Influence of physico-mechanical properties of Indian dimension stones on cutting rate of diamond wire saw

Cutting performance of diamond wire saw is a key factor influencing mine planning, production scheduling, and equipment selection for dimension stone quarries. It is normally measured in terms of cutting rate. Rock samples collected from various granite and marble quarries in India were tested in laboratory to determine their physico-mechanical properties. Cutting rate of diamond wire saw was measured in the field studies during the actual cutting process in quarries. Using these laboratory determined properties and the cutting rate, a multiple linear regression model has been developed to predict the cutting rate of diamond wire saw. Physico-mechanical properties of rocks determined in laboratory are used as independent variables and cutting rate as predictor variable in the regression model. The study indicates that the cutting rate increases with a decrease in most of the hardness and strength parameters of rock. The final model is tested for its goodness of fit indicating a significant linear relation between cutting rate and physico-mechanical properties, namely tensile strength, slake durability index, and Cerchar hardness index with regression coefficient of 94%. The resulting model can be used suitably for different types of hard to medium hard and soft dimension stones. The generalized model for estimating the cutting rate becomes a handy tool for mining engineers to work out operating efficiency, expenses, planning etc. of the dimension stone block cutting.     

Predicting the production rate of diamond wire saw using statistical analysis

Performance prediction of diamond wire saws is important in the cost estimation and the planning of the stone quarries. An accurate estimation of sawability helps to make the planning of the rock cutting projects more efficient. In this paper, the performance prediction of diamond wire saws in cutting carbonate rocks was studied on 14 different carbonate rocks in stone quarries located in Iran. Rock samples were collected from the quarries for laboratory tests. Uniaxial compressive strength, Brazilian tensile strength, Schmidt hammer value, and Los Angeles abrasion were determined in the laboratory. Performance prediction was evaluated using simple and multiple regression analyses. Finally, a new model was proposed for predicting the production rate of diamond wire saw. It was concluded that the production rate of carbonate rock using diamond wire saw can reliably be estimated using the developed model.     

Prediction of sawing performance based on index properties of rocks

Performance analysis of all systems used in industrial production is very important subject in terms of capturing the low production cost. This subject is also important for choosing production methods in natural stone processing. Circular sawing is a commonly used method in natural stone processing. The preliminary prediction of circular sawing performance is very useful for stone producers. In this study, the prediction of sawing performance from some index properties of stones was conducted. The unit wear on diamond segments was evaluated as performance criteria. The statistical analysis was carried out, and this performance parameter was predicted from some index properties including cone indenter hardness, Shore hardness, Böhme surface abrasion, and brittleness. The most suitable prediction equation derived from regression analysis was selected. It was determined that this equation was statistically reliable for prediction of unit wear for natural stone processing.     

Predicting the Building Stone Cutting Rate Based on Rock Properties and Device Pullback Amperage in Quarries Using M5P Model Tree

One of the key parameters that affect the selection of equipment and the cost estimation of dimension stone quarries is the rock cutting rate or production rate. In this study, the M5P tree algorithm is used to determine the relationship between the hard rock sawability and its factors especially the physical and mechanical characteristics of rock. To achieve the research goal, a variety of eleven types of hard dimension stone were selected and nine major physical and mechanical characteristics of rock including uniaxial compressive strength, Young’s modulus, Brazilian tensile strength, equivalent quarts content, grain size, Mohs hardness, point load test, density and P-wave velocity of these samples were evaluated. The cutting rate of diamond wire for all of the Workpiece was measured at different pullback amperage with a fully instrumented cutting platform in laboratory. All operational parameters of cutting process were entirely controlled. Thus, a database containing 99 datasets was provided and it has been used for analyses. The obtained results from the pruned and unpruned tree models showed a significant relationship between cutting rate and its factors. In the end, the results of M5P tree method were compared with statistical analyses (i.e., linear and nonlinear regression). The coefficient of determination be equal with 0.92, 0.86, 0.77 and 0.63 for unpruned tree, pruned tree, linear and nonlinear regression method respectively. This comparison showed that the both method of M5P tree technique have a better performance in predicting the cutting rate rather than the statistical regression methods.     

