Abstract Structural health monitoring (SHM) is a multi-discipline field that involves the automatic sensing of structural loads and response by means of a large number of sensors and instruments, followed by a diagnosis of the structural health based on the collected data. Because an SHM system implemented into a structure automatically senses, evaluates, and warns about structural conditions in real time, massive data are a significant feature of SHM. The techniques related to massive data are referred to as data science and engineering, and include acquisition techniques, transition techniques, management techniques, and processing and mining algorithms for massive data. This paper provides a brief review of the state of the art of data science and engineering in SHM as investigated by these authors, and covers the compressive sampling-based data-acquisition algorithm, the anomaly data diagnosis approach using a deep learning algorithm, crack identification approaches using computer vision techniques, and condition assessment approaches for bridges using machine learning algorithms. Future trends are discussed in the conclusion.
Kamal M. Hammad, Alexey I. Salimon, Eugene S. Statnik
et al.
Fiber reinforced polymer (FRP) composites are multi-scale and heterogeneous, therefore, the reliable prediction of their failure modes and strength is not a simple task. This review hinges on answering the need for improved methods of strength evaluation via judicious combination of experimental testing and computational modeling. The present report focuses on how experimental approaches and advanced multiscale simulations should range simultaneously from micro-scale elementary specimen testing through coupon and component scale tests until the full assembly level based on the modified Carbon Fiber Reinforced Polymer (CFRP) pyramid of testing and design for more reliable aircraft structures. This illustrates the progressive refinement of the approach known as the Rational Experimental-Computational Correlation (RECC). RECC is used to correlate finite element (FE) modeling of the failure mechanisms including matrix cracking, fiber pull-out, delamination, and interface debonding, on the one hand, with the experimental results obtained from quasi-static mechanical testing equipped with Digital Image Correlation (DIC) for displacement mapping and strain visualization. The current review also highlights the importance of multi-scale characterization and modeling of hierarchically structured materials where micro-macro interaction contributes strongly to the observed and predicted deformation response. The paper recommends a comprehensive approach consisting of full field mapping and maintaining systematic connection between coupon-level and structural scale consideration, with further upscaling to higher dimensional studies of ready articles such as key aircraft structures (stringers, ribs and complete wings). Being able to link micro-macro properties through multiscale simulations holds the key to improving the reliability of strength prediction in aerospace applications.
Fares Alawwa, Rashid K. Abu Al-Rub, Bashar El-Khasawneh
et al.
Interpenetrating phase composites (IPCs) are composite materials characterized by co-continuous and interlocked matrix and reinforcing phases. This distinctive combination often leads to superior performance compared to conventional composite assemblies. The present work investigates nitinol-aluminum metal-metal (MM) IPCs fabricated for the first time using a combination of additive manufacturing and spark plasma sintering (SPS) techniques. Using this approach, aluminum lattice constructs are first obtained by means of laser powder bed fusion. The constructs are then filled with pre-alloyed nitinol powder, which is subsequently sintered using SPS. Our investigation considers five different combinations of sintering temperatures and pressure. The samples are examined at both proximal and distal positions relative to the aluminum phase in order to evaluate the influence of temperature, stress, and possible alloying on the microstructure of the IPC and the phase transformation behavior of the nitinol phase. The interface between the nitinol and aluminum phases, as well as the elemental distribution within the IPCs, are characterized using scanning electron microscopy that reveal clear penetration and alloying of nickel and titanium within the aluminum phase. Differential scanning calorimetry further reveals variations in phase transformation temperatures within each sample. The nitinol phase in the IPC samples is shown to retain its shape memory properties. This study opens a new pathway for designing nitinol composites combining low melting point additively manufactured aluminum preforms with a high melting point nitinol matrix. This approach deviates from conventional metal-metal IPC fabrication methods, while still preserving the functional response of the nitinol phase.
Mari Carmen Taboada, Mariane Chludzinski, Raul Gómez
et al.
