Nidhal Selmi, Jean-michel Bruel, Sébastien Mosser
et al.
Decision-making is a core engineering design activity that conveys the engineer's knowledge and translates it into courses of action. Capturing this form of knowledge can reap potential benefits for the engineering teams and enhance development efficiency. Despite its clear value, traditional decision capture often requires a significant amount of effort and still falls short of capturing the necessary context for reuse. Model-based systems engineering (MBSE) can be a promising solution to address these challenges by embedding decisions directly within system models, which can reduce the capture workload while maintaining explicit links to requirements, behaviors, and architectural elements. This article discusses a lightweight framework for integrating decision capture into MBSE workflows by representing decision alternatives as system model slices. Using a simplified industry example from aircraft architecture, we discuss the main challenges associated with decision capture and propose preliminary solutions to address these challenges.
ObjectiveTo explore the effects of the dosage and application time of MH saline-alkali land improvement materials prepared from pulverized coal furnace fly ash on the physical and chemical properties of saline-alkali soil, MethodMH saline-alkali land improvement material prepared with fly ash produced by the pulverized coal furnace process as the base material was subjected to a 25-day soil pot experiment indoors. ResultWith different application rates of MH saline-alkali land improvement materials, the pH value, HCO3-, and exchangeable sodium ion content of the saline-alkali soil all showed a gradually decreasing change pattern over time. When the application rate was 2.5% and the saline-alkali soil was cultivated indoors for 25 days, the soil pH value decreased by 0.64 units and HCO3- decreased by 57.16%. The content of exchangeable sodium ions decreased by 77.27%. Phosphorus and fast-acting potassium content were increased to different degrees, soil bulk weight was reduced to 1.40 g/cm3, porosity was increased by 6.83%, and the proportional distribution of soil solid, liquid and gas was significantly modified. However, organic matter and cation exchange were reduced by 46.06% and 29.96%, respectively, compared with the test soil. ConclusionMH saline improvement materials can effectively regulate the physical and chemical properties of saline soil and improve the crop growth environment.
Abstract To reduce the roadway drivage ratio, drivage cost, and safety accidents, and increase the resource recovery ratio, a no-pillar mining technique with automatically formed gob-side entry retaining is proposed for longwall mining. In this technique, pressure is offloaded via directional roof cutting, and a roadway is automatically formed via the ground pressure and rock breaking expansion. These processes permit the implementation of a new longwall mining process in which there is no mining roadway and no reserves of coal pillars. In this paper, the concepts and key techniques (the technique of directional roof cutting, the formation technique of integrated coal side of the automatically formed gob-side entry retaining, the formation technique of gob-side gangue rib, and the support technique using a constant-resistance and large-deformation anchor cable) of no-pillar mining with automatically formed gob-side entry retaining are introduced, and a field engineering test is performed. The test results are as follows: the no-pillar mining concepts and technique with automatically formed gob-side entry retaining are feasible; all techniques and processes fully satisfy field production requirements; and the deformation of the automatically formed gob-side entry retaining is small, and the control effect is significant. These results prove that the no-pillar mining technique with automatically formed gob-side entry retaining is feasible for longwall mining and achieves the goal of safe and efficient mining.
Over the past ten years, the application of artificial intelligence (AI) and machine learning (ML) in engineering domains has gained significant popularity, showcasing their potential in data-driven contexts. However, the complexity and diversity of engineering problems often require the development of domain-specific AI approaches, which are frequently hindered by a lack of systematic methodologies, scalability, and robustness during the development process. To address this gap, this paper introduces the "ABCDE" as the key elements of Engineering AI and proposes a unified, systematic engineering AI ecosystem framework, including eight essential layers, along with attributes, goals, and applications, to guide the development and deployment of AI solutions for specific engineering needs. Additionally, key challenges are examined, and eight future research directions are highlighted. By providing a comprehensive perspective, this paper aims to advance the strategic implementation of AI, fostering the development of next-generation engineering AI solutions.
This work investigated the adhesion performance of recycled concrete with nano-SiO2 and nano-Al2O3 and steel bars in cold environments through freeze‒thaw cycle tests and tensile tests. The results show that the nanomaterials and number of freeze-thaw cycles have no significant effect on the damage morphology of the sample, but the adhesion strength and steel bar slip of the sample are considerably affected by the nanomaterials and number of freeze-thaw cycles. After the addition of the nanomaterials, the adhesion strength increased by 10.4 % (11.2 %), and the steel slip increased, by 17.3 % (16.8 %). The impact of freeze-thaw cycles on adhesion strength and adhesion slip is significant. The adhesion strength decreases by up to 40.8 % after 55 freeze-thaw cycles, and the slip of the steel bar increases by up to 101.9 %. In addition, an adhesion performance model of recycled concrete and steel bars mixed with nanomaterials in cold environments is established. The strengthening mechanism of nanomaterials and the mechanism of freeze-thaw damage is revealed. This study provides a basis for the practical application of recycled concrete technology and promotes the recyclable use of building materials.
