Danial Jahed Armaghani

University of Technology Sydney | UTS

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...

Many artificial intelligence-based predictive techniques have been developed for assessing elastic modulus (E) of rocks using results of simple rock index tests. However, most of them are related to artificial neural networks (ANNs). On the other hand, developing a feasible and easy-to-use model is still of interest more specifically when the datas...

In production blasting, the primary goal is to produce an appropriate fragmentation, whereas an improper fragmentation is one of the most common side effects induced by these events. This investigation aims at predicting rock fragmentation through a new ansemble technique, namely light gradient-boosting machine (LightGBM) with its hyper-parameters...

Ground vibration due to blasting is identified as a challenging issue in mining and civil activities. Peak particle velocity (PPV) is one of the blasting undesirable consequences, which is resulted during emission of vibration in blasted bench. This study focuses on the PPV prediction in the surface mines. In this regard, two ensemble systems, i.e....

Prediction of bus travel time is a key component of an intelligent transportation system and has many benefits for both service users and providers. Although there is a rich literature on bus travel prediction, some limitations can still be observed. First, high-frequency and low-frequency bus routes have different characterizations in both operati...

The prospect of subsurface structures taking uncontrollable fire is a significant cause of stress held by people from all over the world. Mine fires and explosions have caused countless casualties and material losses for decades. A fire in a mine has the ability to rapidly contaminate the air of the entire mine, which might result in the loss of li...

Tunnels have been constructed in many countries around the world for different purposes, such as the metro system to mitigate traffic congestion. Since the construction of urban tunnels is typically conducted at shallow depths, specific concerns such as structural damage inevitably arise. Surface settlement induced by tunnelling is one of the commo...

Fly-rock induced by blasting is an inevitable phenomenon in quarry mining, which can give rise to severe hazards, for example, causing damage to buildings and human life. Thus, successfully estimating fly-rock distance is crucial. Many researchers attempt to develop empirical, statistical, or machine learning models to accurately predict fly-rock d...

Blasting operations involve some undesirable environmental issues that may cause damage to equipment and surrounding areas. One of them, and probably the most important one, is flyrock induced by blasting, where its accurate estimation before the operation is essential to identify the blasting zone's safety zone. This study introduces several tree-...

Polymer concrete, which contains silica fume powder and vinyl ester resin as two replacements for Portland cement, has improved mechanical properties and durability compared to ordinary concrete. Thus, this kind of concrete is considered to be a high-strength concrete that is resistant to corrosion and chemical attacks. In this paper, the effects o...

Overbreak is a detrimental phenomenon caused by tunnel blasting, which can lead to increased time and cost in the construction schedule. It is very important to establish a model that can accurately predict the overbreak caused by tunnel blasting. To achieve this goal, the random forest (RF) is an ensemble machine learning model optimised by three...

The existing strategy for evaluating the damage condition of structures mostly focuses on feedback supplied by traditional visual methods, which may result in an unreliable damage characterization due to inspector subjectivity or insufficient level of expertise. As a result, a robust, reliable, and repeatable method of damage identification is requ...

Peak particle velocity (PPV) caused by blasting is an unfavorable environmental issue that can damage neighboring structures or equipment. Hence, a reliable prediction and minimization of PPV are essential for a blasting site. To estimate PPV caused by tunnel blasting, this paper proposes two neuro-based metaheuristic models: neuro-imperialism and...

Elevating the accuracy of streamflow forecasting has always been a challenge. This paper proposes a three-step artificial intelligence model improvement for streamflow forecasting. Step 1 uses long short-term memory (LSTM), an improvement on the conventional artificial neural network (ANN). Step 2 performs multi-step ahead forecasting while establi...

When building geotechnical constructions like retaining walls and dams is of interest, one of the most important factors to consider is the soil's shear strength parameters. This study makes an effort to propose a novel predictive model of shear strength. The study implements an extreme gradient boosting (XGBoost) technique coupled with a powerful...

Tunnel Boring Machines (TBMs) have been increasingly used in tunnelling projects. Forecasting future TBM performance would be desirable for project time management and cost control. We aim to use recurrent neural networks to predict the near future TBM penetration rate from historical data. Our datasets are composed of Changsha and Zhengzhou metro...

Because of the complicated geometry and a lack of knowledge about the parameters that impact it, estimating the ultimate bearing capacity (qrs) of a geogrid-reinforced sandy bed on vertical stone columns in soft clay is challenging. In practical applications, developing an accurate prediction model can be beneficial due to the difficulty and expens...

We aimed to develop a prediction model for intensive care unit (ICU) hospitalization of Coronavirus disease-19 (COVID-19) patients using artificial neural networks (ANN). We assessed 25 laboratory parameters at first from 248 consecutive adult COVID-19 patients for database creation, training, and development of ANN models. We developed a new alpha...