The demand for electric mobility has driven the development of advanced laser welding technologies such as dual beam welding and beam shaping. Nevertheless, some intrinsic characteristics present challenges to exploring all its benefits. In this sense, this study investigates the effect of the laser welding parameters employed on the weld quality in busbar–battery interconnects. Dual beam and beam shaping strategies were applied in Al-Al (AA1050 H24) and Al-Cu (AA1050 H24 and C11000) overlap joint configurations adopting statistical methods. For Al-Al joints, welding speed was the most significant parameter influencing interface width, whereas in Al-Cu joints, core power was the only significant parameter affecting both interface width and penetration in the studied configuration. Common defects, such as porosity and cracks, were observed in both material combinations. In Al-Al joints, higher welding speeds resulted in up to a 16% (65.6 HV) increase in hardness, while, in Al-Cu joints, the peak value reached around 900 HV in the interface zone due to the formation of intermetallic compounds (IMCs). In addition, IMCs with complex structures and significant compositional variations, including Cu<sub>9</sub>Al<sub>4</sub> and CuAl<sub>2</sub> were identified.
The L-shaped horizontal shaft of surface mining is an effective technical means to realize the corner gas control in the upper corner of coal mining face. At present, there are problems such as difficulties in well completion and unstable extration effects in the construction process of L-shaped wells of surface mining. In order to improve the treatment effect of the upper corner and return air gas in the working face, the 1076 working face of Yangliu Coal Mine in Huaibei Mining Area was taken as the research object, and the failure law of overburden mining in Yangliu Mine was revealed by theoretical analysis and numerical simulation, the development height of the “two belts” was determined, the location of the L-shaped mining well was optimized, and the key technologies of efficient drilling and completion well of L-shaped horizontal wells of surface mining are proposed from optimized wellbore structure design, three-stage trajectory optimization of horizontal sections, precise control of long horizontal section wellbore trajectory, enhanced drilling of the mining stratum and washing completion with rotating lower screen and double-layer tubing. The results show that: the maximum development height of the collapsing zone of the mining area in Yangliu Mine is about 13 m, and the maximum development height of the fracture zone is about 40 m. The L-shaped mining horizontal well should be arranged vertically at a distance of 22-31 m from the coal seam in the roof and horizontally at a distance of 11.8-47.7 m from the return air passage on the side of the “O” ring to facilitate gas extraction; the maximum vertical ratio of the large displacement mining horizontal well is 2.50, the drilling rate of the target section is 95%, and the drilling efficiency of the horizontal section is increased by more than 20% compared with the conventional process. In the mining period of 1076 working face of Yangliu Coal Mine, the maximum pure amount of pressure relief gas extraction in a single well is 9.33 m3/min, the cumulative gas extraction volume reaches 1.06 million m3, the gas extraction rate of the surface well reaches 81%, and the return air gas volume fraction is less than 0.3% during the mining period, so as to ensure the safe mining of the working face.
Chaos engineering reveals resilience risks but is expensive and operationally risky to run broadly and often. Model-based analyses can estimate dependability, yet in practice they are tricky to build and keep current because models are typically handcrafted. We claim that a simple connectivity-only topological model - just the service-dependency graph plus replica counts - can provide fast, low-risk availability estimates under fail-stop faults. To make this claim practical without hand-built models, we introduce model discovery: an automated step that can run in CI/CD or as an observability-platform capability, synthesizing an explicit, analyzable model from artifacts teams already have (e.g., distributed traces, service-mesh telemetry, configs/manifests) - providing an accessible gateway for teams to begin resilience testing. As a proof by instance on the DeathStarBench Social Network, we extract the dependency graph from Jaeger and estimate availability across two deployment modes and five failure rates. The discovered model closely tracks live fault-injection results; with replication, median error at mid-range failure rates is near zero, while no-replication shows signed biases consistent with excluded mechanisms. These results create two opportunities: first, to triage and reduce the scope of expensive chaos experiments in advance, and second, to generate real-time signals on the system's resilience posture as its topology evolves, preserving live validation for the most critical or ambiguous scenarios.
