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Predictive Assessment of Postural Risk and Biomechanical Analysis of Musculoskeletal Disorder Related Problems of Dump Truck Operators in Indian Surface Metal Mines
(National Institute of Technology Karnataka, Surathkal., 2024) Kar, Mohith Bekal; M., Aruna; B.M., Kunar
This study aimed to determine the postural risk among dumper operators working in Indian surface metal mines. An epidemiological study was conducted to determine the association between driving posture and Work-Related Musculoskeletal Disorders (WRMSDs). A customized self-reported questionnaire was developed to collect personal, habitual, and work related data from the selected sample. The raw data was pre-processing and analysed using Machine Learning (ML) models, such as Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Gradient Boosting Machine (GBM), and Logistic Regression (LR). The performance of these models was evaluated using metrics, such as accuracy, precision, recall, F1 score, and Receiver Operating Characteristic (ROC) curve. The findings of the performance study indicated that the RF model offers better results over SVM, DT, GBM, and LR models with an accuracy of 0.71, precision of 0.75, recall score of 0.78, and F1 score of 0.76. Furthermore, the study revealed that age of the dumper operators had a significant association with WRMSDs, followed by awkward driving posture, work experience, job demand, alcohol consumption, smoking, work design, and marital status. In overall, the epidemiology study proved that the role of awkward driving posture contributes to the WRMSDs among dumper operators. Consequently, a thorough analysis of sitting posture of dumper operators was conducted using the observation method (i.e. fuzzy RULA method). The findings showed that over 80% of dumper operators exhibited a fuzzy RULA score corresponding to 'action level two', indicating the necessity for further investigation. To investigate deeper, a study of operator’s sitting posture with respect to various job cycles (i.e. loading, hauling with load, unloading, and empty travel) was undertaken. The detailed analysis revealed relatively consistent fuzzy RULA scores ranging from 3.5 to 4.25 during dynamic operations. Conversely, during static operations, the fuzzy RULA scores fluctuated more widely, ranging from 3.25 to 4.5. This reveals that operators maintained nearly identical postures during dynamic operations, whereas their sitting postures varied more during static operations. The Fuzzy RULA method do not consider operator’s height and weight, which is important factors contributing to WRMSDs. Therefore, a comprehensive biomechanical analysis of dumper operators was conducted using the "opensim" software package and Gait2354 human model. In this study the load on the spine, muscles, and tendons during primary climb, main v haul, right incline traverse, left incline traverse, and final climb tasks was determined. The outcome of the study showed that the load on the spinal was varying with the job cycle, with maximum load occurring during main haul (335.74N), followed by primary climb (324.30N), final climb (324.30N), right incline traverse (314.43N), and left incline traverse (304.29N). The biomechanical analysis also indicated that the muscle and tendon forces vary with job cycle. During right incline traverse, the right ERCSPN, right EXTOBL, and right INTOBL muscles experienced relatively high total forces (i.e. 41.76N, 59.99N, and 39.21N, respectively). Similarly, during left incline traverse, the left ERCSPN, left EXTOBL, and left INTOBL muscles experienced high total forces (i.e. 47.34N, 70.05N, and 51.33N, respectively). The tendon which joins the muscles with the bone also showed the same trend. The tendons attached to the right ERCSPN, right EXTOBL, and right INTOBL muscles experienced high total force of 41.76N, 59.99N, and 39.21N, respectively during right inclined transverse. Similarly, the tendons attached to the left ERCSPN, left EXTOBL, and left INTOBL muscles experienced high total force of respectively 47.34N, 70.05N, and 51.33N when the dumper operator were performing left inclined transverse. In general, this study showed that muscles suffers significant tensile forces when operators perform right and left inclined transverse movements. During the field study, it was observed that the operators were not wearing seat belts while operating dumpers. Because of this when navigating corners, operators encountered centripetal forces, prompting them to lean their bodies and consequently shifting the center of gravity (COG) from the center to the side. This change in COG led to tensile forces acting on the muscles and tendons connected to the spine. Hence, this study recommends for the mandatory use of seat belts by operators while operating. Similarly, this study also disclosed that the spine undergoes significant compressive forces during the main hauling operation (i.e. the movement of the dumper between loading and unloading points). The compressive load on the spine increases with increase in Body Mass Index (BMI) of the operators. However, considering operators with lower BMIs may not be feasible due to potential recruitment bias. Hence, this study suggests to incorporate regular breaks for operators during work to mitigate ill effects on their health.
