ce/papers最新文献

Post-installed bonded anchors are used to connect structural and non-structural members in a variety of applications in concrete structures. The performance of bonded anchors can be influenced by several different parameters. One of the most important aspects that directly affects the bond strength of the adhesive anchor in both the short and long term, as well as various processes that occur at the material level of the adhesive is related to the temperature conditions during installation and over the design service life of the fastener. For this reason, different adhesive systems have different temperature ranges defined by the manufacturer. These temperature ranges include the maximum short-term temperature and the maximum long-term temperature, which represent the upper limits of the temperature range in which the fasteners can maintain their structural integrity and performance without significant degradation. Therefore, the service life of adhesive anchors is designed by considering the various environmental conditions of the region in which the structure is located. This contribution introduces a method for defining the temperature ranges taking into account historical data and climate change scenarios.

In the paper, a shear strength probabilistic assessment of concrete members with steel reinforcement is performed. The shear strength is analyzed by modelling with low-fidelity models – analytical formulas based on two main approaches to predict the shear strength of reinforced concrete beams with and without shear reinforcement: the modified compression field theory and the truss model. Based on a comparison of these low-fidelity analytical models and experimental data, model uncertainties can be evaluated. The aim of the analysis performed is to verify the existing code analytical formulas for shear strength calculation using stochastic models, to perform uncertainty propagation, sensitivity analysis and model uncertainty assessment. The code models of EN 1992-1-1, ACI 318 and fib Model Code 2010 are examined with respect to uncertainties involved and the reliability of the design value determination.

The paper presents an application of physics-informed polynomial chaos expansion for uncertainty quantification of a characteristic fatigue curve (S-N curve) representing a number of loading cycles leading to a failure of a material or a product. Since there is a significant uncertainty affecting the S-N curve caused by variability of material parameters, it is crucial to also identify a joint probability distribution of the S-N curve instead of a deterministic curve. Therefore, the employed method combines physics of the approximated curve in form of deterministic Woehler curve with data from experiments affected by uncertainty of material parameters. The proposed method respects the local variability of the initially identified fatigue curve and it could serve for identification of an optimal experimental design in specific regions of the fatigue curve, which will sequentially improve the accuracy of the identified curve as well as local statistics. The presented theoretical method is applied for identification of S-N curve based on laboratory experiments of concrete fasteners. The results demonstrated that the proposed method facilitates sequential enrichment of experimental design based on p-adaptivity and variance-based active learning. The active learning led to a substantial reduction in the size of the dataset while ensuring the integrity of the approximations.

This article reviews some practical methods of failure assessment for underground structures such as tunnels and deep excavations during construction stages in urban regions. Many factors, including random variables and parameters with statistically quantified values and laws of distribution, are tentatively considered to evaluate their effects on the failure of a specific objective (i.e., settlement of surface, the collapse risk of the diaphragm wall, etc.). A numerical model for a real sector (subsurface 2.9 km in length) of tunnel ‘Metro Line No1 Sai Gon-Suoi Tien’ is created to estimate the reliability index of the tunnel sector and to predict possible risks for the structure system. By manipulating the input data (predictors) in the numerical model, data about the response (i.e., settlement of the existing buildings) could be collected that are sufficient for estimating the probability of failure, Pf, which is nearly 8 % for BaSon area, and particularly equals 23.8 % for Ben Thanh area; this would be compared to the probability Pf predicted by using some non-parametric machine learning techniques such as multivariate adaptive regression spline (MARS). Besides, some probabilistic methods, such as the Taguchi method, are also reviewed for the failure of a deep excavation case study, from which the percentage of contribution of each predictor to the failure is quantified.

Geotechnical design must address unknown, complex, and heterogeneous ground conditions, which are typically investigated through direct methods such as core drilling combined with indirect methods like standard penetration tests. However, such ground investigations often provide only limited, point-specific information. To make full use of the available information for the optimization of foundation structures, the upcoming second generation of Eurocode 7 will explicitly allow the application of probabilistic and statistical methods in geotechnical design. In this paper, the well-known method of moments (MoM), also referred to as the geostatistical variogram approach, is explained and compared with the maximum likelihood method (ML) to estimate the spatial correlation of ground properties. Both methods are applied in two case studies that demonstrate the applicability, strengths, and limitations in a practical, non-academic site investigation. Additionally, the concept of spatial averaging is explained as a means of incorporating the derived (auto-)correlation length into practical geotechnical design within the framework of the semi-probabilistic design approach.

