Damage assessment of basaltic rock mass due to repeated blasting in a railway tunnelling project - A case study

The demand for tunnelling and underground space creation is rapidly growing due to the requirement of civil infrastructure projects and urbanisation. Blasting remains the most inexpensive method of underground excavations in hard rock. Unfortunately, there are no specific safety guidelines available for the blasted tunnels with regards to the threshold limits of vibrations caused by repeated blasting activity in the close proximity. This paper presents the results of a comprehensive study conducted to find out the effect of repeated blast loading on the damage experienced by jointed basaltic rock mass during tunnelling works. Conducting of multiple rounds of blasts for various civil excavations in a railway tunnel imparted repeated loading on rock mass of sidewall and roof of the tunnel. The blast induced damage was assessed by using vibration attenuation equations of charge weight scaling law and measured by borehole extensometers and borehole camera. Ground vibrations of each blasting round were also monitored by triaxial geophones installed near the borehole extensometers. The peak particle velocity (V-max) observations and plastic deformations from borehole extensometers were used to develop a site specific damage model. The study reveals that repeated dynamic loading imparted on the exposed tunnel from subsequent blasts, in the vicinity, resulted in rock mass damage at lesser vibration levels than the critical peak particle velocity (V-cr). It was found that, the repeated blast loading resulted in the near-field damage due to high frequency waves and far-field damage due to low frequency waves. The far field damage, after 45-50 occurrences of blast loading, was up to 55% of the near-field damage in basaltic rock mass. The findings of the study clearly indicate that the phenomena of repeated blasting with respect to number of cycles of loading should be taken into consideration for proper assessment of blast induced damage in underground excavations.

Massive black-hole binary inspirals: results from the LISA parameter estimation taskforce

The LISA Parameter Estimation Taskforce was formed in September 2007 to provide the LISA Project with vetted codes, source distribution models and results related to parameter estimation. The Taskforce's goal is to be able to quickly calculate the impact of any mission design changes on LISA's science capabilities, based on reasonable estimates of the distribution of astrophysical sources in the universe. This paper describes our Taskforce's work on massive black-hole binaries (MBHBs). Given present uncertainties in the formation history of MBHBs, we adopt four different population models, based on (i) whether the initial black-hole seeds are small or large and (ii) whether accretion is efficient or inefficient at spinning up the holes. We compare four largely independent codes for calculating LISA's parameter-estimation capabilities. All codes are based on the Fisher-matrix approximation, but in the past they used somewhat different signal models, source parametrizations and noise curves. We show that once these differences are removed, the four codes give results in extremely close agreement with each other. Using a code that includes both spin precession and higher harmonics in the gravitational-wave signal, we carry out Monte Carlo simulations and determine the number of events that can be detected and accurately localized in our four population models.

A hydro-wood net-energy approach to hydro project design

An integrated approach to energy planning, when applied to large hydroelectric projects, requires that the energy-opportunity cost of the land submerged under the reservoir be incorporated into the planning methodology. Biomass energy lost from the submerged land has to be compared to the electrical energy generated, for which we develop four alternative formulations of the net-energy function. The design problem is posed as an LP problem and is solved for two sites in India. Our results show that the proposed designs may not be viable in net-energy terms, whereas a marginal reduction in the generation capacity could lead to an optimal design that gives substantial savings in the submerged area. Allowing seasonal variations in the hydroelectric generation capacity also reduces the reservoir size. A mixed hydro-wood generation system is then examined and is found to be viable.

Hydropower Project Design Incorporating Submergence Costs

Submergence of land is a major impact of large hydropower projects. Such projects are often also dogged by siltation, delays in construction and heavy debt burdens-factors that are not considered in the project planning exercise. A simple constrained optimization model for the benefit~ost analysis of large hydropower projects that considers these features is proposed. The model is then applied to two sites in India. Using the potential productivity of an energy plantation on the submergible land is suggested as a reasonable approach to estimating the opportunity cost of submergence. Optimum project dimensions are calculated for various scenarios. Results indicate that the inclusion of submergence cost may lead to a substanual reduction in net present value and hence in project viability. Parameters such as project lifespan, con$truction time, discount rate and external debt burden are also of significance. The designs proposed by the planners are found to be uneconomic, whIle even the optimal design may not be viable for more typical scenarios. The concept of energy opportunity cost is useful for preliminary screening; some projects may require more detailed calculations. The optimization approach helps identify significant trade-offs between energy generation and land availability.

Nucleotide sequence of the first cosmid from the Mycobacterium leprae genome project: structure and function of the Rif-Str regions

The nucleotide sequence of cosmid B1790, carrying the Rif-Str regions of the Mycobacterium leprae chromosome, has been determined. Twelve open reading frames were identified in the 36716bp sequence, representing 40% of the coding capacity. Five ribosomal proteins, two elongation factors and the β and β'subunits of RNA polymerase have been characterized and two novel genes were found. One of these encodes a member of the so-called ABC family of ATP-binding proteins while the other appears to encode an enzyme involved in repairing genomic lesions caused by free radicals. This finding may well be significant as M. leprae, an intracellular pathogen, lives within macrophages.