Correlation of production rate of ornamental stone with rock brittleness indexes

The prediction of production rate in ornamental stones sawing is very important in cost estimation and process planning of the rock sawing plants. The main aim of this paper is finding a mathematical correlation between production rate and rock brittleness indexes. The utilized data have been collected from several stone factories in Iran. Seventeen different granite and carbonate rocks have been experienced sawing conditions with large-diameter circular saws. The laboratory tests such as uniaxial compressive strength and tensile strength, were carried out on the rock samples which were collected from these factories. The ratio of compressive strength to tensile strength (B ₁), the ratio of compressive strength minus tensile strength to compressive strength plus tensile strength (B ₂), and the area under the line of compressive strength and the line of tensile strength (B ₃) were used as brittleness indexes in this study. Then, the relationships between these brittleness indexes and production rate were investigated by using a regression analysis. The results show that, there is not a good relationship between the production rate and B ₁ and B ₂. However, the production rate correlates with B ₃ very well. Therefore, there is a reliable prediction for ornamental stones production based on B ₃ as a brittleness indexes.     

A methodology for locating the original quarries used for constructing historical buildings: application to Málaga Cathedral, Spain

It is often necessary to locate the original quarry which supplied the stone for a particular historical building. This stones could be used for future restoration work and for testing in the laboratory (artificial aging tests, physical properties determination, control of the efficacy of conservation treatments, etc.). Generally, reviewing historical documentation gives information about the geographical setting of quarries and location of the stones in the monument, but this information needs to be proved by field and laboratory studies. The comparative study of stone from quarries and monuments should basically include the following: (1) mineralogical and petrographical studies; (2) the chemical analysis of major, minor and trace elements; (3)stable isotopes determinations; (4) physical properties of quarry materials and unweathered building stone (water absorption, ultrasound transmission velocity, porosity and porous system, density, bulk density, compressive strength, etc.). This methodology was applied to Málaga Cathedral stones represented in the main façade, towers, and the western zone of the terrace, which, according to historical literature, came from Almayate (Miocene–Pliocene limestones) and Cerro Coronado (Permotriassic sandstone) in Málaga. The conclusion of the comparative study carried out on quarries and building stones was consistent with the information available from the historical documentation.     

Evaluation of Indirect Tensile Strength of Track Ballast Using Weibull Statistics

Iraq is planning to expand and rehabilitate its current railway network, thus demanding huge amounts of proper ballast material. There are three common quarries in Iraq, Sinjaar in the northern sector, Al-Qaim in western desert and Najaf-sea in the middle and southern parts of Iraq. The selection of proper track ballast material is of prime importance in design and construction of railway projects. The single particle-crushing test is a simple indirect tensile test that provides useful data for the selection of the material. Two types of railway ballast materials from (Najaf-sea and Al-Qaim) quarries are commonly used in the construction of railway in Iraq. The single particle-crushing test was performed for various ballast sizes, and the obtained data were analyzed using Weibull analysis. Najaf-sea crushed stones indicate higher tensile strength for all particle sizes compared to corresponding particle sizes of Al-Qaim crushed stone. In addition, an inverse linear relationship had obtained, between tensile strength and particle size with R ² (0.837 and 0.979) for Al-Qaim and Najaf-sea crushed stone, respectively, such relationships are important in evaluating the suitability of the materials in terms of the survival probability     

The Effects of Operational Parameters on a Mono-wire Cutting System: Efficiency in Marble Processing

Mono-wire block cutting machines that cut with a diamond wire can be used for squaring natural stone blocks and the slab-cutting process. The efficient use of these machines reduces operating costs by ensuring less diamond wire wear and longer wire life at high speeds. The high investment costs of these machines will lead to their efficient use and reduce production costs by increasing plant efficiency. Therefore, there is a need to investigate the cutting performance parameters of mono-wire cutting machines in terms of rock properties and operating parameters. This study aims to investigate the effects of the wire rotational speed (peripheral speed) and wire descending speed (cutting speed), which are the operating parameters of a mono-wire cutting machine, on unit wear and unit energy, which are the performance parameters in mono-wire cutting. By using the obtained results, cuttability charts for each natural stone were created on the basis of unit wear and unit energy values, cutting optimizations were performed, and the relationships between some physical and mechanical properties of rocks and the optimum cutting parameters obtained as a result of the optimization were investigated.     

