Calculation Method of Probability Integration Method Parameters Based on MIV-GP-BP Model

In order to guarantee the precision of the parameters of the probability integral method (PIM), starting from optimizing input and improving algorithm an algorithm integrating the genetic algorithm (GA) and particle swarm optimization (PSO) was put forward to optimize the prediction model of BP neural network and the mean impact value algorithm (MIV) was applied to optimize the input of BP neural network. The mean impact value algorithm (MIV) was applied to optimize the input of BP neural network. The measured data of 50 working faces were chosen as the training and testing sets to build the MIV-GP-BP model. The results showed that among the five parameters, the RMSE was between 0.0058 and 1.1575, the MaxRE of q, tanβ, b and θ was less than 5.42%, and the MeaRE was less than 2.81%. The RMSE of s/H did not exceed 0.0058, the MaxRE was less than 9.66% and the MeaRE was less than 4.31% (the parameters themselves were small). The optimized neural network model had higher prediction accuracy and stability

Tunneling-induced ground movement and building damage prediction using hybrid artificial neural networks

The construction of tunnels in urban areas may cause ground displacement which distort and damage overlying buildings and services. Hence, it is a major concern to estimate tunneling-induced ground movements as well as to assess the building damage. Artificial neural networks (ANN), as flexible non-linear function approximations, have been widely used to analyze tunneling-induced ground movements. However, these methods are still subjected to some limitations that could decrease the accuracy and their applicability. The aim of this research is to develop hybrid particle swarm optimization (PSO) algorithm-based ANN to predict tunneling-induced ground movements and building damage. For that reason, an extensive database consisting of measured settlements from 123 settlement markers, geotechnical parameters, tunneling parameters and properties of 42 damaged buildings were collected from Karaj Urban Railway project in Iran. Based on observed data, the relationship between influential parameters on ground movements and maximum surface settlements were determined. A MATLAB code was prepared to implement hybrid PSO-based ANN models. Finally, an optimized hybrid PSO-based ANN model consisting of eight inputs, one hidden layer with 13 nodes and three outputs was developed to predict three-dimensional ground movements induced by tunneling. In order to assess the ability and accuracy of the proposed model, the predicted ground movements using proposed model were compared with the measured settlements. For a particular point, ground movements were obtained using finite element model by means of ABAQUS and the results were compared with proposed model. In addition, an optimized model consisting of seven inputs, one hidden layer with 21 nodes and one output was developed to predict building damage induced by ground movements due to tunneling. Finally, data from damaged buildings were used to assess the ability of the proposed model to predict the damage. As a conclusion, it can be suggested that the newly proposed PSO-based ANN models are able to predict three-dimensional tunneling-induced ground movements as well as building damage in tunneling projects with high degree of accuracy. These models eliminate the limitations of the current ground movement and building damage predicting methods

Intelligent prediction model based on genetic algorithm and support vector machine for evaluation of mining-induced building damage