There are usually two types of slopes in nature: soil and rock. However, the granular slope has gradually come into view and has caused concern. In this paper, a modified Mohr-Coulomb (MC) criterion has been proposed to estimate the strength parameters of medium materials using a new parameter (τ 0) instead of the traditional MC parameter (c). Mean...

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 (compr...

The resilient modulus (MR) of ballast is one of the key output parameters in any rail design project because it controls the elastic magnitude of track deformation under cyclic loading. This study investigates the response of MR under cyclic conditions as a function of four key parameters, i.e., the loading magnitude, the number of loading cycles,...

Reinforced concrete bond strength deterioration is one of the most serious problems in the construction industry. It is one of the most common factors impacting structural deterioration and the major cause of premature decadence of reinforced concrete structures. Therefore, developing an accurate model with the lowest variance and high reliability...

Soft computing (SC) refers to the ability of a digital computer or robot to perform functions that are normally associated with intelligent individuals, such as reasoning and problem solving. An example of this would be a project aimed at creating systems capable of reasoning, discovering meaning, generalising, or learning from past experience. Sci...

The use of three artifcial neural network (ANN)-based models for the prediction of unconfned compressive strength (UCS) of granite using three non-destructive test indicators, namely pulse velocity, Schmidt hammer rebound number, and efective porosity, has been investigated in this study. For this purpose, a sum of 274 datasets was compiled and use...

The collapse settlement of granular soil, which brings about considerable deformations, is an important issue in geotechnical engineering. Several factors are involved in this phenomenon, which makes it difficult to predict. The present study aimed to develop a model to predict the collapse settlement and coefficient of stress release of sandy grav...

Air overpressure (AOp) is an undesirable environmental effect of blasting. To date, a variety of empirical equations have been developed to forecast this phenomenon and prevent its negative impacts with accuracy. However, the accuracy of these methods is not sufficient. In addition, they are resource-consuming. This study employed support vector re...

Prediction of pile bearing capacity has been considered an unsolved problem for years. This study presents a practical solution for the preparation and maximization of pile bearing capacity, considering the effects of time after the end of pile driving. The prediction phase proposes an intelligent equation using a genetic programming (GP) model. Th...

After earthquakes, qualified inspectors typically conduct a semisystematic information gathering, physical inspection, and visual examination of the nation’s public facilities, buildings, and structures. Manual examinations, however, take a lot of time and frequently demand too much work. In addition, there are not enough professionals qualified to...

Electric vehicles (EVs) have been progressing rapidly in urban transport systems given their potential in reducing emissions and energy consumptions. The Shared Free-Floating Electric Scooter (SFFES) is an emerging EV publicized to address the first-/last-mile problem in travel. It also offers alternatives for short-distance journeys using cars or...

In this paper, a rotation-invariant local binary pattern operator equipped with a local contrast measure (riLBPc) is employed to characterize the type of mineral twinning by inspecting the texture properties of crystals. The proposed method uses photomicrographs of minerals and produces LBP histograms, which might be compared with those included in...

During excavation of roadheader, specific energy (SE) is a key component of rock cuttability evaluation and cutting head design. Previous studies have shown that the specific energy is simultaneously affected by physical and mechanical parameters of rock, pick geometry, and pick operation parameters. In the paper, six machine learning (ML) algorith...

Coal pillar assessment is of broad importance to underground engineering structure, as the pillar failure can lead to enormous disasters. Because of the highly non-linear correlation between the pillar failure and its influential attributes, conventional forecasting techniques cannot generate accurate outcomes. To approximate the complex behavior o...

In blasting operations, a huge amount of energy is normally wasted to generate some unwanted issues in the surrounding zones, e.g., flyrock and oversize boulders. In this study, to solve these environmental issues of blasting, two phases of prediction and optimization were considered and applied. In the phase of prediction, the performance of two s...

Accurate prediction of TBM performance is very important for efficient completion of TBM construction tunnel project. This paper aims to predict the advance rate (AR) of tunnel boring machine (TBM) using three hybrid models by combining three swarm intelligence optimization algorithm (Ant Lion Optimizer (ALO), Loin swarm optimization (LSO) and Seag...

Physico-mechanical properties of rocks have a direct correlation with the drilling rate of percussive drill. The prediction of drilling rate is important for the deployment of drills during the planning stage. In tropical climatic regions, limestone is classified as blocky, very blocky, blocky/ seamy and disintegrated based on the degree of weather...

Blasting is an economical and practical construction technique for excavating underground structures. However, there are some undesirable environmental issues caused by tunnel blasting. One of the detrimental effects induced by tunnel blasting is overbreak. To alleviate the influence of overbreak, some techniques like empirical, statistical, and nu...

Rock mass classifications are in vogue for quite some time now. Lot of work has been done on the subject for engineering design of underground structures and their support, ground control, slope stability, foundation and rippability for excavations of varied nature. The essence of such classification systems is that most of them include information...

