A neural network based general reservoir operation scheme

Construction of dams and the resulting water impoundments are one of the most common engineering procedures implemented on river systems globally; yet simulating reservoir operation at the regional and global scales remains a challenge in human–earth system interactions studies. Developing a general reservoir operating scheme suitable for use in large-scale hydrological models can improve our understanding of the broad impacts of dams operation. Here we present a novel use of artificial neural networks to map the general input/output relationships in actual operating rules of real world dams. We developed a new general reservoir operation scheme (GROS) which may be added to daily hydrologic routing models for simulating the releases from dams, in regional and global-scale studies. We show the advantage of our model in distinguishing between dams with various storage capacities by demonstrating how it modifies the reservoir operation in respond to changes in capacity of dams. Embedding GROS in a water balance model, we analyze the hydrological impact of dam size as well as their distribution pattern within a drainage basin and conclude that for large-scale studies it is generally acceptable to aggregate the capacity of smaller dams and instead model a hypothetical larger dam with the same total storage capacity; however we suggest limiting the aggregation area to HUC 8 sub-basins (approximately equal to the area of a 60 km or a 30 arc minute grid cell) to avoid exaggerated results.     

Applying Optimized Support Vector Regression Models for Prediction of Tunnel Boring Machine Performance

One of the main factors in the effective application of a tunnel boring machine (TBM) is the ability to accurately estimate the machine performance in order to determine the project costs and schedule. Predicting the TBM performance is a nonlinear and multivariable complex problem. The aim of this study is to predict the performance of TBM using the hybrid of support vector regression (SVR) and the differential evolution algorithm (DE), artificial bee colony algorithm (ABC), and gravitational search algorithm (GSA). The DE, ABC and GSA are combined with the SVR for determining the optimal value of its user defined parameters. The optimization implementation by the DE, ABC and GSA significantly improves the generalization ability of the SVR. The uniaxial compressive strength (UCS), average distance between planes of weakness (DPW), the angle between tunnel axis and the planes of weakness (α), and intact rock brittleness (BI) were considered as the input parameters, while the rate of penetration was the output parameter. The prediction models were applied to the available data given in the literature, and their performance was assessed based on statistical criteria. The results clearly show the superiority of DE when integrated with SVR for optimizing values of its parameters. In addition, the suggested model was compared with the methods previously presented for predicting the TBM penetration rate. The comparative results revealed that the hybrid of DE and SVR yields a robust model which outperforms other models in terms of the higher correlation coefficient and lower mean squared error.     

Image Segmentation for Hydrothermal Alteration Mapping Using PCA and Concentration–Area Fractal Model

Information extraction from processed remotely sensed images, in the case of missing initial spectra of pixels, can be a challenge for the users. In such situations, application of conventional methods based on spectral properties of pixels is impractical. We took advantage of the fractal theory for image segmentation of a principal component (PC) image for hydrothermal alteration mapping. The selected input images included short wave infrared bands of ASTER imagery covering the Darrehzar porphyry copper mine and surrounding areas with well-known hydrothermal alteration zones. Hydrothermal alteration like other geological processes can show spatial distribution with fractal properties. Principal component analysis was used to enhance hydrothermal alteration associated with the Darrehzar porphyry copper deposit. None of the resulting PCs were appropriate to portray clearly important alteration types in the study area. The PC1 image, which contains more than 98% of variance of the input bands, was selected for image segmentation using a digital number–area technique based on the established concentration–area fractal model. This technique was proposed based on frequency distributions and spatial correlation and variability of pixel values. The resulting hydrothermal alteration map indicates intense phyllic, weak phyllic, and propylitic as the main alteration types exposed at the surface of the Darrehzar area. In addition, the proposed technique delineated the phyllic zone in the exposed mine pit and a transition zone between inner phyllic and surrounding propylitic alteration zones. Field investigation and sampling in 23 locations including spectral measurements, XRD and thin section studies, confirmed the accuracy of the classified image by the proposed technique.     

Challenges in financing public sector low-carbon initiatives: lessons from private finance for a school district in British Columbia,Canada

Governments are major investors in climate change mitigation, but aversion to public indebtedness has led to reliance on private finance to deliver public assets. Compounding this challenge, financing through Energy Service Contracts is ruled out by accounting rules. With public and traditional private funding avenues closed, government departments have sought contracts that do not disclose the full cost of borrowing, such as the Public–Private Partnership (PPP) described in this case study. We unpack the utility contract filed with the provincial regulator to show that circumventing budgetary constraints cost the Delta School Board (DSB) 8.75% per annum on borrowed private funds while public finance would have cost 4%pa. All levels of the public sector are keen to play their role in climate mitigation. Climate policy is about not passing our burden of unbridled fossil fuel use and greenhouse gas emissions to future generations. If we do not exempt public sector capital investments for decarbonization from deficit regulations, we risk passing an unnecessary economic burden to future generations.

Key policy insights

• Transition to a low-carbon economy requires public sector investments that exceed budget deficit regulations and political aversion in many jurisdictions;

• Private–Public Partnerships are currently viewed as the solution to this self-imposed fiscal constraint;

• PPPs without clear performance targets or contractual templates will expose less experienced public sector investors to high costs and emissions above expectations.

     

The effect of surface profile, rock strength and pretension load on bending behaviour of fully grouted bolts

Fully encapsulated rock bolting has, in recent years, become a universally accepted system of ground reinforcement in mining and tunnel construction. The application of bolting systems extends both to rebar as well as cable bolting. The effectiveness of the bolt application has been studied in shear, both by laboratory tests as well as by numerical modeling. A specially constructed double shearing apparatus (DSA) was used to examine the shearing behaviour of a bolt installed perpendicularly across two joints. The experimental study was complemented with three-dimensional numerical analysis. Parameters examined include, the effect of reinforced material on tension/compression zones along the sheared bolt, shear resistance, shear displacement and induced strains and stresses during bolt bending process. The study was undertaken at both free load and pretension conditions. The conclusions drawn from the study were the level of bolt resistance to shear was influenced by bolt profile configuration, the strength of the rock or medium influenced the level of load generated on the bolt and the increased bolt pretension contributed to increased shearing load of the bolted medium. The numerical simulation of the bolt/medium interaction and deformational behaviour were found to be in close agreement with the experimental test results.     

Prediction of Blast-Induced Ground Vibration in a Mine Using Relevance Vector Regression Optimized by Metaheuristic Algorithms

Natural Resources Research - Prediction of ground vibration induced by blasting operations is a crucial challenge to engineers working in surface mines. This study aims to assess the efficiency of...