Improving rockbolt design in tunnels using topology optimization

Finding an optimum reinforcement layout for underground excavation can result in a safer and more economical design, and is therefore highly desirable. Some works in the literature have applied topology optimization in tunnel reinforcement design in which reinforced rock is modeled as homogenized isotropic material. Optimization results, therefore, do not clearly show reinforcement distributions, leading to difficulties in explaining the final outcomes. To overcome this deficiency, a more sophisticated modeling technique in which reinforcements are explicitly modeled as truss elements embedded in rock mass media is used. An optimization algorithm extending the solid isotropic material with penalization method is introduced to seek for an optimal bolt layout. To obtain the stiffest structure with a given amount of reinforced material, external work along the opening is selected as the objective function with a constraint on the volume of reinforcement. The presented technique does not depend on material models used for rock and reinforcements and can be applied to any material model. Nonlinear material behavior of rock and reinforcement is considered in this work. Through solving some typical examples, the proposed approach is proved to enhance the conventional reinforcement design and provide clear and practical reinforcement layouts.

Commensalism used by freshwater crayfish species to survive drying in seasonal habitats

Gramastacus insolitus is a very small non-burrowing Australian freshwater crayfish with a restricted distribution, occurring almost exclusively in seasonal habitats throughout its range. It is listed as a threatened species but its strategy for surviving dry periods was unknown. Eight seasonal surveys of crayfish distribution showed that members of G. insolitus were never found at sites that were outside the distribution of two larger burrowing freshwater crayfish species, Geocharax falcata and Cherax destructor. Excavation of 80 burrows of members of G. falcata and C. destructor in three different seasonal habitats in the Grampians National Park, Victoria, Australia, revealed that individuals of G. insolitus found refuge from drying by estivating in cracks and shallow depressions at the side of the main burrow tunnels constructed by larger species. Members of G. insolitus were not found estivating at the surface, such as under fallen wood, nor was it usually found in crayfish burrows unoccupied by the host crayfish. This study indicates that members of G. insolitus are commensal upon larger crayfish species, using their burrows to survive the seasonal drying of their habitat. Conservation strategies for populations of G. insolitus will need to consider co-existing species of burrowing crayfish.

A method for tracking rats in a complex and completely dark environment using computerized video analysis

The automated tracking of rodents in open field environments has become a standard laboratory technique for the investigation of the effects of drugs, novel therapeutic interventions and genetic mutations on behavior. Here, we develop an extension of this technique that permits tracking in full darkness through a complex (‘enriched’) environment comprising naturalistic structures such as tunnels and hides. To eliminate unwanted light reflections and tape noise, we developed a unique video filter that combines the advantages of differential and non-differential filtering. This filter enabled the tracking of albino rats against a relatively dark background to an accuracy of approximately 97% compared to hand tracking of the same animal, irrespective of whether the rat was inside a hide box or tunnel or out in the open field. The system as a whole can be easily deployed using standard PCs and inexpensive infrared cameras and lights.

Artificial neural network analysis of twin tunnelling-induced ground settlements

In this paper, we apply a computational intelligence method for tunnelling settlement prediction. A supervised feed forward back propagation neural network is used to predict the surface settlement during twin-tunnelling while surface buildings are considered in the models. The performance of the statistical neural network structure is tested on a dataset provided by numerical parametric studies conducted by ABAQUS software based on Shiraz line 1 metro data. Six input variables are fed to neural network model for predicting the surface settlement. These include tunnel center depth, distance between centerlines of twin tunnels, buildings width and building bending stiffness, and building weight and distance to tunnel centerline. Simulation results indicate that the proposed NN models are able to accurately predict the surface settlement.

Out of sight but not out of mind: corvids prey extensively on eggs of burrow-nesting penguins

Context Egg depredation is a major cause of reproductive failure among birds and can drive population declines. In this study we investigate predatory behaviour of a corvid (little raven; Corvus mellori) that has only recently emerged, leading to widespread and intense depredation of eggs of a burrow-nesting seabird (little penguin; Eudyptula minor). Aims The main objective of this study was to measure the rate of penguin egg depredation by ravens to determine potential threat severity. We also examined whether penguin burrow characteristics were associated with the risk of egg depredation. Ravens generally employ two modes of predatory behaviour when attacking penguin nests; thus we examined whether burrow characteristics were associated with these modes of attack. Methods Remote-sensing cameras were deployed on penguin burrows to determine egg predation rates. Burrow measurements, including burrow entrance and tunnel characteristics, were measured at the time of camera deployment. Key results Overall, clutches in 61% of monitored burrows (n≤203) were depredated by ravens, the only predator detected by camera traps. Analysis of burrow characteristics revealed two distinct types of burrows, only one of which was associated with egg depredation by ravens. Clutches depredated by ravens had burrows with wider and higher entrances, thinner soil or vegetation layer above the egg chamber, shorter and curved tunnels and greater areas of bare ground and whitewash near entrances. In addition, 86% were covered by bower spinach (Tetragonia implexicoma), through which ravens could excavate. Ravens used two modes to access the eggs: they attacked through the entrance (25% of burrow attacks, n≤124); or dug a hole through the burrow roof (75% of attacks, n≤124). Burrows that were subject to attack through the entrance had significantly shorter tunnels than burrows accessed through the roof. Conclusions The high rates of clutch loss recorded here highlight the need for population viability analysis of penguins to assess the effect of egg predation on population growth rates. Implications The subterranean foraging niche of a corvid described here may have implications for burrow-nesting species worldwide because many corvid populations are increasing, and they exhibit great capacity to adopt new foraging strategies to exploit novel prey. Journal compilation

Data mining analysis of an urban tunnel pressure drop based on CFD data

An accurate estimation of pressure drop due to vehicles inside an urban tunnel plays a pivotal role in tunnel ventilation issue. The main aim of the present study is to utilize computational intelligence technique for predicting pressure drop due to cars in traffic congestion in urban tunnels. A supervised feed forward back propagation neural network is utilized to estimate this pressure drop. The performance of the proposed network structure is examined on the dataset achieved from Computational Fluid Dynamic (CFD) simulation. The input data includes 2 variables, tunnel velocity and tunnel length, which are to be imported to the corresponding algorithm in order to predict presure drop. 10-fold Cross validation technique is utilized for three data mining methods, namely: multi-layer perceptron algorithm, support vector machine regression, and linear regression. A comparison is to be made to show the most accurate results. Simulation results illustrate that the Multi-layer perceptron algorithm is able to accurately estimate the pressure drop.