Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques

A reliable and accurate prediction of the tunnel boring machine(TBM) performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects.This research aims to develop six hybrid models of extreme gradient boosting(XGB) which are optimized by gray wolf optimization(GWO), particle swarm optimization(PSO), social spider optimization(SSO), sine cosine algorithm(SCA), multi verse optimization(MVO) and moth flame optimization(MFO), for estimation of the TBM penetration rate(PR).To do this, a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation, the rock mass rating, Brazilian tensile strength(BTS), rock mass weathering, the uniaxial compressive strength(UCS), revolution per minute and trust force per cutter(TFC), were set as inputs and TBM PR was selected as model output.Together with the mentioned six hybrid models, four single models i.e., artificial neural network, random forest regression, XGB and support vector regression were also built to estimate TBM PR for comparison purposes.These models were designed conducting several parametric studies on their most important parameters and then, their performance capacities were assessed through the use of root mean square error, coefficient of determination, mean absolute percentage error, and a10-index.Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of(0.1453, and 0.1325), R~2 of(0.951, and 0.951), mean absolute percentage error(4.0689, and 3.8115), and a10-index of(0.9348, and 0.9496) in training and testing phases, respectively.The developed hybrid PSO-XGB can be introduced as an accurate, powerful and applicable technique in the field of TBM performance prediction.By conducting sensitivity analysis, it was found that UCS, BTS and TFC have the deepest impacts on the TBM PR.     

Application of multivariate statistical methods to indicate the origin and geochemical behavior of potentially hazardous elements in sediment around the Sarcheshmeh copper mine,SE Iran

Multivariate statistical techniques, i.e., correlation coefficient analysis, principal components analysis (PCA), and hierarchical cluster analysis (CA), were applied to the total and water-soluble concentrations of potentially hazardous metals in sediments associated with the Sarcheshmeh mine, one of the largest Oligo-Miocene porphyry copper deposits in the world. The samples were analyzed for hazardous metal concentration levels by inductively coupled plasma mass spectrometry method. Results indicate that the contaminant metals As, Cd, Cu, Mo, S, Sb, Sn, Se, Pb, and Zn were positively correlated with the total concentrations. These hazardous metals also have strong association in the PCA and CA results. Different anthropic versus natural sources of contaminant metals were distinguished by using CA method. Water-soluble fraction of hazardous metals showed that the hydro-geochemical behavior of these metals in sediments is different considerably. Elements such as Cd, Co, Cr, Cu, Fe, Mn, Ni, S, and Zn are readily water soluble from contaminated samples, especially from evaporative mineral phases, while the release of As, Mo, Sb, and Pb into the water is limited by adsorption processes. Results obtained from the application of multivariate techniques on the water-soluble fraction data set show that the hazardous metals are categorized into three groups including (1) Ni, S, Co, Cu, Cr, and Fe; (2) Se, Mn, Cd, and Zn; and (3) Sb, As, Mo, and Sn. This classification describes the hydro-geochemical behavior of hazardous metals in water–sediment environments of the Sarcheshmeh porphyry copper mine and can be used as a basis in remedial and treatment strategies.     

Estimation of sediment load and erosion of different geological units: A case study from a basin of north-eastern Iran

Erosion of geological units and sediment load in rivers can be considered as the serious problems in recent decades.Increasing sediment loads generate major hazards for water resources development,particularly in terms of loss of reservoir storage due to sedimentation and siltation of water distribution systems.In this paper,the performance of four sediment rating curve (SRC) development methods was evaluated for the Shirin Darreh River(SDR) basin (1750 km2),located in North Khorasan Province,Iran.Data of flow discharge (Q) and suspended sediment flux (SSF)(Q-SSF pairs,N=957)and daily flow discharge,recorded by the Regional Water Company of North Khorasan (RWCNK) at the Qaleh-Barbar (QB) gauging site during 1989-2018were used.The flow discharge classification method performed best by meeting the desired criteria of most statistical indices,including normalized root mean square error (NRMSE),mean bias error (MBE),mean absolute error (MAE),index of agreement (d),and coefficient of determination (R~2).Based on the optimized method,the rate of suspended sediment transportation at the study site was estimated about2.7×10~6 ton year~(-1).Erodibility of the exposed formations in the study area was estimated based on a factorial scoring model (FSM).Three indices,focused on the outcrop and erodibility,were calculated for the geological units at sub-basin and total scales.Marl deposits are the most extensive geological unit in the three sub-basins and the maximum formation outcrop ratio (FOR) and participation in erosion (PCE)were obtained for these rocks at total scale.In fact,marl unit can be regarded as the main source to supply the suspended sediments in the study basin.     

A GIS‐based multi‐criteria seismic vulnerability assessment using the integration of granular computing rule extraction and artificial neural networks

This study proposes multi‐criteria group decision‐making to address seismic physical vulnerability assessment. Granular computing rule extraction is combined with a feed forward artificial neural network to form a classifier capable of training a neural network on the basis of the rules provided by granular computing. It provides a transparent structure despite the traditional multi‐layer neural networks. It also allows the classifier to be applied on a set of rules for each incoming pattern. Drawbacks of original granular computing (GrC) are covered, where some input patterns remained unclassified. The study was applied to classify seismic vulnerability of the statistical units of the city of Tehran, Iran. Slope, seismic intensity, height and age of the buildings were effective parameters. Experts ranked 150 randomly selected sample statistical units with respect to their degree of seismic physical vulnerability. Inconsistency of the experts' judgments was investigated using the induced ordered weighted averaging (IOWA) operator. Fifty‐five classification rules were extracted on which a neural network was based. An overall accuracy of 88%, κ = 0.85 and R² = 0.89 was achieved. A comparison with previously implemented methodologies proved the proposed method to be the most accurate solution to the seismic physical vulnerability of Tehran.     