Značajke čimbenika koji utječu na štetu nastalu na zgradama zbog iskapanja zemlje su različite, nelinearne i multi linearne. Za bolji opis tih čimbenika razvijen je inteligentni model zasnovan na potpornom vektorskom stroju (SVM) kojim se može predvidjeti šteta na zgradama nastala podzemnim iskapanjem. Na temelju opsežnog razmatranja geoloških, rudarskih i građevnih faktora, 10 ih je pažljivo odabrano. Posebice je, kao glavna ulazna varijabla u predloženom modelu, upotrebljen stupanj oštećenja građevine od opeke i betona, nastao podzemnim iskapanjem. Stupanj oštećenja i najšira pukotina građevinske konstrukcije od opeke i betona izabrani su kao izlazne varijable u predloženom modelu. Ukupno su odabrana 32 tipična slučaja oštećenja zgrada u Kini zbog iskapanja zemlje te upotrebljena kao podaci za uvježbavanje (training data). Funkcija radijalne baze (radial basis function – RBF) upotrebljena je za SVM klasifikaciju i primjenu modela regresije s najvećom širinom pukotine. Kako bi primjena modela bila što šira i njegova sposobnost predviđanja što veća, za izbor učinkovitih parametara za SVM model upotrebljen je genetski algoritam (GA), i tada je izvršena odgovarajuća identifikacija šest grupa uzoraka. Rezultati klasifikacije i regresije pokazuju da se predloženim modelom, koji koristi GA-SVM, može predvidjeti šteta na konstrukciji od opeke i betona, nastala iskapanjem zemlje, a rezultati procjene u skladu su s praćenim podacima. To navodi na praktičnost primjene predloženog modela u rješavanju različitih inženjerskih problema.Characteristics of factors influencing mining-induced building damage are diverse, nonlinear, and multi-linear. For a better description of these factors, an intelligent prediction model for building damage induced by underground mining is developed based on the support vector machine (SVM). Based on a comprehensive consideration of geological, mining, and building factors, 10 factors are carefully selected. In particular, the mining-induced damage grade of the brick-concrete building structure is used as the main input variable in the proposed model. The damage grade and largest crack width of the brick-concrete building structure are selected as output variables in the proposed model. A total of 32 typical cases of mining-induced building damage in China are collected and used as training data. The radial basis function (RBF) is used for SVM classification and the application of the largest-crack-width regression model. To improve the model’s generalizability and predictive capacity, the genetic algorithm (GA) is adopted to select effective parameters for the SVM model, and then the corresponding identification of six group samples is performed. The classification and regression results show that the proposed prediction model using GA-SVM can predict the mining-induced damage of a brick-concrete building structure, and the evaluation results show good agreement with monitored data. This suggests the practicality of the proposed model in a wide range of engineering problems

Mining Safety and Sustainability I

Safety and sustainability are becoming ever bigger challenges for the mining industry with the increasing depth of mining. It is of great significance to reduce the disaster risk of mining accidents, enhance the safety of mining operations, and improve the efficiency and sustainability of development of mineral resource. This book provides a platform to present new research and recent advances in the safety and sustainability of mining. More specifically, Mining Safety and Sustainability presents recent theoretical and experimental studies with a focus on safety mining, green mining, intelligent mining and mines, sustainable development, risk management of mines, ecological restoration of mines, mining methods and technologies, and damage monitoring and prediction. It will be further helpful to provide theoretical support and technical support for guiding the normative, green, safe, and sustainable development of the mining industry

Remote Sensing of Natural Hazards

Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches

Persistent scatterer interferometry to monitor mining related ground surface deformation for data-driven modelling

The monitoring, interpretation and prediction of gradual ground surface deformation are critical factors in the understanding of earth systems. In many parts of the world, particularly in coastal areas where resources are often easily transportable and where steep cliffs allow access to underlying strata, the patterns of natural ground surface deformation are complicated by mining or extraction activities. To accurately estimate the amount of sea-level rise and Its total affect on, for example, frequency of flooding or salt-water intrusion, the amount of ground surface deformation, either subsidence or uplift, need to be understood in great detail. Ground surface dynamics over an area of contemporary deep mining, IS investigated through two research objectives. A feasibility study of conventional InSAR and Persistent Scatterer InSAR (PSI) in a rural setting was carried out. Rural areas are generally avoided for the application of these techniques for the measurement of gradual ground surface deformation due to the lack of coherence between scenes. The results demonstrate that the new PSI technique significantly outperformed conventional InSAR m the detection of gradual ground surface deformation. However, limitations to the technique were identified in the low density and limited distribution of permanent scatterers. The behaviour of the deformation rate over time appears to be biased to a linear trend. Furthermore, in order to understand the link between underground mining activities and local ground surface response a data-driven model has been developed and evaluated. Based on different mining scenarios, this mode! IS able to estimate the total subsidence in a four dimensional space. It was found that local ground surface deformation can be forecasted accurately, based on an angle of draw and four variables. Five key indicators, which are the extent of die disturbed area, the total period of deformation, the peak rate, the moment of the peak rate and the total deformation, are relevant to understand the impact of underground excavations on the surface and to place it in a wider Earth system