Many organisational problems are addressed through systemic change and re-engineering of existing Information Systems rather than radical new design. In the face of widespread IT project failure, devising effective ways to tackle this type of change remains an open challenge. This work discusses the motivation, theoretical foundation, characteristics and evaluation of a novel framework - referred to as POE-$Δ$, which is rooted in design and engineering and is aimed at providing systematic support for representing, structuring and exploring change problems of a socio-technical nature, including implementing their solutions when they exist. We generalise an existing framework of greenfield design as problem solving for application to change problems. From a theoretical perspective,POE-$Δ$ is a strict extension to its parent framework, allowing the seamless integration of greenfield and brownfield design to tackle change problems. A Design Science Research methodology was applied over a decade to define and evaluate POE-$Δ$, with significant case study research conducted to evaluate the framework in its application to real-world change problems of varying criticality and complexity. The results show that POE-$Δ$ exhibits desirable characteristics of a design approach to organisational change and can bring tangible benefits when applied in practice as a holistic and systematic approach to change in socio-technical contexts.
AbstractModern mine planning techniques are advancing at an incredible rate, and mining companies are progressively more interested in quantifying the uncertainty in their business plans. However, for many decision makers and other stakeholders, the leap from tried-and-true deterministic techniques to stochastic methods will be challenging to understand and accept due to the complexity of the work involved. At the 2019 SME Annual Conference, the author introduced a multifaceted model to embrace uncertainty in mine planning (Roos 2019), and at APCOM 2023, the author brought forth some modern data visualization techniques that can be leveraged to achieve this goal (Roos 2023). The purpose of this model is to fill the gap in embracing uncertainty by recommending improvements in visualization and communication techniques to ensure that all mining operations can take steps toward embracing uncertainty quantification. This paper presents a case study leveraging this model to adapt the cut-off grade selection process for an underground gold mine to one that utilizes grade uncertainty and provides the appropriate decision makers with the information they need to embrace the uncertainty in their business plan. The methods presented in this paper do not produce a finite solution to the cut-off grade selection process, but instead provide additional information that can be used in understanding the impact of geological uncertainty on the results of a deterministic study. In this study, the visualization techniques presented here have been useful in identifying areas of the deposit with a higher risk of achieving the estimated grade and also areas where there may be an opportunity to develop future stopes with additional geological information.
Shaanxi province is rich in coal resources and is one of the main coal-producing provinces in China, with a coal production of 746 million tons in 2022. Due to the obvious differences in geological and hydrogeological conditions in the coal mining areas of the province, there are various types of water disasters in its coal mines. With the increase of coal mining intensity in recent years, the frequency of water disasters increases significantly, and the situation of water prevention and control is challenging. On the basis of the overall analysis of the hydrogeological conditions in the coal mining areas of Shaanxi province, combined with the types of water disasters revealed in the mining process of various mining areas in the province, the regional distribution of all kinds of water disas-ters is systematically summarized, and the formation mechanism and characteristics of typical water disasters are discussed. Also, the corresponding prevention and control technology and progress are put forward. The results show that ① the main coal producing areas in Shaanxi province are divided into northern Shaanxi, Huanglong and Weibei. The Jurassic coalfield in northern Shaanxi is mainly affected by the water disaster of roof's loose sand layer, the water disaster of thick sandstone, water and sand burst disaster, and the burnt rock flood exists in some coal mines. The Huanglong coalfield is mainly threatened by roof super-thick sandstone water, separated layer water, mud and sand burst disaster or hidden danger. The Carboniferous-Permian coalfields in northern Shaanxi and Weibei are mainly threatened by the water damage of Ordovician limestone on the coal seam floor. ② The hidden danger of roof flood in the Jurassic coalfield in northern Shaanxi is mainly formed by the disturbance of roof aquifer by mining water diversion fracture zone, which can form a continuous large flow water gushing through sandstone aquifer or loose sand layer, and the water filling intensity is large in some areas. Roof flood occurs when the amount of water exceeds the capacity of the drainage system in a short time, and it can be transformed into water and sand burst disaster when the collapse zone in the thin bedrock area directly leads to the loose sand layer. When the water-conducting fracture zone affects the water-rich area of burnt rock, it can form a flood of burnt rock with large instantaneous water volume and continuous flood. The super-thick sandstone aquifer on the roof of the Huanglong coalfield is swept by the mining water-conducting fracture zone to form a high-intensity continuous water gushing and may form roof flood. When the thickness of the lower aquifer is large, the separated layer water burst disaster may be formed. When the roof strata near the coal seam is soft and easy to collapse, it can be transformed into mud and sand burst disaster. In the Carboniferous-Permian coalfield and the Weibei coalfield in northern Shaanxi, when structures such as coal seam mining floor disturbance failure zones or faults lead to the Ordovician limestone aquifers, they will cause extremely serious floor water inrush disasters, which are characterized by strong sudden occurrence and large instantaneous water volume. ③ The roof water disaster treatment technology in each region mainly includes drilling hole drainage, grouting treatment, mining parameter control and so on, and the floor water damage is mainly treated by regional grouting reinforcement and plugging. Burnt rock water disaster is mainly treated by the combination of curtain grouting and drilling exploration.
