Non-parametric bill of quantities estimation of concrete road bridges' superstructure: an artificial neural networks approach

Bridge construction responds to the need for environmentally friendly design of motorways and facilitates the passage through sensitive natural areas and the bypassing of urban areas. However, according to numerous research studies, bridge construction presents substantial budget overruns. Therefore, it is necessary early in the planning process for the decision makers to have reliable estimates of the final cost based on previously constructed projects. At the same time, the current European financial crisis reduces the available capital for investments and financial institutions are even less willing to finance transportation infrastructure. Consequently, it is even more necessary today to estimate the budget of high-cost construction projects -such as road bridges- with reasonable accuracy, in order for the state funds to be invested with lower risk and the projects to be designed with the highest possible efficiency. In this paper, a Bill-of-Quantities (BoQ) estimation tool for road bridges is developed in order to support the decisions made at the preliminary planning and design stages of highways. Specifically, a Feed-Forward Artificial Neural Network (ANN) with a hidden layer of 10 neurons is trained to predict the superstructure material quantities (concrete, pre-stressed steel and reinforcing steel) using the width of the deck, the adjusted length of span or cantilever and the type of the bridge as input variables. The training dataset includes actual data from 68 recently constructed concrete motorway bridges in Greece. According to the relevant metrics, the developed model captures very well the complex interrelations in the dataset and demonstrates strong generalisation capability. Furthermore, it outperforms the linear regression models developed for the same dataset. Therefore, the proposed cost estimation model stands as a useful and reliable tool for the construction industry as it enables planners to reach informed decisions for technical and economic planning of concrete bridge projects from their early implementation stages.

Numerical analysis of complex failure mechanisms in composite panels

A three-dimensional continuum damage mechanics-based material model has been implemented in an implicit Finite Element code to simulate the progressive degradation of advanced composite materials. The damage model uses seven damage variables assigned to tensile, compressive and non-linear shear damage at a laminae level. The objectivity of the numerical discretization is assured using a smeared formulation. The material model was benchmarked against experimental uniaxial coupon tests and it is shown to reproduce key aspects observable during failure, such as the inclined fracture plane in matrix compression and the shear band in a ±45° tension specimen.

Eradication and phenotypic tolerance of Burkholderia cenocepacia biofilms exposed to atmospheric pressure non-thermal plasma.

Chronic lung infection with bacteria from the Burkholderia cepacia complex (BCC), and in particular B. cenocepacia, is associated with significant morbidity and mortality in patients with cystic fibrosis (CF). B. cenocepacia can spread from person to person and exhibits intrinsic broad-spectrum antibiotic resistance. Recently, atmospheric pressure non-thermal plasmas (APNTPs) have gained increasing attention as a novel approach to the prevention and treatment of a variety of hospital-acquired infections. In this study, we evaluated an in-house-designed kHz-driven plasma source for the treatment of biofilms of a number of clinical CF B. cenocepacia isolates. The results demonstrated that APNTP is an effective and efficient tool for the eradication of B. cenocepacia biofilms but that efficacy is highly variable across different isolates. Determination of phenotypic differences between isolates in an attempt to understand variability in plasma tolerance revealed that isolates which are highly tolerant to APNTP typically produce biofilms of greater biomass than their more sensitive counterparts. This indicates a potential role for biofilm matrix components in biofilm tolerance to APNTP exposure. Furthermore, significant isolate-dependent differences in catalase activity in planktonic bacteria positively correlated with phenotypic resistance to APNTP by isolates grown in biofilms.

Complete framework for efficient characterisation of non-Markovian processes

A non-Markovian process is one that retains `memory' of its past. A systematic understanding of these processes is necessary to fully describe and harness a vast range of complex phenomena; however, no such general characterisation currently exists. This long-standing problem has hindered advances in understanding physical, chemical and biological processes, where often dubious theoretical assumptions are made to render a dynamical description tractable. Moreover, the methods currently available to treat non-Markovian quantum dynamics are plagued with unphysical results, like non-positive dynamics. Here we develop an operational framework to characterise arbitrary non-Markovian quantum processes. We demonstrate the universality of our framework and how the characterisation can be rendered efficient, before formulating a necessary and sufficient condition for quantum Markov processes. Finally, we stress how our framework enables the actual systematic analysis of non-Markovian processes, the understanding of their typicality, and the development of new master equations for the effective description of memory-bearing open-system evolution.

Complex contests and the influence of aggressiveness in pigs

Animal contests vary greatly in behavioural tactics used and intensity reached, with some encounters resolved without physical contact while others escalate to damaging fighting. However, the reasons for such variation remains to be fully explained. Aggressiveness, in terms of a personality trait, offers a potentially important source of variation that has typically been overlooked. Therefore, we studied how aggressiveness as a personality trait influenced escalation between contestants matched for resource holding potential (RHP), using detailed observations of the contest behaviour, contest dynamics, and escalation levels. We predicted that winner and loser behaviour would differ depending on personality. This was tested by examining 52 dyadic contests between pigs (Sus scrofa). Aggressiveness was assayed in resident-intruder tests prior to the contest. Contests were then staged between pigs matched for RHP in terms of body weight but differing in their aggressiveness. In 27% of the contests a winner emerged without escalated physical fighting, demonstrating that a fight is not a prerequisite between RHP-matched contestants. However, the duration of contests with or without fighting was the same. In contests without a fight, opponents spent more time on mutual investigation and non-contact displays such as parallel walking, which suggests that ritualized display may facilitate assessment and decision making. Winners low in aggressiveness invested more time in opponent investigation and display and showed substantially less aggression towards the loser after its retreat compared to aggressive winners. Aggressiveness influenced contest dynamics but did not predict the level of escalation. Prominent behavioural differences were found for the interaction between personality and outcome and we therefore recommend including this interaction in models where personality is considered. Analyses based on contest duration only would miss many of the subtleties which are shown here and we therefore encourage more detailed analyses of animal contests, irrespective of the level of contest escalation.