Geological study and mining plan importance for mitigating alkali silica reaction in aggregate quarry operation

More than 80 million tonnes of construction aggregate are produced in Peninsular Malaysia. Majority of construction aggregate are produced from granite. Developing regions of Johor Bahru, Kuala Lumpur, Penang and Selangar utilize granite aggregates. Normally it is considered aggregates as non-alkali reactive. Geological study can identify various rock types, geological structures, and reactive minerals which contribute to Alkali Silica Reaction (ASR). Deformed granites formed through faulting results in reduction of quartz grain size. Microcrystalline quartz and phyllosilicates are found in granites in contact with country rocks. Secondary reactive minerals such as chalcedony and opal may be found in granite. Alkali Silica reaction is slow chemical reaction in concrete due to reactive silica minerals in aggregates, alkalis in cement and moisture. For long term durable concrete, it is essential to identify potential alkali silica reactive aggregates. Lack of identifying reactive aggregates may result spalling, cracking in concrete and ultimately ASR can result in hazard to concrete structure. This paper deals with geological study of any aggregate quarry to identify rock type and geological structures with laboratory test –petrographic analysis and bar mortar test can identify type of aggregates being produced. Mine plan with Surpac software can be developed for systematic working for aggregate quarry to meet construction aggregate demand

Introduction to the Special Issue on Soft Computing Techniques in Materials Science and Engineering

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Iterative Finite Element Analysis of Concrete-Filled Steel Tube Columns Subjected to Axial Compression

Since laboratory tests are usually costly, simulating methods using computers are always under the spotlight. This study performed a finite element analysis (FEA) using iterative solutions for simulating circular and square concrete-filled steel tube (CFST) columns infilled with high-strength concrete and reinforced with a cross-shaped plate (comprising two plates along the columns that divide the hollow columns into four equal sections) with and without opening. For this reason and for validation purposes, the columns had length of 900 mm, width/diameter of 150 mm and wall thickness of 3 mm. In this study, unlike in some other studies, the cross-shaped plate was assumed to be fixed at the top and the bottom of a column, and the columns were subjected to axial compression pointed in the center. The outcomes revealed that the cross-shaped plate could improve the axial strength of both circular and square CFST columns; however, the structural performance of the square CFST columns changed: local outward buckling was observed after inserting the cross-shaped plate. By inserting an opening on the cross-shaped plate, the bearing capacity of the circular CFST columns was further improved, while the square CFST columns experienced a decline in their ultimate bearing capacity compared with the corresponding models without the opening. The lateral deflection also improved for the circular CFST columns by adding the reinforcement. However, for the square CFST columns, while it initially improved, increasing the thickness of the cross-shaped plate inversely influenced the lateral deflection of the square CFST columns. The results were also compared with some available codes, and a good agreement was achieved with those outcomes

Data-Driven Optimized Artificial Neural Network Technique for Prediction of Flyrock Induced by Boulder Blasting

One of the most undesirable consequences induced by blasting in open-pit mines and civil activities is flyrock. Furthermore, the production of oversize boulders creates many problems for the continuation of the work and usually imposes additional costs on the project. In this way, the breakage of oversize boulders is associated with throwing small fragments particles at high speed, which can lead to serious risks to human resources and infrastructures. Hence, the accurate prediction of flyrock induced by boulder blasting is crucial to avoid possible consequences and its’ environmental side effects. This study attempts to develop an optimized artificial neural network (ANN) by particle swarm optimization (PSO) and jellyfish search algorithm (JSA) to construct the hybrid models for anticipating flyrock distance resulting in boulder blasting in a quarry mine. The PSO and JSA algorithms were used to determine the optimum values of neurons’ weight and biases connected to neurons. In this regard, a database involving 65 monitored boulders blasting for recording flyrock distance was collected that comprises six influential parameters on flyrock distance, i.e., hole depth, burden, hole angle, charge weight, stemming, and powder factor and one target parameter, i.e., flyrock distance. The ten various models of ANN, PSO–ANN, and JSA–ANN were established for estimating flyrock distance, and their results were investigated by applying three evaluation indices of coefficient of determination (R2), root mean square error (RMSE) and value accounted for (VAF). The results of the calculation of evaluation indicators revealed that R2, values of (0.957, 0.972 and 0.995) and (0.945, 0.954 and 0.989) were determined to train and test of proposed predictive models, respectively. The yielded results denoted that although ANN model is capable of anticipating flyrock distance, the hybrid PSO–ANN and JSA–ANN models can anticipate flyrock distance with more accuracy. Furthermore, the performance and accuracy level of the JSA–ANN predictive model can estimate better compared to ANN and PSO–ANN models. Therefore, the JSA–ANN model is identified as the superior predictive model in estimating flyrock distance induced from boulder blasting. In the final, a sensitivity analysis was conducted to determine the most influential parameters in flyrock distance, and the results showed that charge weight, powder factor, and hole angle have a high impact on flyrock changes