Utilization of Gold Ore Tailings as a Partial Replacement to the Fine Aggregates in the Production of Geopolymer Concrete with Recycled Coarse Aggregates
(National Institute of Technology Karnataka, Surathkal., 2024) L., Eshwarayya B; Mangalpady, Aruna; Reddy, Sandi Kumar
The mining industry generates a large amount of waste, particularly in the form of tailing dumps, which creates major environmental difficulties, such as air pollution, water pollution, soil erosion, acid mine drainage, and so on. Earlier studies confirmed that the mine waste could be used in making building materials, such as bricks, tiles, concrete blocks, pavement blocks, precast concrete elements etc. The Gold Ore Tailings (GOTs) are one of the waste materials in the mining industry. The disposal of these tailings could be the problem to human health and major environmental concern from several years. Hence, the attempt should be made for effective utilization of these mine waste in different forms. In this study, the GOTs were utilized as an alternative material to the River Sand (RS) in the production of Geopolymer Concrete (GPC). In total, 11 mix proportions of GPC cubes, beams and cylinders were prepared by partially replacing the class F Fly Ash (FA) with Ground Granulated Blast Furnace Slag (GGBFS) as binder in steps of 10% up to 100%, along with GOTs (as a partial substitute to the river sand in steps of 5% up to 30%) and Recycled Coarse Aggregates (RCAs). These mix proportions were named as Mix Proportion-I, Mix Proportion-II, Mix Proportion-III, Mix Proportion-IV, Mix Proportion-V, Mix Proportion-VI, Mix Proportion-VII, Mix Proportion-VIII, Mix Proportion-IX, Mix Proportion-X and Mix Proportion-XI respectively. In addition to the above said 11 mix proportions, one more set of GPC cubes, cylinders and beams were prepared using FA, GOTs and Natural Coarse Aggregates (NCAs), which is designated as Mix Proportion-XII. Furthermore, Conventional Concrete (CC) of M25 (CC1) and M40 (CC2) grades were created using a mixture of Ordinary Portland Cement (OPC) of 43 grade, RS, and NCAs with a water/cement (W/C) ratio of 0.45 and 0.4, respectively. Among 12 types of mix proportions, GPC sample GOT-12-0 of Mix Proportion-XII (i.e. FA-100%, GOT-0%, NCAs-100%) showed a maximum slump value of 89.3 mm, whereas GPC sample GOT-1-0 of Mix Proportion-I (i.e., FA-100%, GOT-0%, RCA 100%) exhibited the maximum slump of 65.3 mm. Further, Conventional Concrete (CC) of M25 (CC1) and M40 (CC2) grades were showed the slump values of 110 mm and 58.3 mm, respectively. vii The GPC samples were cast and cured at room temperature until the curing ages and after that the compressive strength, split tensile strength and flexural strength of samples were determined. The laboratory tests demonstrated a maximum compressive strength of 52.25 MPa, split tensile strength of 5.99 MPa and flexural strength of 7.98 MPa for sample GOT-11-15 (11 indicates Mix Proportion-XI and 15 indicates 15% of GOTs) of Mix Proportion-XI. For Mix Proportion-XII, the highest compressive strength of 43.71 MPa, split tensile strength of 4.17 MPa and flexural strength of 6.13 MPa was achieved for sample GOT-12-15 (12 indicates Mix Proportion-XII and 15 indicates 15% of GOTs). Further, the CC2 samples exhibited the maximum compressive strength of 47.4 MPa, split tensile strength of 4.4 MPa, and flexural strength of 4.89 MPa for 28 days of curing. Based on the test results, the sample GOT-8-15 (i.e., FA-30%, GGBFS-70%, GOTs 15%, and RCAs-100%) of Mix Proportion-VIII was considered as the best mixture among all the mix proportions, with a slump value of 35.1 mm, compressive strength of 47.8 MPa, split tensile strength of 5.01 MPa, and flexural strength of 6.98 MPa when compared to CC2 sample (i.e., standard mix of same composition). The developed GPC samples were tested to know their durability properties, such as resistance to sulphates and chlorides. The sulphate attack test was conducted by immersing the CC and GPC samples in 5% magnesium sulphate (MgSO4) solution for a period of 28 days, 56 days, 90 days, 180 days, 270 days, and 365 days. In this test, the GPC samples showed the reduction in compressive strength, which is slightly more when compared to CC samples, for 270 and 365 days of exposure condition. The Rapid Chloride Penetration Test (RCPT) was also conducted to know the chloride ion penetration in which GPC sample (GOT-8-15) exhibited less chloride penetration when compared to CC1 and CC2 samples. Further, the Toxic Characteristic Leaching Procedure (TCLP) analysis showed that the GOTs has very high concentration of hazardous metals, such as arsenic (As), zinc (Zn), iron (Fe), and mercury (Hg). But the concentration of cyanide (CN-) was minimum in GOTs. In this regard, geopolymerization would be a better method for immobilizing the hazardous metals present in GOTs. viii The mineralogical and chemical composition of raw materials (i.e., GOTs and FA) was analyzed using X-Ray Diffraction (XRD) and X-Ray Fluorescence (XRF), respectively. The XRD analysis revealed that the quartz has highest peak intensity of 55% in GOTs and 50% of corundum in FA. The XRF analysis exhibited that GOTs and FA have high silicon oxides up to 39% and 38% respectively, and hence these materials can be effectively utilized in the manufacture of GPC. The crushed GPC samples were