The accumulation of ice and snow on cables in winter poses a serious threat to the transmission and safety of electricity. It is extremely necessary to prepare a strong superhydrophobic anti-icing coating on the surface of the cable to gently remove the ice and snow on its surface. In this study, we constructed rough structures using inexpensive nano SiO2, modified the hydrophobicity and flexibility of epoxy resin with acrylic resin and polydimethylsiloxane as modifiers, and prepared a robust superhydrophobic anti-icing coating that combines multiple binders. The coating was prepared by a simple air spraying process and has compatibility with both flat and curved surfaces. Compared with the pristine cable, this coating can reduce ice accumulation by about 4 times, shorten deicing time by about 2.1 times, and lower the average ice adhesion strength to 12.5 KPa, a reduction of about 90%; On stainless steel plates, the anti-icing time can be extended by about 5.8 times. In addition, the coating still has low ice adhesion strength and good hydrophobic properties after being scratched by a knife, impacted by 12 h of water flow, impacted by 500 mL sand drop, and peeled off by 60 adhesive tapes. The preparation of this coating has a simple process flow and excellent anti-icing performance, and has broad application prospects in the field of anti-icing for transmission lines.
Aiming at the promotion and application of 5G communication technology in coal mines, this paper analyzes the explosion prevention risk of radio frequency (RF) electromagnetic energy under the condition of far field radiation. In this paper, the equivalent model of the loop antenna and the theoretical calculation model of the spark power are established under the assumption that the characteristic impedance of the spark is matched with the internal impedance of the antenna. Secondly, for the 3.55 GHz operating frequency, a narrow bandwidth loop receiving antenna and a horn transmitting antenna by theoretical calculation are designed, and the S11 curve of the designed antenna by CST simulation software is verified. Finally, based on this, a simulation platform for transceiver antenna is built, which can be used to estimate the electric field strength and integrated voltage at both ends of the breakpoint of the loop antenna. The simulation results show that: when the electric discharge machining (EDM) is matched with the internal resistance and impedance of the antenna, the EDM power is maximum, and the impedance value of the EDM has no effect on the induced voltage of the whole antenna, and the power obtained by the EDM decreases with the increase of the distance between the transmitting and receiving antennas. The simulation and calculation show that when the transmitting antenna power is 100 W and the transmitting antenna gain is 5, the theoretical maximum spark power generated under the far-field radiation condition is not enough to ignite the gas. Therefore, the threshold power index in the standard may be too conservative and needs to be further verified.