During design and construction of buildings, the employed materials can substantially impact the structures’ performance. In composite columns, the properties and performance of concrete and steel have a significant influence on the behavior of structure under various loading conditions. In this study, two metaheuristic algorithms, particle swarm o...

Since determining the rock deformation directly in the laboratory is costly and time consuming, it is important to reliably determine/estimate this parameter through the use of several simple rock index tests. This study develops a new hybrid intelligent technique according to Takagi–Sugeno Fuzzy Inference System-Group Method of Data Handling optim...

Fiber-reinforced polymer (FRP) has several benefits, in addition to excellent tensile strength and low self-weight, including corrosion resistance, high durability, and easy construction, making it among the most optimum options for concrete structure restoration. The bond behavior of the FRP-concrete (FRPC) interface, on the other hand, is extreme...

The aim of this paper is to extend a stochastic model for simulation of highly nonstationary ground motions (GMs). In this model, the dual-tree complex discrete wavelet transform (DT-CDWT) is applied to a recorded ground motion to extract its wavelet coefficients and then the Smooth Transition Generalized Autoregressive Conditional Heteroscedastic...

The relevance vector machine (RVM) is considered a robust machine learning method and its superior performance has been confirmed through many successful engineering applications. To improve the performance of the RVM model, three single kernel functions, and three multikernel functions, including two newly proposed multikernel functions, tenfold c...

The occurrence of unpredictable hazards are frequent with the increased depth of mining, especially the hazards caused by stress concentration. In order to mitigate the negative effectiveness results from mining-induce stress, various approaches have been employed in underground mines. Destress blasting, as an efficient method, has gained a lot of...

The squeezing behavior of surrounding rock can be described as the time-dependent large deformation during tunnel excavation, which appears in special geological conditions, such as weak rock masses and high in situ stress. Several problems such as budget increase and construction period extension can be caused by squeezing in rock mass. It is sign...

In the mining industry, the most common approach to rock fragmentation is blasting. Blasting operations generate flyrock, which is a critical and tough task, and its assessment is critical in decreasing related hazards. In this study, ensemble learning approaches such as simple averaging ensemble, weighted averaging ensemble, integrated stacking mo...

The occurrence of rockburst can cause significant disasters in underground rock engineering. It is crucial to predict and prevent rockburst in deep tunnels and mines. In this paper, the deficiencies of ensemble learning algorithms in rockburst prediction were investigated. Aiming at these shortages, a novel machine learning model, deep forest, was...

Accurate and reliable predictions of rock deformations are crucial in many rock-based projects in civil and mining engineering. In this research, a new system for the prediction of rock deformation was developed using various machine learning models, including multi-layer perceptron (MLP), the k-nearest neighbors (KNN), random forest (RF), and tree...

Air overpressure is a critical negative effect of blasting in construction or production sites and projects. So far, many attempts have been made to prevent or reduce this negative effect on the nearby construction, equipment, or people. While various experiential equations have been proposed to forecast the air overpressure value for determining t...

Among the research hotspots in geological/geotechnical engineering, research on the prediction of soil liquefaction potential is still limited. In this research, several machine-learning methods were developed to evaluate the liquefaction potential of soil using random forest (RF) as the base model. The parameters of the RF model were optimized usi...

Blasting is a common technique for rock breakage in the numerous civil and mining engineering activities such as excavation, leveling, and tunneling. However, this technique has several environmental issues, such as ground vibration. More importantly, the peak particle velocity (PPV), as the main indicator of ground vibration, should be considered...

Slope stability analysis allows engineers to pinpoint risky areas, study trigger mechanisms for slope failures, and design slopes with optimal safety and reliability. Before the widespread usage of computers, slope stability analysis was conducted through semi analytical methods , or stability charts. Presently, engineers have developed many comput...

Tunnel construction is a complex technology, with a huge number of effective parameters, which cannot be accurately analyzed/designed using empirical or theoretical methods. With the rapid development of computer technologies, Soft Computing (SC) approaches have been widely used in tunnel construction. Typically, the two common tunneling methods, b...

Rockburst is a severe geological hazard that restricts deep mine operations and tunnel constructions. To overcome the shortcomings of widely used algorithms in rockburst prediction, this study investigates the ensemble trees, i.e., random forest (RF), extremely randomized tree (ET), adaptive boosting machine (AdaBoost), gradient boosting machine, e...

Tunnel construction is a complex technology, with a huge number of effective parameters, which cannot be accurately analyzed/designed using empirical or theoretical methods. With the rapid development of computer technologies, Soft Computing (SC) approaches have been widely used in tunnel construction. Typically, the two common tunneling methods, b...

Effective prediction of the peak shear strength (PSS) is of crucial importance in evaluating the stability of a rock slope with interlayered rocks and has both theoretical and practical significance. This paper offers two novel prediction tools for the PSS prediction based on radial basis function neural network (RBFNN) and meta-heuristic computing...