analyzed using Field Emission Scanning Electron Microscopy (FESEM) to observe the morphological changes. The FESEM analysis indicated that Si and Al are the two main constituents in GOTs and FA. This analysis also revealed the existence of uneven forms of quartz particles in GOTs, as well as the spherical shapes of FA particles adhering in the RCAs. The GPC sample comprised 15% GOTs exhibited denser and compacted microstructures. The multiple regression analysis illustrated R2 value of 69.9%, 70.9%, and 68.0%, respectively for compressive strength for 3, 7, and 28 days curing period. Similarly, the R2 value for split tensile strength and flexural strength was 89.0% and 85.5%, respectively, for 28 days curing period. The P-value for the developed model was less than 0.05 and hence the developed model was considered as significant and best-fit model. Finally, the cost analysis was done to know the economic feasibility of raw materials. It was found that the cost of GPC was more than that of CC2 about 38.20%.
Development of Iot Enabled Lorawan Based Real Time Early Warning Monitoring System for Underground Mine Environmental Parameters
(National Institute of Technology Karnataka, Surathkal., 2024) Naik, Anil S; Reddy, Sandi Kumar; Mandela, Govinda Raj
In underground mines, real time monitoring of environmental parameters is crucial for detecting hazardous scenarios during mining operations. This research study explores wireless communication technology and the Internet of Things (IoT) to enhance safety and prevent underground mining accidents, benefitting workers and organizations. Gas parameters like oxygen(O2), Carbon monoxide (CO), Carbon dioxide (CO2), Methane (CH4), Hydrogen sulfide (H2S), Nitrous oxide (NO), Nitrogen dioxide (NO2), Sulfur dioxide (SO2), and Ethylene oxide (EO) and environmental factors like temperature and humidity are monitored using portable multi-gas detectors certified by DGMS and a hygrometer once per shift. A hardware prototype employing IoT-enabled Sx1278 Ra-02 LoRa 433 MHz and ZigBee modules enables wireless communication from underground mine tunnels to the surface. This system was successfully tested in two Indian underground gold mines. Additionally, an IoT-enabled real-time monitoring system using HPD13A LoRa 868 MHz modules integrates CO, CO2, CH4, H2S, H2, temperature, and humidity sensors. Data is stored locally and uploaded to the cloud via LoRa receivers, providing a reliable, power-efficient solution for continuous real time monitoring in underground mines. However, the developed hardware prototype communication range and sensor power consumption limit are deployed in underground mines, especially in harsh environmental conditions. To address these challenges, an IoT-enabled LoRaWAN Gateway based system is proposed. This system integrates industrial RS485 sensors, RS485-LN converter, and LoRaWAN Gateway to monitor environmental parameters from the surface continuously. The system promptly generates an email alert notification on the surface to the concerned authority and initiates an audible alarm alert sound in underground mine tunnels and at the surface when the specified parameters exceed the predetermined thresholds. The developed LoRaWAN system was tested in an underground gold mine 832 meters below the surface, demonstrating effective wireless communication over distances up to 1000 meters. The system facilitates the transmission of vi environmental parameters data of approximately 1800 meters from an underground mine of a specific location to the surface. Real-time data displayed in a surface control room dashboard offers immediate insights into underground mine environment conditions, complementing traditional multi-gas detectors' measurements. The environmental parameters measured by the IoT-enabled LoRaWAN system are compared with those of DGMS-approved multi-gas detection devices. The measurement accuracy for gases like CO2 and NO was recorded at 86.95% and 88.57%, respectively. CO levels spiked during blasting activities. The H2S, CH4, and H2 concentrations were not detected in underground mine tunnels, while N2 concentration was noted at 77.8%. Temperature and humidity readings from the IoT-enabled LoRaWAN system ranged between 28°C to 33°C and 55% to 61%, respectively. In contrast, a portable recorder device reported temperature variations from 31°C to 33.5°C and humidity levels from 58.9% to 61.5%. Environmental data gathered through an IoT-enabled LoRaWAN system is processed using the LSTM and XGBoost machine learning algorithm to predict environmental conditions accurately. The standard validation metric RMSE validates the accuracy of these predictions. Furthermore, the system's design is robust, with intrinsic safety features, flameproof construction, and an IP65-rated panel, making it exceptionally suitable and secure for hazardous underground mine environments. The system design includes inherent safety features and IP65-rated panels for robustness in hazardous environments. In conclusion, this research emphasizes the need for standardized strategies to manage and mitigate hazardous gases in underground mines, particularly from diesel vehicles. Implementation of the IoT-enabled LoRaWAN system proves cost effective and efficient for continuous monitoring, ensuring safety and productivity in underground mining operations.
