Ecologically based hybrid life cycle analysis of continuously reinforced concrete and hot-mix asphalt pavements

An ecologically-based hybrid life cycle assessment model is used to evaluate the resource consumption and atmospheric emissions of continuously reinforced concrete and a hot-mix asphalt pavements. The cumulative mass and ecological resource consumption values are lower for continuously reinforced concrete, but the median values of cumulative energy and industrial energy consumption were lower for hot-mix asphalt. In addition, the use of reclaimed asphalt pavement results in a higher sensitivity for the ecological resource consumption of hot-mix asphalt compared to that of fly ash when use on the natural capital utilization of continuously reinforced concrete pavement. The cumulative and industrial exergy consumption values are significantly reduced with increases in reclaimed asphalt pavement and fly ash, and the use of low fuel transportation modes.     

Prediction of wave parameters by using fuzzy inference system and the parametric models along the south coasts of the Black Sea

Forecasting of sea-state characteristics has a great importance in coastal and ocean engineering studies. Therefore, the purpose of this study was to investigate performances of Adaptive-Network-Based Fuzzy Inference System (ANFIS) and several parametric methods in the Black Sea. For this purpose, different fuzzy models with different input combinations were developed for two different wind data sources (TSMS and ECMWF) at two offshore buoy stations. It also aimed to apply several approaches to event-based data sets for wave predictions. Generally, in literature the tendency is to use time series data for wave predictions. In this kind of prediction approach, lagged time series data are taken as inputs and current or future variables are taken as output. In this study, event-based data for each independent storm were extracted from time series data. Simultaneous or concurrent data of wind speed, blowing duration, fetch length and wave characteristics were detected for each single storm. These event data were then used to set up models. The hindcast results were validated with significant wave height and mean wave period data recorded in Hopa and Sinop buoy stations. The performance of developed fuzzy models were also compared with that of four different parametric methods (Wilson, SPM, Jonswap, and CEM methods) applied for two wind data sources at both buoy stations. Finally, it was determined that in the prediction of both wave parameters (H _s and T _z) the ANFIS models (R = 0.66, squared correlation coefficient, and MAE = 0.37 m, mean absolute error, for the best model in prediction of H _s) were more accurate than the parametric methods (R = 0.63 and MAE = 0.75 m for the best model in prediction of H _s).     

Skid road effects on annual ring widths and diameter increment of fir (Abies bornmulleriana Mattf.) trees

This study examines annual ring widths, diameter growth and increment differences of trees at a skid road and the undisturbed area in a pure plantation fir (Abies bornmulleriana Mattf.) forest and compare these data with annual ring widths diameter growth and increment of trees at the skid road edge and undisturbed forest area. The trees growing there commonly display better growth and diameter increment than the trees growing on the skid road. Tree growth and increment on the undisturbed area was found to be about 60% greater than the skid road.     

An application of BBNs on the integrated energy efficiency of ship–port interface: a dry bulk shipping case

ABSTRACT

A considerable amount of energy is consumed with every completed ship voyage and each port operation performed. Recent regulative approaches by the IMO are to enforce the shipping industry to become more energy efficient and sustainable. There is a growing amount of literature on the energy efficiency management of ships and ports. However, there is still inadequate research effort on the ship–port interface in order to obtain an Integrated Energy Efficiency framework for marine transport operations. In this research, a theoretical concept is generated to measure holistic energy efficiency in shipping operations. The main purpose of this paper is to identify nodes of the integrated ship-port energy efficiency framework and develop a probabilistic approach, which can help to increase energy efficiency and reduce CO2 emissions for shipping companies. This research generates an application of BBNs on ship–port interface regarding the integrated operational energy efficiency interactions by aiming to optimise holistic operational energy efficiency and CO2 emissions. The outcomes suggest that it would be possible to increase the probability of a more energy efficient and sustainable marine transport operation by integrating the influences of port and ship operation performances and their elements on the related route planning and slow steaming decision-making.     

Modelling trip distribution with fuzzy and genetic fuzzy systems

This paper explores the potential capabilities of fuzzy and genetic fuzzy system approaches in urban trip distribution modelling with some new features. First, a simple fuzzy rule-based system (FRBS) and a novel genetic fuzzy rule-based system [GFRBS: a fuzzy system improved by a knowledge base learning process with genetic algorithms (GAs)] are designed to model intra-city passenger flows for Istanbul. Subsequently, their accuracy, applicability and generalizability characteristics are evaluated against the well-known gravity- and neural network (NN)-based trip distribution models. The overall results show that: traditional doubly constrained gravity models are still simple and efficient; NNs may not show expected performance when they are forced to satisfy trip constraints; simply-designed FRBSs, learning from observations and expertise, are both efficient and interpretable even if the data are large and noisy; and use of GAs in fuzzy rule-based learning considerably increases modelling performance, although it brings additional computation cost.