Land Surface Monitoring Based on Satellite Imagery

This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought

Rising groundwater levels in the Neapolitan area and its impacts on civil engineering structures, agricultural soils and archaeological sites

The rise of groundwater levels (GWLr) is a worldwide phenomenon with several consequences for urban and rural environment, cultural heritage and human health. In this thesis the phenomenon and its effects are analysed in two sectors of the Metropolitan City of Naples (southern Italy). These areas are the central sector of the eastern plain of Naples and the Cumae archaeological site in the western coastal sector of Phlegraean Fields. The triggering mechanism of GWLr is attributed to anthropogenic and natural causes, as the groundwater rebound (GR) process and the relative sea level rise due to volcano-tectonic subsidence of coastal areas. In the eastern plain of Naples, the interruption of pumping for public and private purposes occurred in 1990, leading to a progressive increase of piezometric levels with values up to 16.54 m. Since the end of 2000s, episodes of groundwater flooding (GF) have been registered on underground structures and agricultural soils. The historical piezometric levels and a comprehensive conceptual model of the aquifer have been reconstructed, as well as a first inventory of GF episodes and the hydrogeological controlling factors of GF occurrence have been detected. The economic consequences of GF have been analysed for an experimental building of study area, in which a sharp increment of expenditures has been registered. These costs include technical and legal support, construction and maintenance of GF mitigation measures and electricity consumption. Others GWLr-induced phenomena have been recognised, as ground vertical deformation and variations of the groundwater contamination. A relationship between GWLr and ground uplift emerges from the coupled analysis of piezometric and interferometric data, referred to the 1989-2013 period. The ground deformation occurs in response to the recovery of pore-pressure in the aquifer system, reaching an uplift magnitude up to 40-50 mm. In the 1989-2017 period, the piezometric levels and the concentrations of some natural contaminants in groundwater (Fe, Mn, fluorides) show opposite trends, conversely the same rising trend has been observed with nitrates. These different responses to piezometric rise are related to the lack of mobilization of deep fluids due to the interruption of pumping and to the reduction of the surficial contaminants' time travel caused by a shorter thickness of the vadose zone. In the western sector of Phlegraean Fields, the naturally triggered GWLr has caused GF in the Cumae archaeological site for the last decade, threatening safeguard and conservation of the archaeological heritage. From an integrated hydrogeological, hydrochemical and isotopic survey, a considerable contamination of groundwater resulted, due to the presence of rising highly mineralized fluids, mobilized during pumping periods, and others anthropogenic sources of contamination. Lastly, a novel methodology for groundwater flooding susceptibility (GFS) assessment has been developed by using machine learning techniques and tested in the eastern plain of Naples. Points of GF occurrence have been connected to environmental predisposing factors through Spatial Distribution Models' algorithms to estimate the most prone areas' distribution. Ensemble Models have been carried out to reduce the uncertainty associated with each algorithm and increase its reliability. Mapping of GFS has been realized by dividing occurrence probability values into five classes of susceptibility. Results show an optimal correspondence between GF points' location and the highest classes (93% of GF points falls into high and very high classes). The results of this research provide new knowledge on the GWLr phenomenon that has impacted a large territory of the Metropolitan City of Naples. The methodological approach used can be exported in others hydrogeological contexts to characterize GWLr and its impacts. In addition, the implemented GFS methodology represents a new tool to assist local government authorities, planners and water decision-makers in addressing the problems deriving from GF, and a first step for the evaluation of GF risk as required by Italian and European legislation

Solar Power System Plaing & Design

Photovoltaic (PV) and concentrated solar power (CSP) systems for the conversion of solar energy into electricity are technologically robust, scalable, and geographically dispersed, and they possess enormous potential as sustainable energy sources. Systematic planning and design considering various factors and constraints are necessary for the successful deployment of PV and CSP systems. This book on solar power system planning and design includes 14 publications from esteemed research groups worldwide. The research and review papers in this Special Issue fall within the following broad categories: resource assessments, site evaluations, system design, performance assessments, and feasibility studies