Investigations on the Role of Green Synthesized Iron Nanoparticles in the Fenton’s Oxidation of Triclosan in Wastewater
(National Institute of Technology Karnataka, Surathkal, 2024) K N, Rashmishree; Shrihari, S.; Thalla, Arun Kumar
Design of Operational Strategies for Public Bus Transit System Considering Variations in Passenger Mobility Pattern
(National Institute of Technology Karnataka, Surathkal, 2024) K S, Nithin; Mulangi, Raviraj H
In developing countries, the migration of people to urban areas has led to an increase in the urban population. Growth in urban population directly influences the vehicle population, leading to traffic congestion, pollution and accidents. To overcome this, people should be made to choose public transit over private vehicles by making it more efficient and attractive. The transit system can be made efficient and attractive when the travel time of passengers is reduced. This can be achieved by the implementation of operational strategies including limited-stop services, short-turning, dead-heading and so on. However, before the implementation of these strategies, variability in passenger mobility patterns should be assessed to identify which strategy is best suited for a given corridor. Thus, the present study aimed to design operational strategies for public bus transit system (PBTS) considering variability in passenger mobility patterns. Initially, seasonal variations in passenger mobility patterns were analysed to evaluate the impact of weather on passenger behaviour. Based on the factors influencing passenger flow, passenger demand forecasting models were developed at route and stop levels. Then, the operational strategy was designed for PBTS by integrating limitedstop service with the existing all-stop service. Further, a regression model was developed based on variability in passenger mobility patterns to evaluate the feasibility of limited-stop service before implementing it in any corridor. Based on the outcomes obtained from the analysis, recommendations were given to the transport planners to improve the efficiency of the PBTS. The research work was carried out by considering PBTS from two cities i.e., Davanagere and Udupi of Karnataka state, India. The PBTS of both cities is operated by Karnataka State Road Transport Corporation (KSRTC). The Electronic Ticketing Machine (ETM) is used by the systems to collect ticket data. The ETM data of both cities was procured from KSRTC to obtain passenger flow information. Two cities were considered because of their different weather conditions, making it possible to compare the influence of weather on the variability in passenger mobility patterns. To analyse the impact of different intensities of weather on passenger flow, the weather data of both cities was collected from the Indian Meteorological Department (IMD). Further, land use data was also procured from urban development authorities to evaluate variations in passenger mobility patterns at different spatial regions. The impact of weather on passenger mobility patterns was analysed at different temporal and spatial scales. The change in passenger flow with a change in the intensity of weather was computed to evaluate the impact of different intensities of weather on passenger mobility. The analysis was carried out at system, stop and route levels to have microscopic insights into the relationship between weather and passenger flow. Time-series analysis was carried out at the system and stop levels to evaluate the hourly variations in passenger flow at different intensities of weather. At the system level, the were categorised based on land use and aggregated to evaluate the impact of weather on different spatial regions with different land use. Further, at the route level, the variation in passenger flow between the stops of a route was analysed based on the coefficient of variation (CV). The outcome from the analysis indicated that, at high rainfall, the passenger flow increased in Davanagere and decreased in Udupi. At moderate rainfall, only during weekend variation in passenger flow was observed. The village stops of Davanagere experienced a significant increase in passenger demand during the rainy season. Further, high CV was observed at the routes of Davanagere and Udupi under different weather conditions indicating the concentration of demand among a few stops of a route. These results proved that there is a weather-related impact on passenger flow, but it is region-specific. It was not possible to identify a specific pattern in the relationship between passenger flow and weather. Passenger demand forecasting models were developed with the incorporation of factors influencing passenger flow. The models were developed at the route level and stop level using long short-term memory (LSTM) and graph convolutional neural network (GCN), respectively. Based on the analysis of variability in passenger mobility patterns it was evident that passenger flow is influenced by weather attributes at different temporal and spatial scales. As a result, the LSTM model was developed with the incorporation of weather and temporal attributes and the GCN model was developed with the incorporation of land use as