The top 100 questions of importance to the future of global agriculture

Despite a significant growth in food production over the past half-century, one of the most important challenges facing society today is how to feed an expected population of some nine billion by the middle of the 20th century. To meet the expected demand for food without significant increases in prices, it has been estimated that we need to produce 70-100 per cent more food, in light of the growing impacts of climate change, concerns over energy security, regional dietary shifts and the Millennium Development target of halving world poverty and hunger by 2015. The goal for the agricultural sector is no longer simply to maximize productivity, but to optimize across a far more complex landscape of production, rural development, environmental, social justice and food consumption outcomes. However, there remain significant challenges to developing national and international policies that support the wide emergence of more sustainable forms of land use and efficient agricultural production. The lack of information flow between scientists, practitioners and policy makers is known to exacerbate the difficulties, despite increased emphasis upon evidence-based policy. In this paper, we seek to improve dialogue and understanding between agricultural research and policy by identifying the 100 most important questions for global agriculture. These have been compiled using a horizon-scanning approach with leading experts and representatives of major agricultural organizations worldwide. The aim is to use sound scientific evidence to inform decision making and guide policy makers in the future direction of agricultural research priorities and policy support. If addressed, we anticipate that these questions will have a significant impact on global agricultural practices worldwide, while improving the synergy between agricultural policy, practice and research. This research forms part of the UK Government's Foresight Global Food and Farming Futures project.

Frontier Based Multiple Goal Search in Unknown Environments

We present a frontier based algorithm for searching multiple goals in a fully unknown environment, with only information about the regions where the goals are most likely to be located. Our algorithm chooses an ``active goal'' from the ``active goal list'' generated by running a Traveling Salesman Problem (Tsp) routine with the given centroid locations of the goal regions. We use the concept of ``goal switching'' which helps not only in reaching more number of goals in given time, but also prevents unnecessary search around the goals that are not accessible (surrounded by walls). The simulation study shows that our algorithm outperforms Multi-Heuristic LRTA* (MELRTA*) which is a significant representative of multiple goal search approaches in an unknown environment, especially in environments with wall like obstacles.

Goal Seeking for Robots in Unknown Environments

We consider the problem of goal seeking by robots in unknown environments. We present a frontier based algorithm for finding a route to a goal in a fully unknown environment, where information about the goal region (GR), the region where the goal is most likely to be located, is available. Our algorithm efficiently chooses the best candidate frontier cell, which is on the boundary between explored space and unexplored space, having the maximum ``goal seeking index'', to reach the goal in minimal number of moves. Modification of the algorithm is also proposed to further reduce the number of moves toward the goal. The algorithm has been tested extensively in simulation runs and results demonstrate that the algorithm effectively directs the robot to the goal and completes the search task in minimal number of moves in bounded as well as unbounded environments. The algorithm is shown to perform as well as a state of the art agent centered search algorithm RTAA*, in cluttered environments if exact location of the goal is known at the beginning of the mission and is shown to perform better in uncluttered environments.

Towards building an intelligent decision support system for project management

Management of large projects, especially the ones in which a major component of R&D is involved and those requiring knowledge from diverse specialised and sophisticated fields, may be classified as semi-structured problems. In these problems, there is some knowledge about the nature of the work involved, but there are also uncertainties associated with emerging technologies. In order to draw up a plan and schedule of activities of such a large and complex project, the project manager is faced with a host of complex decisions that he has to take, such as, when to start an activity, for how long the activity is likely to continue, etc. An Intelligent Decision Support System (IDSS) which aids the manager in decision making and drawing up a feasible schedule of activities while taking into consideration the constraints of resources and time, will have a considerable impact on the efficient management of the project. This report discusses the design of an IDSS that helps in project planning phase through the scheduling phase. The IDSS uses a new project scheduling tool, the Project Influence Graph (PIG).

Machine-tool selection and operation allocation in FMS: solving a fuzzy goal-programming model using a genetic algorithm

The decision-making process for machine-tool selection and operation allocation in a flexible manufacturing system (FMS) usually involves multiple conflicting objectives. Thus, a fuzzy goal-programming model can be effectively applied to this decision problem. The paper addresses application of a fuzzy goal-programming concept to model the problem of machine-tool selection and operation allocation with explicit considerations given to objectives of minimizing the total cost of machining operation, material handling and set-up. The constraints pertaining to the capacity of machines, tool magazine and tool life are included in the model. A genetic algorithm (GA)-based approach is adopted to optimize this fuzzy goal-programming model. An illustrative example is provided and some results of computational experiments are reported.

Non-Gaussianity and direction-dependent systematics in HST Key Project data

Two new statistics, namely Delta(chi 2) and Delta(chi), based on the extreme value theory, were derived by Gupta et al. We use these statistics to study the direction dependence in the HST Key Project data, which provides one of the most precise measurements of the Hubble constant. We also study the non-Gaussianity in this data set using these statistics. Our results for Delta(chi 2) show that the significance of direction-dependent systematics is restricted to well below the 1 sigma confidence limit; however, the presence of non-Gaussian features is subtle. On the other hand, the Delta(chi). statistic, which is more sensitive to direction dependence, shows direction dependence systematics to be at a slightly higher confidence level, and the presence of non-Gaussian features at a level similar to the Delta(chi 2) statistic.