金刚石工具在钢筋混凝土结构修复改造中的应用与研究现状

我国正处于钢筋混凝土结构应用与发展的大规模新建与修复改造并重阶段。为对钢混结构进行局部或整体的拆除与修复改造,传统机械破碎与爆破拆除方式存在施工效率低、安全隐患大和环境污染严重等问题,而利用金刚石工具对其进行静力切割拆除的施工方法在近年来得到迅速发展。目前,在钢混结构拆除与修复改造施工中应用最多的金刚石工具为金刚石薄壁钻头、金刚石锯片和金刚石绳锯。本文对上述3类金刚石工具在钢混结构修复改造施工中的应用现状、钻进与锯切机理、制造技术和施工工艺等研究现状进行了总结与分析,在此基础上指出了其可能的研究趋势与发展方向。研究成果可为金刚石工具在工民建和部分特殊领域的广泛应用提供技术参考。     

Prediction of rainfall using artificial neural networks for synoptic station of Mashhad: a case study

In this paper, we have utilized ANN (artificial neural network) modeling for the prediction of monthly rainfall in Mashhad synoptic station which is located in Iran. To achieve this black-box model, we have used monthly rainfall data from 1953 to 2003 for this synoptic station. First, the Hurst rescaled range statistical (R/S) analysis is used to evaluate the predictability of the collected data. Then, to extract the rainfall dynamic of this station using ANN modeling, a three-layer feed-forward perceptron network with back propagation algorithm is utilized. Using this ANN structure as a black-box model, we have realized the complex dynamics of rainfall through the past information of the system. The approach employs the gradient decent algorithm to train the network. Trying different parameters, two structures, M₅₃₁ and M₇₄₁, have been selected which give the best estimation performance. The performance statistical analysis of the obtained models shows with the best tuning of the developed monthly prediction model the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) are 0.93, 0.99, and 6.02 mm, respectively, which confirms the effectiveness of the developed models.     

Black dimensional stones: geology, technical properties and deposit characterization of the dolerites from Uruguay

Dimensional stones with a black color occupy a prominent place on the international market. Uruguayan dolerite dikes of andesitic and andesitic–basaltic composition are mined for commercial blocks of black dimensional stones. A total of 16 dikes of both compositions were studied and samples collected for geochemical and petrographical analysis. Color measurements were performed on different black dimensional stones in order to compare them with the Uruguayan dolerites. Samples of the two commercial varieties (Absolute Black and Moderate Black) were obtained for petrophysical analysis (e.g. density, porosity, uniaxial compressive strength, tensile strength, etc.). Detailed structural analyses were performed in several quarries. Geochemistry and petrography determines the intensity of the black color. The Uruguayan dolerite Absolute Black is the darkest black dimensional stone analyzed in this study. The petrophysical properties of Uruguayan dolerites make them one of the highest quality black dimensional stones. Structural analyses show that five joint sets have been recognized: two sub-vertical joints, one horizontal and two diagonal. These joint sets are one of the most important factors that control the deposits, since they control the block size distribution and the amount of waste material.     

Geological modeling and short-term production planning of dimension stone quarries based on market demand

In order to obtain minimum amount of ore wastage and maximum profitability in dimension stone quarries an optimal short-term production planning procedure has been established here according to the market demand. As the base of this procedure geological modelling has been first created and smoothing of ore-body boundaries was done. Then, an economical block model has been provided. In the next step, all probable shapes of minable-blocks were specified and their priorities in assignment were done according to desired size of the blocks by market demand. Finally, searching from a base sub-block was started in order to find the optimal plan. The procedure has been used for a dimension marble quarry located in northwest of Iran. It demonstrates a decrease about 26% of ore wastage and 21.3% of diamond wire assumption with comparing to the existent traditional procedure.     

Estimation of effective stress parameter of unsaturated soils by using artificial neural networks

Great efforts are required for determination of the effective stress parameter χ, applying the unsaturated testing procedure, since unsaturated soils that have the three‐phase system exhibit complex mechanical behavior. Therefore, it seems more reasonable to use the empirical methods for estimation of χ. The objective of this study is to investigate the practicability of using artificial neural networks (ANNs) to model the complex relationship between basic soil parameters, matric suction and the parameter χ. Five ANN models with different input parameters were developed. Feed‐forward back propagation was applied in the analyses as a learning algorithm. The data collected from the available literature were used for training and testing the ANN models. Furthermore, unsaturated triaxial tests were carried out under drained condition on compacted specimens. ANN models were validated by a part of data sets collected from the literature and data obtained from the current study, which were not included in the training phase. The analyses showed that the results obtained from ANN models are in satisfactory agreement with the experimental results and ANNs can be used as reliable tool for prediction of χ. Copyright © 2007 John Wiley & Sons, Ltd.     