Tree-Based Solution Frameworks for Predicting Tunnel BoringMachine Performance Using RockMass andMaterial Properties

Tunnel Boring Machines (TBMs) are vital for tunnel and underground construction due to their high safety and efficiency. Accurately predicting TBM operational parameters based on the surrounding environment is crucial for planning schedules and managing costs. This study investigates the effectiveness of tree-based machine learning models, including Random Forest, Extremely Randomized Trees, Adaptive Boosting Machine, Gradient Boosting Machine, Extreme Gradient Boosting Machine (XGBoost), Light Gradient Boosting Machine, and CatBoost, in predicting the Penetration Rate (PR) of TBMs by considering rock mass and material characteristics. These techniques are able to provide a good relationship between input(s) and output parameters; hence, obtaining a high level of accuracy. To do that, a comprehensive database comprising various rock mass and material parameters, including Rock Mass Rating, Brazilian Tensile Strength, andWeathering Zone, was utilized for model development. The practical application of these models was assessed with a new dataset representing diverse rock mass and material properties. To evaluate model performance, ranking systems and Taylor diagrams were employed. CatBoost emerged as the most accurate model during training and testing, with R2 scores of 0.927 and 0.861, respectively. However, during validation, XGBoost demonstrated superior performance with an R2 of 0.713. Despite these variations, all tree-based models showed promising accuracy in predicting TBM performance, providing valuable insights for similar projects in the future

A deep dive into tunnel blasting studies between 2000 and 2023—A systematic review

Tunnel blasting is a common practice used to excavate rock formations. Many academic research articles have emerged and burgeoned in the field of tunnel blasting. These articles are dedicated to investigating objectives such as blasting vibration, rock damage, and vibration energy individually. However, no systematic analysis is conducted to consolidate and analyze the findings from the literature related to tunnel blasting. This study addresses this by offering a systematic review to explore the state of tunnel blasting research. A science mapping approach using bibliometric analysis is employed to examine 144 peer-reviewed journal articles. The review identified the most influential journals, institutions, researchers, and articles on tunnel blasting research, and it also summarizes the research hotspots of tunnel blasting according to the cluster analysis of research keywords. Findings in this review revealed the contribution of two leading journals, three leading institutions, and three leading researchers on the research of tunnel blasting. Moreover, four research keywords, i.e., blasting vibration, numerical simulation, rock damage, and overbreak, were identified as the research hotspots in 2018–2023. Finally, this review also speculated the future research directions/avenues of tunnel blasting, aiming to bring to light the deficiencies in the currently existing research and provide paths for future research

Fuzzy Cognitive Map for Evaluating Critical Factors Causing Rockbursts in Underground Construction: A Fundamental Study

The rockburst phenomenon in excavation endeavours reveals a multitude of complexities and obstacles that significantly impact both the technical and financial dimensions of project execution. Investigating critical rockburst factors in underground excavations is of considerable importance for addressing pivotal safety issues and operational complexities within the field of underground excavation projects. This research proposes an innovative approach based on an expert-based fuzzy cognitive map (FCM) framework, aiming to identify and prioritize the key critical rockburst factors prevalent in underground excavations and tunnelling. A tailored cognitive map of the parameters of problem was constructed, integrating 56 critical and critical factors meticulously curated by a team of seasoned managers, engineers, deputy managers, trainee engineers and assistant managers. The structured cognitive map was meticulously developed, considering the relative weights of the identified critical factors and their intricate interrelationships—all informed by the invaluable insights and expertise of seasoned engineers in the field. Subsequently, the cognitive map underwent a systematic solution process, whereby the causal relationships and influences amongst the identified critical factors were analysed and factored in. The outcomes of the comprehensive analysis unveiled several critical factors: lack of rockburst risk assessments, high in situ stress, presence of rock seams and weak layers, rock quality variations, and geological heterogeneity as the most paramount concerns demanding immediate attention and strategic intervention. By adopting the proposed FCM approach and leveraging the collective expertise of industry professionals, this research offers a robust and systematic framework for comprehensively assessing and addressing the key challenges associated with rockburst events in underground excavations and tunnelling projects, thereby fostering enhanced project performance and efficacy within the field

