstrated that this effect could influence the reliability level of concrete structures. Such benefits can be quantified by using Bayesian statistics in combination with reliability-based techniques such as First or Second Order Reliability Methods (Level II methods) or simulation methods as Crude Monte Carlo or Monte Carlo methods with variance reduction (Level III methods). Presented as a case study, this paper aims to illustrate the influence of quality control on the structural safety of a steel reinforced beam verified for shear resistance without shear reinforcement according to DIN EN 1992-1-1+NA. The positive results on the structural safety of beams highlight the relevance of considering quality control information in safety assessments and their potential to unlock structural safety margins.