Application of neural networks to predict net present value in mining projects

Net present value (NPV) is the most popular economic indicator in evaluation of the investment projects. For the mining projects, this criterion is calculated under uncertainty associated with the relevant parameters of say commodity price, discount rate, etc. Accurate prediction of the NPV is a quite difficult process. This paper mainly deals with the development of a new model to predict NPV using artificial neural network (ANN) in the Zarshuran gold mine, Iran. Gold price (as the main product), silver price (as the byproduct), and discount rate were considered as input parameters for the ANN model. To reach an optimum architecture, different types of networks were examined on the basis of a trial and error mechanism. A neural network with architecture 3-15-10-1 and root mean square error of 0.092 is found to be optimum. Prediction capability of the proposed model was examined through computing determination coefficient (R ²?=?0.987) between predicted and real NPVs. Absolute error of US$0.1 million and relative error of 1.4 % also confirmed powerfulness of the developed ANN model. According to sensitivity analysis, it was observed that the gold price is the most effective and discount rate is the least effective parameter on the NPV.     

Testing of Sydney dimension sandstone for use in the conservation of heritage buildings

This paper deals with the scientific assessment of the physical properties of sandstone used in the conservation of Sydney's historic heritage sandstone buildings. The local Hawkesbury Sandstone has been widely used for all manner of constructions since the early days of the first settlement. In the nineteenth century, dimension sandstone quarries existed all over the Sydney region, and the demand for the stone was great. During this time, a true ‘freestone’ known as ‘yellow block’ sandstone, in which bedding planes were absent, was quarried extensively. This sandstone is also known as a ‘self-colouring’ sandstone, where the rock, although grey when quarried, turns a yellow–brown after a few months exposure to the atmosphere. It can be easily carved and was eminently suitable for the ornate Victorian architecture of the time. There are very few quarries within the Sydney region today capable of producing ‘yellow block’ sandstone. Whenever possible, sandstone from suitable building excavations, particularly on the Pyrmont Peninsula, is used for this work. Conservation work is extremely expensive and the building elements that most need replacement, the overhanging and decorative elements, are usually those that cost the most. It is essential that the most durable replacement stone be selected. Thus, a strict regime of scientific testing is used for any major conservation project, in order to determine the physical properties of possible replacement stone. This is the province of the geologist and involves visual observation in the quarry or excavation, which is a simple, cost-effective means of weeding out poor quality stones, followed by laboratory engineering tests to establish the physical parameters of a sandstone and finally, petrographic analysis. The results of such tests, combined with careful sampling, ensure the best possible quality of replacement material.     

Estimating compaction parameters of fine- and coarse-grained soils by means of artificial neural networks

This paper presents artificial neural network (ANN) prediction models for estimating the compaction parameters of both coarse- and fine-grained soils. A total number of 200 soil mixtures were prepared and compacted at standard Proctor energy. The compaction parameters were predicted by means of ANN models using different input data sets. The ANN prediction models were developed to find out which of the index properties correlate well with compaction parameters. In this respect, the transition fine content ratio (TFR) was defined as a new input parameter in addition to traditional soil index parameters (i.e. liquid limit, plastic limit, passing No. 4 sieve and passing No. 200 sieve). Highly nonlinear nature of the compaction data dictated development of two separate ANN models for maximum dry unit weight (γ_dmax) and optimum water content (ω_opt). It was found that generalization capability and prediction accuracy of ANN models could be further enhanced by sub-clustered data division techniques.     

Simultaneous parameter identification of a heterogeneous aquifer system using artificial neural networks

An artificial neural network (ANN) model is proposed for the simultaneous determination of transmissivity and storativity distributions of a heterogeneous aquifer system. ANNs may be useful tools for parameter identification problems due to their ability to solve complex nonlinear problems. As an extension of previous study—Karahan H, Ayvaz MT (2006) Forecasting aquifer parameters using artificial neural networks, J Porous Media 9(5):429–444—the performance of the proposed ANN model is tested on a two-dimensional hypothetical aquifer system for transient flow conditions. In the proposed ANN model, Cartesian coordinates of observation wells, associated piezometric heads and observation time are used as inputs while corresponding transmissivity and storativity values are used as outputs. The training, validation and testing processes of the ANN model are performed under two scenarios. In scenario 1, all the sampled data are used through the simulation time. However, in the scenario 2, there are data gaps due to irregular observations. By using the determined synaptic network weights, transmissivity and storativity distributions are predicted. In addition, the performance of the proposed ANN is tested for different noise data conditions. Results showed that the developed ANN model may be used in simultaneous aquifer parameter estimation problems.     