Musfekur Rahman Dihan, Saymom Himel, Md Foysal Abedin
et al.
We here investigated for the first time how the surface morphology, pore volume, swelling, mechanical stability, and photocatalytic performance of the TiO2 photocatalyst-immobilized chitosan beads (TCB) change with the hot air drying and fully freeze drying. The fully lyophilized (freeze-dried) TCB exhibited the presence of wider surface cracks and fissures (8.45 ± 3.04 μm), and had 2.73- and 11.67-times higher surface area and pore volume as compared to the air-dried TCBs. The internal porous structure destruction during hot air drying led to the absence of surface pores, cracks, and fissures, resulting in only a 1.32 % water uptake capacity. However, the freeze-dried TCB had very low mechanical strength (100 % breakage and mass loss) compared to highly stable air-dried ones (15 % mass loss and 33 % breakage). We proposed a hybrid partial air drying (50 ± 10 %) followed by freeze drying for the TCB, which retained surface openings and cracks (6.3 ± 2.27 μm), good surface area (115.15 m2/g, 4.5 times higher pore volume), good mechanical stability, and a higher swelling capacity (227 %), alongside better photocatalytic performance. The optimum beads, prepared with 25 % TiO2 loading, a 1:1 crosslinker ratio, and air-freeze drying, were able to remove 75 % of the anionic dye within 120 min of operation with comparatively low catalyst doses. These beads showed excellent re-swelling capacity and photocatalytic performance in repeated recycles with air drying after each run. The estimated catalytic potential, synergy index, and relative contribution suggest that the synergistic nature of biopolymers and photocatalysts leads to higher degradation than the constituent processes.
With the increasingly demanding service conditions of coiled tubing, its welded joints require superior synergistic strength-toughness properties to meet comprehensive mechanical performance requirements. This study achieved synergistic optimization of strength and toughness in deposited metal via lanthanum microalloying technology and elucidated microstructural evolution mechanisms and fracture failure mechanisms via multi-scale characterization techniques. The results demonstrate that lanthanum oxide addition effectively modifies inclusion characteristics, inducing phase transformation from O-Mn-Si-Al-Ti to O-Mn-Si-Al-Ti-S-La, with average particle size significantly decreased from 0.19 μm to 0.12 μm. The deposited metal microstructure comprises lath bainite and granular bainite. The addition of 0.5 wt.% lanthanum oxide results in significant microstructural refinement: average grain size decreases from 1.16 ± 1.18 μm to 1.02 ± 1.00 μm, while granular bainite volume fraction decreases from 8.6% to 4.7%. The microstructural optimization also enhances mechanical properties substantially: yield strength increases from 628 ± 14 MPa to 673 ± 12 MPa, and impact toughness improves from 160 ± 6 J to 189 ± 6 J. Mechanistic analysis revealed that proper addition of lanthanum (0.5 wt.%) promotes grain refinement via heterogeneous nucleation and modifies inclusion morphology, effectively inhibiting crack initiation. However, excessive addition (1.0 wt.%) induces inclusion clustering, forming stress concentration sites that degrade mechanical properties.
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.
Aline de Campos, Jorge Melegati, Nicolas Nascimento
et al.
Generative Artificial Intelligence (GenAI) has become an emerging technology with the availability of several tools that could impact Software Engineering (SE) activities. As any other disruptive technology, GenAI led to the speculation that its full potential can deeply change SE. However, an overfocus on improving activities for which GenAI is more suitable could negligent other relevant areas of the process. In this paper, we aim to explore which SE activities are not expected to be profoundly changed by GenAI. To achieve this goal, we performed a survey with SE practitioners to identify their expectations regarding GenAI in SE, including impacts, challenges, ethical issues, and aspects they do not expect to change. We compared our results with previous roadmaps proposed in SE literature. Our results show that although practitioners expect an increase in productivity, coding, and process quality, they envision that some aspects will not change, such as the need for human expertise, creativity, and project management. Our results point to SE areas for which GenAI is probably not so useful, and future research could tackle them to improve SE practice.
In recent years, open-pit mining has seen significant advancement, the cooperative operation of various specialized machinery substantially enhancing the efficiency of mineral extraction. However, the harsh environment and complex conditions in open-pit mines present substantial challenges for the implementation of autonomous transportation systems. This research introduces a novel paradigm that integrates Scenario Engineering (SE) with autonomous transportation systems to significantly improve the trustworthiness, robustness, and efficiency in open-pit mines by incorporating the four key components of SE, including Scenario Feature Extractor, Intelligence and Index (I&I), Calibration and Certification (C&C), and Verification and Validation (V&V). This paradigm has been validated in two famous open-pit mines, the experiment results demonstrate marked improvements in robustness, trustworthiness, and efficiency. By enhancing the capacity, scalability, and diversity of autonomous transportation, this paradigm fosters the integration of SE and parallel driving and finally propels the achievement of the '6S' objectives.