Journal of Engineering Research and Reports

Carbon-Fibre-Reinforced Carbon–Silicon Carbide Composites for Extreme Environments: A Critical Narrative Review of Processing Strategies, Interface Engineering, Oxidation Resistance and Engineering Applications

Jie Chen — 2026-10-01

Carbon-fibre-reinforced carbon–silicon carbide (C/C–SiC) and carbon-fibre-reinforced silicon carbide (C/SiC) composites occupy a distinctive position among materials for extreme environments because they combine low density, damage tolerance and thermal-shock resistance with a matrix that forms protective silica at high temperature. Their use in re-entry thermal protection, rocket propulsion and high-energy friction systems has nevertheless remained constrained by an unresolved tension between the carbon reinforcement, which is required for toughness, and its vulnerability to oxidation. This critical narrative review examines how processing strategies, interface engineering and oxidation-protection approaches interact to determine the engineering performance of these composites. Literature was identified through structured searching of multidisciplinary scholarly indexes and specialised technical repositories, supplemented by backward and forward citation tracking, and was appraised for methodological adequacy, test realism and consistency with independent evidence. The synthesis indicates that chemical vapour infiltration, polymer infiltration and pyrolysis, and liquid or gaseous silicon infiltration yield materially different phase assemblies, residual-stress states and crack networks, so that properties reported for one route cannot be transferred uncritically to another. Interphase optimisation is supported by consistent evidence that intermediate pyrolytic carbon thicknesses maximise strength and reliability, but the optimum depends on the matrix route and residual stress rather than on a universal value. Oxidation behaviour follows temperature-dependent regimes governed by reaction control, diffusion through matrix microcracks and crack sealing by silica, yet most evidence derives from short, unstressed laboratory exposures. Protective coatings and ultra-high-temperature ceramic modifications improve short-term ablation resistance, whereas evidence for long-duration, multi-cycle durability under combined mechanical, thermal and chemical loading remains limited. Friction applications show the most mature translation, while reusable hot structures remain constrained by environmental durability and manufacturing cost. The review concludes that progress now depends less on incremental compositional modification than on standardised coupled-environment testing, validated life-prediction models and process routes that control residual silicon, porosity and interphase integrity simultaneously.

Crude Oil Price and Nigeria's Economy: An Empirical Test of the Resource Curse, 2010–2023

Edeh Mark Bekweri — 2026-09-30

Background and Aims: Nigeria’s substantial dependence on crude petroleum for export earnings and public revenue makes its economy particularly exposed to movements in global oil prices. This study examined whether Brent crude oil price is significantly associated with Nigeria’s gross domestic product (GDP) after accounting for exchange-rate and inflation dynamics, and to assess whether the evidence is consistent with a resource-curse interpretation.

Study Design: An ex postfacto annual time-series design was used.

Place and Duration of Study: Nigeria; national annual data for 2010–2023, analysed in 2026.

Methodology: Fourteen annual observations were assembled from the Federal Reserve Economic Data repository and the Central Bank of Nigeria. An ordinary least squares regression was estimated on log-transformed GDP and Brent crude oil price, with the exchange rate and inflation rate as controls. A real-GDP specification was estimated as a robustness check.

Results: The primary model was statistically significant, F(3, 10) = 30.35, p < .001, R² = .90. Brent crude oil price was not a significant predictor of GDP (B = −0.02, p = .899), whereas the exchange rate was significant (p < .001). Brent price also remained non-significant in the real-GDP robustness model (p = .463).

Conclusion: The short annual sample provides no evidence of a significant contemporaneous association between Brent crude oil price and GDP after the controls were included. The pattern is consistent with, but does not establish, an exchange-rate-mediated resource-curse mechanism.

Voltage Stability Improvement of Nigerian 330 KV Network in South Western Region Using UPFC and ANN

Ekemini E. Udofia — 2026-09-30

Voltage instability remains a critical operational concern in the Nigerian 330 kV transmission network, particularly under disturbances and increasing loading. This study evaluated voltage-stability improvement in selected substations within the south-western region using a unified power flow controller (UPFC) and an artificial neural network (ANN). A five-bus representation comprising Egbin GS, Okearo TS, Aja TS, Akangba TS, and Ikeja West TS was modelled in the Power System Analysis Toolbox (PSAT), while MATLAB/Simulink was used for the analysis of power-flow and dynamic-voltage responses. Newton-Raphson power-flow analysis was applied to determine line active power losses and identify the controller-placement location. Line 3, connecting bus 1 (Egbin GS) to bus 5 (Ikeja West TS), recorded the highest active power loss of 0.1084 pu and was selected for UPFC and ANN allocation. Following implementation, the bus voltage profiles were improved to the stated operating range of 0.95–1.05 pu. The dynamic responses also showed reduced oscillations and shorter settling behaviour under both control approaches. Using the voltage stability index reported in the study, the ANN achieved 97%, whereas the UPFC achieved 93%. Under the specific simulation conditions considered, the ANN therefore produced the higher reported VSI and a smoother voltage response. The findings are limited to the model configuration and operating conditions evaluated in this study.

Development and Evaluation of Recycled Plastic–sand Tiles for Sustainable Construction Applications

Thin Thin Hlaing — 2026-10-01

Aims: This study developed construction tiles from recycled polyethylene (PE) waste and sand and evaluated the effects of PE proportion and heating and mixing method on tile properties.