a spatial attribute. In the LSTM model, temporal features such as recent time intervals (R), daily periodicity (P) and weekly trend (T) were included along with the weather attributes (W) and named RPTW-LSTM. Recent time intervals are the most recent steps in time-series data that account for short-term fluctuations in the data. Periodicity refers to patterns that repeat at regular intervals in time-series data. Daily periodicity specifically involves daily cyclic repetitions at the same hour. The weekly trend is encompassed in the model to account for long-term changes observed in the passenger flow data. The output (predictions) of hourly variation, daily periodicity, weekly trend and weather models are fused in the final model. The fused model uses LSTM for multivariate time series analysis, accounting for multiple factors that change over time. In the GCN model, the bus network is constructed as a graph with bus stops were considered as nodes and link between two nodes specifying the passenger flow between the stops. For each node, the land use around the bus stops within a service range of 500m was extracted and included as a node feature to capture spatial correlations in passenger flow. The developed models were compared with the baseline models to evaluate the impact of different features. The developed model showed better accuracy than the baseline models. This indicates that the performance of the model can be improved by incorporating the factors that influence passenger flow. Thus, the development of the prediction models assists transport planners in identifying the possible passenger flow variations in future, based on which transit schedules and frequencies can be designed. The operational strategies were designed for the PBTS by integrating limitedstop service with the existing all-stop service. The frequency of the existing all-stop service was shifted to the limited-stop service to avoid expansion of the fleet size, thereby avoiding additional investments. An optimisation model was developed to optimise stopping strategy and frequency of limited-stop service by maximising the total cost savings (TCS). The optimisation model included maximisation of the operator cost and in-vehicle travel time of passengers and minimisation of waiting time for passengers not served by limited-stop service. The limited-stop service was designed for PBTS of both cities. Genetic Algorithm (GA) was used to optimise the stopping pattern and frequency of limited-stop service by maximising the TCS. During each iteration, a single-point crossover and bit-flip mutation were applied to the population to introduce genetic diversity. To maintain the quality of the solutions, two elite individuals with the best fitness values were preserved in each generation. From the results, it was observed that the implementation of limited-stop service resulted in significant savings for both PBTS. The model strategically included important stops and skipped stops with low demand. Further, it was observed that the savings and stopping patterns were dependent on the number of stops and demand variability, respectively. As a result, to further identify the impact of different characteristics of corridors on the performance of the limited-stop service, a feasibility analysis was carried out. The feasibility analysis was carried out to identify when and where limited-stop service can be implemented. A regression model was developed to evaluate the influence of different characteristics of corridors on the savings incurred due to the implementation of limited-stop services. Based on the factors identified, the adaptability of limited-stop service can be verified before implementing it in any corridor. The scenarios were generated based on variability in passenger mobility patterns at different weather and temporal conditions. The limited-stop service was optimised for each of these scenarios and TCS was determined. Then, cost percentage savings (CPS), achieved due to the implementation of limited-stop service compared to total cost (TC) from all-stop service, was computed. Employing CPS as the dependent variable and various characteristics of corridors as independent variables, a regression model was calibrated. The model was found to have a better fit with the variables considered. The features like CV, dwell time, operator cost and number of stops had a significant positive impact on the model and wait time cost had a negative impact on the model. The variable CV with a positive impact indicates that the limited-stop service could be feasible when variability in passenger mobility patterns is high and it was observed that variability was high in different weather and temporal conditions. Similarly, limited-stop service could be feasible in the corridors where operator cost, dwell time and number of stops are high. However, care must be taken to reduce the waiting time of passengers whose stops are not served. Thus, the developed methodology can be adopted by transport planners to assess the variability in passenger mobility patterns and based on the observed variations different operational strategies can be designed to make the PBTS efficient and attractive.