The provenance,petrology and sedimentology of building stone in Bromyard,Herefordshire, UK

A multi-disciplinary study was undertaken of the Lower Old Red Sandstone building stones of Bromyard, Bromyard Downs and Bringsty Common, Herefordshire, UK, to trace their original quarry sources. 196 stone structures were recorded in detail and 3 broad lithological groups recognised. Combining old maps, archival sources, Light Detection and Ranging (LiDAR) data and testimony of local residents, the historic quarries in the area have been mapped. Field studies of the geology of quarries and outcrops have established the area of origin of two distinctive coarse grained lithologies. Intraformational conglomerates have been demonstrated, with some confidence, to be derived from Bromyard itself. Coarse, pebbly, quartzose sandstones have been traced to Bringsty Common and shown to be used only in the church in Bromyard before the 20th century. The origins of fine grained sandstone are found to be more complex. Through a combination of fieldwork and archival research, Clater Park Quarry is indicated as the origin of high quality green sandstones in Bromyard. 15 samples of sandstone from the Bromyard Downs, have been petrographically analysed by point counting to determine their clastic and mineralogical proportions. Early results of this analysis are presented which show modal variation in lithics, feldspars and cements that may serve to link quarries to buildings. Finally, it is suggested that at least some of the diversity of stone present in Bromyard is the result of slight variation in depositional processes in the immediately underlying bedrock.     

2D probabilistic prediction of sparsely measured earth properties constrained by geophysical imaging fully accounting for tomographic reconstruction ambiguity

Many hydrological, environmental, or engineering exploration tasks require predicting spatially continuous scenarios of sparsely measured borehole logging data. We present a methodology to probabilistically predict such scenarios constrained by ill-posed geophysical tomography. Our approach allows for transducing tomographic reconstruction ambiguity into the probabilistic prediction of spatially continuous target parameter scenarios. It is even applicable to data sets where petrophysical relations in the survey area are non-unique, i.e., different facies related petrophysical relations may be present. We employ static two-layer artificial neural networks (ANNs) for prediction and additionally evaluate, whether the training performance of the ANNs can be used to rank geophysical tomograms, which are mathematically equal reconstructions of physical parameter distributions in the ground. We illustrate our methodology using a realistic synthetic database for maximal control about the prediction performance and ranking potential of the approach. For doing so, we try to link geophysical radar and seismic tomography as input parameters to porosity of the ground as target parameter of ANN. However, the approach is flexible and can cope with any combination of geophysical tomograms and hydrologic, environmental or engineering target parameters. Ranking of equivalent geophysical tomograms based on additional borehole logging data is found to be generally possible, but risks remain that the ranking based on the ANN training performance does not fully coincide with the closeness of geophysical tomograms to ground truth. Since geophysical field data sets do usually not offer control options similar to those used in our synthetic database, we do not recommend the utilization of recurrent ANNs to learn weights for the individual geophysical tomograms used in the prediction procedure.     

Estimation of compaction parameters of fine‐grained soils in terms of compaction energy using artificial neural networks

The determination of the compaction parameters such as optimum water content (w_opt) and maximum dry unit weight (γ_dmax) requires great efforts by applying the compaction testing procedure which is also time consuming and needs significant amount of work. Therefore, it seems more reasonable to use the indirect methods for estimating the compaction parameters. In recent years, the artificial neural network (ANN) modelling has gained an increasing interest and is also acquiring more popularity in geotechnical engineering applications. This study deals with the estimation of the compaction parameters for fine‐grained soils based on compaction energy using ANN with the feed‐forward back‐propagation algorithm. In this study, the data including the results of the consistency tests, standard and modified Proctor tests, are collected from the literature and used in the analyses. The optimum structure of a network is determined for each ANN models. The analyses showed that the ANN models give quite reliable estimations in comparison with regression methods, thus they can be used as a reliable tool for the prediction of w_opt and γ_dmax. Copyright © 2010 John Wiley & Sons, Ltd.