Assessing ageing infrastructure is a critical challenge for civil engineers due to the difficulty in the estimation and integration of uncertainties in structural models. Field measurements are increasingly used to improve knowledge of the real behavior of a structure; this activity is called structural identification. Error-domain model falsification (EDMF) is an easy-to-use model-based structural-identification methodology which robustly accommodates systematic uncertainties originating from sources such as boundary conditions, numerical modelling and model fidelity, as well as aleatory uncertainties from sources such as measurement error and material parameter-value estimations. In most practical applications of structural identification, sensors are placed using engineering judgment and experience. However, since sensor placement is fundamental to the success of structural identification, a more rational and systematic method is justified. This study presents a measurement system design methodology to identify the best sensor locations and sensor types using information from static-load tests. More specifically, three static-load tests were studied for the sensor system design using three types of sensors for a performance evaluation of a full-scale bridge in Singapore. Several sensor placement strategies are compared using joint entropy as an information-gain metric. A modified version of the hierarchical algorithm for sensor placement is proposed to take into account mutual information between load tests. It is shown that a carefully-configured measurement strategy that includes multiple sensor types and several load tests maximizes information gain.

Good prediction of the behavior of wind around buildings improves designs for natural ventilation in warm climates. However wind modeling is complex, predictions are often inaccurate due to the large uncertainties in parameter values. The goal of this work is to enhance wind prediction around buildings using measurements through implementing a multiple-model system-identification approach. The success of system-identification approaches depends directly upon the location and number of sensors. Therefore, this research proposes a methodology for optimal sensor configuration based on hierarchical sensor placement involving calculations of prediction-value joint entropy. Computational Fluid Dynamics (CFD) models are generated to create a discrete population of possible wind-flow predictions, which are then used to identify optimal sensor locations. Optimal sensor configurations are revealed using the proposed methodology and considering the effect of systematic and spatially distributed modeling errors, as well as the common information between sensor locations. The methodology is applied to a full-scale case study and optimum configurations are evaluated for their ability to falsify models and improve predictions at locations where no measurements have been taken. It is concluded that a sensor placement strategy using joint entropy is able to lead to predictions of wind characteristics around buildings and capture short-term wind variability more effectively than sequential strategies, which maximize entropy.

Due to the complex relationship between pollen and vegetation, it is not yet clear how pollen diagrams may be interpreted with respect to changes in floristic diversity and only a few studies have hitherto investigated this problem. We compare pollen assemblages from moss samples in two southeastern European forests with the surrounding vegetation to investigate (a) their compositional similarity, (b) the association between their diversity characteristics in both terms of richness and evenness, and (c) the correspondence of the main ecological gradients that can be revealed by them. Two biogeographical regions with different vegetation characteristics, the Pieria mountains (north central Greece) and the slopes of Ciomadul volcano (eastern Romania), were chosen as divergent examples of floristic regions, vegetation structure and landscape openness. Pollen assemblages are efficient in capturing the presence or absence, rather than the abundance in distribution of plants in the surrounding area and this bias increases along with landscape openness and vegetation diversity, which is higher in the Pieria mountains. Pollen assemblages and vegetation correlate better in terms of richness, that is, low order diversity indices. Relatively high correlation, in terms of evenness, could be potentially found in homogenous and species poor ecosystems as for Ciomadul. Composition and diversity of woody, rather than herb, vegetation is better reflected in pollen assemblages of both areas, especially for Pieria where a direct comparison of the two components was feasible, although this depends on the species-specific pollen production and dispersal, the openness of landscape and the overall diversity of vegetation. Gradients revealed by pollen assemblages are highly and significantly correlated with those existing in vegetation. Pollen assemblages may represent the vegetation well in terms of composition, diversity (mainly richness) and ecological gradients, but this potential depends on land use, vegetation structure, biogeographical factors and plant life forms.

The Upper Pleistocene geoarchives in the south‐eastern Carpathian Basin are represented predominantly by loess–palaeosol records. In 2015, a 10 m sediment core composed of clay‐rich lacustrine sediments was recovered by vibracoring a dry lake basin located between the Vršac Mountains (Serbia) and the Banat Sands in the south‐eastern Carpathian Basin; a location relevant for placing regional archaeological results in a palaeoenvironmental context. Here, we present results from geoelectrical prospection and a lithostratigraphic interpretation of this sequence supported by a detailed granulometric study supplemented by ostracod analysis. An age model based on luminescence dating is discussed against sedimentological proxy data and its implication for palaeoenvironmental change. The cores show a stratigraphy of lighter ochre‐coloured and darker greyish sediment, related to the deposition of clay and silt trapped in an aquatic environment. Geophysical measurements show ~20 m thick lacustrine sediments. The grain‐size distributions including the variability in fine clay are indicative of a lacustrine environment. Fine particles were brought into the depositional environments by aquatic input and settled from suspension; also, direct dust input is constrained by grain‐size results. Riverine input and aeolian dust input interplayed at the locality.