The lack of data on existing buildings inhibits circular economy strategies, such as reuse. To overcome this issue, the current study infers unknowns using Bayesian Networks (BNs), which are directed acyclic graphs of probabilistic variables. The study proposes two types of BNs to infer probabilities of variables of existing buildings. The first BN is based on conditional probability tables and associations between global building variables. The second builds on this with detailed structural load variables via engineering equations from historical structural design codes. The BNs generated probabilistic estimates which reflect uncertainty in the input data. With increased evidence, the BNs' estimates were updated, thereby reducing uncertainty of inferred building variables. Urban planners can use the tool to estimate building variables without physically measuring existing buildings, thereby enabling circular construction planning. Future studies may expand the BN to include more structural design equations to infer additional structural building variables.

One of the central struggles of engineers is to create sustainable buildings and enhance the usable area for often limited construction site dimensions. One possible and often considered solution is the design of high-rise structures. When placed in seismic areas the seismic response of high-rise structures may become difficult to control. The current study investigates the structural performance of several structural solutions for a 30 stories structure to be built for high ductility class in a seismic area with corner period of 1.8s. The main issue to be solved refers to provide enough stiffness to reduce excessive lateral displacement of structural elements and limit damage of nonstructural elements. Following structural solutions were investigated and compared: (i) central core with perimeter frames and different distributions of rigid stories; (ii) central core with perimeter frames and interior walls and (iii) central core with perimeter frames and dampers. The comparison was made in terms of lateral drift, base shear force, overturning moment and lateral stiffness variations. It turned out that the design of a regular, almost symmetric high-rise structure may face a large range of uncertainties to come up with a functional structural concept. The safety level promoted by seismic design codes rises with each code generation and structural requirements enhance. Future design of high-rise structures in long corner period seismic areas will most probably become impossible without the help of anti-seismic devices.

This paper presents the risk assessment of all bridges on the Swiss railways and Swiss highways, overall app. 14 000 bridges. In contrast to the usual approach, this study uses adjusted collapse frequencies instead of failure probabilities. The adjustments are based on statistical and AI evaluations of collapse databases and literature reviews and are carried out by factors specific to each bridge based on bridge databases. In addition, the consequence parameters for the bridges are determined specifically on the means of transport, the traffic volume, the detour route, and potential loss of life. As a result, the bridges are divided into risk categories and risk lists. Besides a short explanation of the procedure, several related topics are briefly discussed. For example, the consequences of the analysis in terms of bridge closure and further steps are also indicated. The paper is an extension of a former investigation of the Swiss railway bridges only. The paper finishes with a short discussion of open issues such as correlation either between certain adjustment factors and between certain bridges, for example during a flood.

The resilience of infrastructure is increasingly threatened by climate-induced hazards, such as sea-level rise and intensified rainfall, which challenge conventional engineering assumptions based on stationary conditions. This keynote paper presents recent advances in life-cycle structural reliability assessment under nonstationary climate effects. Probabilistic models incorporating sea-level rise projections and non-Poisson earthquake occurrences reveal amplified tsunami risks to coastal infrastructure. For rainfall-induced landslides, time-dependent fragility functions derived from stochastic rainfall models and slope stability analyses capture escalating failure probabilities across the lifespans of various structures and civil infrastructure systems. Additionally, a revised load and resistance factor design methodology introduces climate-adjusted partial factors for embankment design under future flood scenarios. These approaches integrate climate projections, surrogate modeling, and stochastic processes to better assess dynamic structural vulnerabilities. By aligning structural reliability analysis with evolving environmental conditions, this study supports the development of adaptive, risk-informed infrastructure systems.

Flooding is a major cause of physical loss or damage during civil engineering and infrastructure construction, and a key risk for tunnels and underground works. Damage to partially or fully completed projects can lead to high reinstatement and cleanup costs, delays, budget overruns, lost revenue, penalties, and DSU insurance expenses. These impacts affect all stakeholders — clients, designers, consultants, contractors, insurers, and third parties. Where flood risk exists, exposures must be identified, responsibilities defined in contracts, and mitigation measures costed into project delivery. Climate change is making weather patterns more unpredictable, with heavy rain and floods causing significant damage and delays. It also undermines the reliability of historical hydrological data. This guide, compiled by the Construction Insurance Risk Engineers Group (CIREG), focuses on flood risk during construction, shares lessons learned, and provides a reference protocol for risk management. It aims to raise awareness, prompt action, and support the creation of Flood Risk Management Plans, Baseline Reports, and Hazard Maps.