Real-time forecasting of TBM cutterhead torque and thrust force using aware-context recurrent neural networks

Tunnel Boring Machines (TBMs) are instrumental in the construction of modern tunnels, known for their operational reliability and efficiency. The real-time prediction of cutterhead loads is essential for effective project scheduling, cost management, and risk reduction. This study develops machine learning models for predicting future cutterhead torque and thrust force, simultaneously. The dataset is derived from the Yingsong water diversion project and consists of 12,962 steady-state boring cycles. Because geological conditions and setting values are available in advance, we build a geological parameter-based model and a setting value-based model for regression analysis of cutterhead torque and thrust force. In one-step forecasts, the operational parameter-based model closely captures the trend of the measured results, employing the recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU). We propose an aware-context recurrent neural network (AC-RNN) model that integrates historical operational parameters and the aware context of setting values, leading to a significant improvement in forecasting accuracy. A sensitivity analysis is carried out to quantify the relative importance of input parameters, revealing that the setting value of revolutions per minute is crucial in forecasting. The results of this study offer valuable practical insights into real-time forecasting, thereby informing engineering applications in this field

Estimation of powder factor in mine blasting: feasibility of tree-based predictive models

Drilling and blasting is a process frequently used in rock-surface and deep excavation. For a proper drilling plan, accurate prediction of the amount of explosive material is essential to reduce the environmental effects associated with blasting operations. This study introduces a series of tree-based models, namely extreme gradient boosting machine (XGBoost), gradient boosting machine (GBM), adaptive boosting machine (AdaBoost), and random forest (RF), for predicting powder factor (PF) values obtained from blasting operations. The predictive models were constructed based on geomechanical characteristics at the blasting site, blasting pattern parameters, and rock material properties. These tree-based models were designed and tuned to minimize system error or maximize accuracy in predicting PF. Subsequently, the best model from each category was evaluated using various statistical metrics. It was found that the XGBoost model outperformed the other implemented techniques and exhibited outstanding potential in establishing the relationship between PF and input variables in the training set. Among the input parameters, hole diameter received the highest significance rating for predicting the system output, while the point load index had the least impact on the PF values

Mechanical Properties of Polyamide Fiber-Reinforced Lime–Cement Concrete

Lime–cement concrete (LCC) is a type of lime-based concrete in which lime and cement are utilized as the main binding agents. This type of concrete has been extensively used to construct support layers for shallow footings and road backfills in some warm regions. So far, there has been no systematic research conducted to investigate the mechanical characteristics of polyamide fiber-reinforced LCC. To address this gap, LCC specimens were prepared with 0%, 0.5%, 1%, and 2% of polyamide fibers (a synthetic textile made of petroleum-based plastic polymers). Specimens were then cured for 3, 7, and 28 days at room and oven temperatures. Then, the effects of the fibers’ contents, curing conditions, and curing periods on the mechanical characteristics of LCC, such as secant modulus, deformability index, bulk modulus, shear modulus, stiffness ratio, strain energy, failure strain, strength ratio, and failure patterns, was investigated. The results of the unconfined compressive strength (UCS) tests showed that specimens with 1% fiber had the highest UCS values. The curing condition and curing period had significant effects on the strength of the LCC specimens, and oven-cured specimens developed higher UCS values. The aforementioned mechanical properties of the LCC specimens and the ability of the material to absorb energy significantly improved when the curing period under the oven-curing condition was increased, as well as through the application of fibers in the mix design. Based on the test results, a simple mathematical model was also established to forecast the mechanical properties of fiber-reinforced LCC. It is concluded that the use of polyamide fibers in the mix design of LCC can both improve mechanical properties and perhaps address the environmental issues associated with waste polyamide fibers