Cu–15Ni–8Sn-xNb alloys (x = 0, 0.2, 0.6 wt%) were prepared using a medium-frequency induction melting furnace. The effect of Nb addition on grain refinement and inhibition of discontinuous precipitation (DP) in Cu–15Ni–8Sn alloys was systematically studied to elucidate the mechanism by which microstructural characteristics contribute to strength improvement. The results indicate that the increase the Nb content from 0 to 0.6 wt% reduces the average grain size of the as-cast alloy from approximately 524.8 μm to approximately 81.3 μm, and significantly decreases the lamellar transition structure region (α+γ). During solution treatment, dispersed needle-like NbNi3 phases were observed in the Cu–15Ni–8Sn-0.2Nb alloy. After aging, the DP growth rate in the Cu–15Ni–8Sn-0.2Nb alloy was notably slower than those of the other alloys. This was attributed to NbNi3 phases at the grain boundaries hindering DP nucleation, with intragranular NbNi3 phases inhibiting DP growth. The phase transformation order of the solid solution Cu–15Ni–8Sn-0.2Nb at 673 K aging is: spinodal structure → D022 ordered phase → L12 ordered phase → DP. The hardness and tensile strength of the Cu–15Ni–8Sn-0.2Nb alloy peaked at 338.3 HV and 725.42 MPa, respectively, after aging for 120 min. Using the Johnson–Mehl–Avrami–Kolmogorov (JMAK) and Arrhenius equations, the activation energies of DP in Cu–15Ni–8Sn-xNb (x = 0, 0.2, 0.6 wt%) alloys were calculated to be 93.19, 148.64, and 98.33 kJ/mol, respectively. These values suggest that the diffusion of DP atoms in the Nb-containing alloys is hindered, which effectively inhibits DP formation.
In the last two decades, several researchers provided snapshots of the "current" state and evolution of empirical research in requirements engineering (RE) through literature reviews. However, these literature reviews were not sustainable, as none built on or updated previous works due to the unavailability of the extracted and analyzed data. KG-EmpiRE is a Knowledge Graph (KG) of empirical research in RE based on scientific data extracted from currently 680 papers published in the IEEE International Requirements Engineering Conference (1994-2022). KG-EmpiRE is maintained in the Open Research Knowledge Graph (ORKG), making all data openly and long-term available according to the FAIR data principles. Our long-term goal is to constantly maintain KG-EmpiRE with the research community to synthesize a comprehensive, up-to-date, and long-term available overview of the state and evolution of empirical research in RE. Besides KG-EmpiRE, we provide its analysis with all supplementary materials in a repository. This repository contains all files with instructions for replicating and (re-)using the analysis locally or via executable environments and for repeating the research approach. Since its first release based on 199 papers (2014-2022), KG-EmpiRE and its analysis have been updated twice, currently covering over 650 papers. KG-EmpiRE and its analysis demonstrate how innovative infrastructures, such as the ORKG, can be leveraged to make data from literature reviews FAIR, openly available, and maintainable for the research community in the long term. In this way, we can enable replicable, (re-)usable, and thus sustainable literature reviews to ensure the quality, reliability, and timeliness of their research results.
Natural Language Processing (NLP) is now a cornerstone of requirements automation. One compelling factor behind the growing adoption of NLP in Requirements Engineering (RE) is the prevalent use of natural language (NL) for specifying requirements in industry. NLP techniques are commonly used for automatically classifying requirements, extracting important information, e.g., domain models and glossary terms, and performing quality assurance tasks, such as ambiguity handling and completeness checking. With so many different NLP solution strategies available and the possibility of applying machine learning alongside, it can be challenging to choose the right strategy for a specific RE task and to evaluate the resulting solution in an empirically rigorous manner. In this chapter, we present guidelines for the selection of NLP techniques as well as for their evaluation in the context of RE. In particular, we discuss how to choose among different strategies such as traditional NLP, feature-based machine learning, and language-model-based methods. Our ultimate hope for this chapter is to serve as a stepping stone, assisting newcomers to NLP4RE in quickly initiating themselves into the NLP technologies most pertinent to the RE field.
The Department of Civil, Materials, and Environmental Engineering (CME) offers programs leading to the Master of Science and Doctor of Philosophy degrees in Materials Engineering. Study and research is available in the areas of metallurgy, ceramics, nanomaterials, electronic materials, composites, welding and joining, solidification, corrosion, and processing. The department also offers programs leading to degrees in Civil Engineering at both the master’s and doctoral levels. Consult the appropriate sections of the catalog for more information. Updated information about the faculty, staff, curriculum, and courses is found on the CME home page.
Abstract The object of this article is to present the innovative technology system for the deep mining of metal mines in China. Based on the current situation and major problems of deep mining of metal mineral resources in China, the key engineering technology development strategies to solve the problems, such as the green intelligent mining modes, are proposed, which is conducive to ensuring the safety of mineral resources supply and the sustainable development of the national economy in China.