Risk assessment of agricultural water requirement based on a multi-model ensemble framework,southwest of Iran

Water shortage and climate change are the most important issues of sustainable agricultural and water resources development. Given the importance of water availability in crop production, the present study focused on risk assessment of climate change impact on agricultural water requirement in southwest of Iran, under two emission scenarios (A2 and B1) for the future period (2025–2054). A multi-model ensemble framework based on mean observed temperature-precipitation (MOTP) method and a combined probabilistic approach Long Ashton Research Station-Weather Generator (LARS-WG) and change factor (CF) have been used for downscaling to manage the uncertainty of outputs of 14 general circulation models (GCMs). The results showed an increasing temperature in all months and irregular changes of precipitation (either increasing or decreasing) in the future period. In addition, the results of the calculated annual net water requirement for all crops affected by climate change indicated an increase between 4 and 10 %. Furthermore, an increasing process is also expected regarding to the required water demand volume. The most and the least expected increase in the water demand volume is about 13 and 5 % for A2 and B1 scenarios, respectively. Considering the results and the limited water resources in the study area, it is crucial to provide water resources planning in order to reduce the negative effects of climate change. Therefore, the adaptation scenarios with the climate change related to crop pattern and water consumption should be taken into account.

     

Standard thermodynamic data for Rhodochrosite from equilibrium decomposition curve

Hydrothermal equilibrium decomposition curve for MnCO₃⇌MnO + CO₂ in the total CO₂ pressure range of 100–1700 bars and temperature range of 500–800°C was studied. The standard thermodynamic data obtained are: ΔH⁰ _f= − 894.382 ± 0.74 kj/mol and ΔG⁰ _f = − 822.170 ± 0.74 kj/mol. These values are more negative than the reported calorimetric data.     

A Geometric Computational Model for Calculation of Longwall Face Effect on Gate Roadways

In this paper a geometric computational model (GCM) has been developed for calculating the effect of longwall face on the extension of excavation-damaged zone (EDZ) above the gate roadways (main and tail gates), considering the advance longwall mining method. In this model, the stability of gate roadways are investigated based on loading effects due to EDZ and caving zone (CZ) above the longwall face, which can extend the EDZ size. The structure of GCM depends on four important factors: (1) geomechanical properties of hanging wall, (2) dip and thickness of coal seam, (3) CZ characteristics, and (4) pillar width. The investigations demonstrated that the extension of EDZ is a function of pillar width. Considering the effect of pillar width, new mathematical relationships were presented to calculate the face influence coefficient and characteristics of extended EDZ. Furthermore, taking GCM into account, a computational algorithm for stability analysis of gate roadways was suggested. Validation was carried out through instrumentation and monitoring results of a longwall face at Parvade-2 coal mine in Tabas, Iran, demonstrating good agreement between the new model and measured results. Finally, a sensitivity analysis was carried out on the effect of pillar width, bearing capacity of support system and coal seam dip.     

Geochemical evaluation and thermal modeling of the Eocene–Oligocene Pabdeh and Middle Cretaceous Gurpi Formations in the northern part of the Dezful Embayment

Rock–Eval pyrolysis analysis, burial history, and 1D thermal maturity modeling have allowed the evaluation of the source rock potential, thermal maturation state, and impacts of the Pabdeh and Gurpi Formations in Cretaceous–Miocene petroleum system in the Naft Safid (NS) and Zeloi (ZE) oilfields, North Dezful Embayment. The total organic carbon (TOC) content of the Pabdeh and Gurpi Formations ranges from 0.2 to 4.7 wt% and 0.3 to 5.3 wt%, respectively. S2 values of the Pabdeh Formation in the ZE and NS oilfields vary from 0.41 to 13.77 and 0.29 to 14.5 mg HC (Hydrocarbon)/g rock, with an average value of 4.48 and 4.14 mg HC/g rock, respectively. These values for the Gurpi Formation in the ZE and NS oilfields range from 0.31 to 16.96 and 0.26 to 1.44 mg HC/g rock, with an average value of 8.54 and 2.43 mg HC/g rock, respectively. The S2 versus TOC diagram reveals a fair to good hydrocarbon generation potential of the Pabdeh Formation and poor to fair potential of the Gurpi Formation. The high values of S2 (S2 > S1) for samples of the both formations in the ZE and NS oilfields show that the samples are not contaminated with petroleum generated from underlying source rocks. The samples of the Pabdeh Formation in the ZE oilfield are characterized by a relatively narrow range of activation energy values with principal activation energy of 46 kcal/mol and frequency factor of 5.27 × 10⁺¹¹ s^?1. It seems that the high sulfur content of the Pabdeh organic matter probably caused the frequency factor and principal activation energy to be lower than usual. Hydrogen index (HI) values of the Pabdeh and Gurpi Formations in the ZE oilfield vary from 71 to 786 and 97 to 398 mg HC/g TOC, with an average value of 310 and 277 mg HC/g TOC, respectively. These values in the NS oilfield range from 66 to 546 and 51 to 525 mg HC/g TOC, with an average value of 256 and 227 mg HC/g TOC, respectively. Plot of HI vs. T _max value indicates that the majority of the Pabdeh and Gurpi samples contain predominantly type II kerogen and their organofacies are directly related to the more homogeneous precursor materials. Based on thermal maturity modeling results, kinetic parameters, and Rock–Eval analysis, both formations in the ZE and NS oilfields are thermally mature and immature or early mature stage, respectively.