Mechanisms of partly flooded loose sand deposits

Partly flooded loose sand deposits are left back from lignite mining, particularly in east Germany. After flooding, gas inclusions in macropores enhance the pore pressure increase by total stress cycles without drainage, and this can cause the transition into a kind of suspension. Thus, natural and technical changes of surface loads triggered flow slides in chain reactions of sometimes catastrophic extent. Such collapsible deposits were and are stabilized by blast-induced liquefaction and densification. Successive collapse fronts in flooded grain skeletons are seismogeneous, and this appears as wavering in the near-field and leads to power-law spectra in the far-field. Apart from this evidence of mild fractality, the wild fractality of chain reactions eludes mathematical treatment. Therefore, uncommon methods of geotechnical design, operations and monitoring are required.     

Intrinsic groundwater vulnerability determination at the aquifer scale: a methodology coupling travel time estimation and rating methods

Groundwater vulnerability has been subject of much research due to the valuable information it provides concerning groundwater protection and exploitation potential. Up to now, most groundwater vulnerability studies adopt subjective systems of rating the various factors and subsequently, their results are often ambiguous and contradicting. Within the present study a methodology for the estimation of intrinsic groundwater vulnerability at the aquifer scale is presented. The methodology is based on travel time estimation from specified sources of pollution to the aquifer. Besides the deterministic calculation of travel times, the methodology provides a rating system for each pollution source, based on its relative severity and the estimated threat that it poses to the aquifer. Therefore, it can be regarded as a hybrid method that couples the advantages provided by the physically based methods with those of the subjective rating systems. The methodology is applied to the Neon Sidirochorion aquifer, Northeastern Greece, an overexploited aquifer where river waters, sea waters and lake waters interact, causing groundwater quality deterioration to the aquifer. The results indicated that the proposed groundwater vulnerability assessment methodology is well capturing pollution related to saltwater intrusion and agricultural activities, while it is concluded that the conceptual model is significantly affecting the vulnerability assessment results and therefore has to be previously developed.     

Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique

Blasting operations usually produce significant environmental problems which may cause severe damage to the nearby areas. Air-overpressure (AOp) is one of the most important environmental impacts of blasting operations which needs to be predicted and subsequently controlled to minimize the potential risk of damage. In order to solve AOp problem in Hulu Langat granite quarry site, Malaysia, three non-linear methods namely empirical, artificial neural network (ANN) and a hybrid model of genetic algorithm (GA)–ANN were developed in this study. To do this, 76 blasting operations were investigated and relevant blasting parameters were measured in the site. The most influential parameters on AOp namely maximum charge per delay and the distance from the blast-face were considered as model inputs or predictors. Using the five randomly selected datasets and considering the modeling procedure of each method, 15 models were constructed for all predictive techniques. Several performance indices including coefficient of determination (R ²), root mean square error and variance account for were utilized to check the performance capacity of the predictive methods. Considering these performance indices and using simple ranking method, the best models for AOp prediction were selected. It was found that the GA–ANN technique can provide higher performance capacity in predicting AOp compared to other predictive methods. This is due to the fact that the GA–ANN model can optimize the weights and biases of the network connection for training by ANN. In this study, GA–ANN is introduced as superior model for solving AOp problem in Hulu Langat site.