Study Design: A quantitative, laboratory-based experimental design was used.

Place and Duration of Study: The study was conducted at the Chemical Technology Research Center, Department of Research and Innovation, Yangon, Myanmar, between April 2023 and March 2024.

Methodology: Recycled PE waste was cleaned, dried, mechanically cut, and sieved to approximately 500 µm. Sand was dried and sieved before use. Four PE proportions, 25%, 30%, 33.33%, and 35% based on sand weight, and three heating and mixing methods were investigated. The produced tiles were evaluated for water absorption, density, compressive strength, flexural strength, and tensile strength, with measurements conducted in triplicate.

Results: In the PE-proportion comparison, the 33.33% PE tile recorded the highest compressive strength (8.27 N/mm²), flexural strength (2.01 N/mm²), and tensile strength (2220 psi). In the processing-method comparison, Method 3 produced the highest compressive strength (15.97 N/mm²) and flexural strength (2.80 N/mm²), while Method 2 produced the highest tensile strength (3040 psi). The Method 3 tile had a density of 2.55 g/cm³ and water absorption of 3.4%.

Conclusion: The 33.33% PE formulation processed using Method 3 provided the most favourable overall performance among the investigated conditions and represents a potential route for value-added use of recycled PE waste in construction tiles.

Analysis of Concrete Formwork Inspection and Distance Measurement based on YOLOv5

Gu Shuhao — 2026-10-01

The joint quality of prefabricated buildings directly determines structural performance. To realise the automatic positioning of concrete pouring equipment for small-size joints in prefabricated buildings, this paper develops an intelligent positioning system integrating YOLOv5 object detection and binocular vision ranging. For the screw-hole detection task in prefabricated components, a custom dataset was built using 2,000 on-site images collected under various viewing angles and illumination conditions. Five models, including YOLOv5n/s/m/l/x are compared in terms of recall, precision, mAP, and box regression loss. Considering detection accuracy, hardware cost, and inference speed collectively, YOLOv5l is selected as the object detection model. Zhang’s calibration method was adopted to calibrate the binocular camera and obtain its internal and external parameters. Using the SGBM semi-global stereo-matching algorithm, the depth and 3D positions of target screw holes were calculated based on the binocular triangulation principle. Data exchange between the host computer and PLC was achieved via the Modbus TCP protocol. The 3D coordinates of targets obtained through visual recognition are transmitted to the PLC to drive the end effector to complete positioning operations. A scaled-down indoor test platform was built for verification experiments. The effect of different shooting heights on screw-hole recognition was tested to determine the optimal working height. Experimental results showed that the average recognition accuracy of screw holes was 93.4% at a working distance of 600 mm. The measurement error of the target 3D coordinates was within the allowable engineering range, and the displacement values fed back by the encoder were generally consistent with the reference values measured using a laser rangefinder. The system achieves full-process automation of screw hole recognition, binocular ranging, positioning and operation. With moderate cost and practical applicability, the proposed scheme can provide technical support for intelligent pouring equipment for joints of prefabricated buildings.

Design and Analysis of Forced Drop Hammer Hydraulic Compactor for Soft Foundation Reinforcement of Large Dike Breach

Chen Yi — 2026-10-01

This study addresses the engineering challenges of high water content, low bearing capacity, poor equipment accessibility and unsatisfactory reinforcement performance in soft foundations following large-scale dike breaches. A forced-drop-hammer hydraulic compactor is designed for emergency foundation reinforcement at dike-breach sites. Based on the geotechnical characteristics of post-breach soft soil, the overall system configuration and hydraulic-circuit scheme are determined, and a constant-pressure, low-shock hydraulic circuit for forced-drop-hammer operation is optimised. Dynamic governing equations describing the complete hammer-lifting and compaction cycle are established. The AMESim platform is used to investigate system-pressure fluctuations, dynamic response and hammer-motion stability under multiple working conditions. Comparative laboratory-scale model tests are conducted to identify an appropriate compaction-parameter range for dike-breach soft ground, and a targeted emergency construction procedure is proposed for on-site rescue applications. The simulation and laboratory-test results show that the optimised hydraulic power supply maintains stable constant-pressure output with reduced hydraulic shock and reliable cyclic compaction performance. Within the recommended parameter range, the compactness of the simulated soft foundation increases by more than 12 %, improving compaction uniformity and reinforcement quality. The findings provide support for structural design and hydraulic-parameter matching of similar compaction equipment and offer a technical reference for rapid emergency reconstruction of breached dikes.

Experimental Characterization of Motor Fault Conditions Using Vibration, Gyroscope, and Acoustic Measurements with an Arduino-Based Monitoring System

Aarosh Srivastava — 2026-10-05

Background: Electric motors are widely used in industrial and electromechanical systems, making reliable condition monitoring important for detecting mechanical abnormalities before performance deterioration occurs. However, individual fault conditions may produce different responses depending on operating speed, fault geometry, mounting conditions, and sensor modality. Low-cost multisensor platforms integrating vibration, angular-motion, and acoustic measurements therefore provide a practical approach for experimentally characterising distinct motor fault signatures.

Aims: To develop and experimentally evaluate a multisensor platform that identifies controlled motor fault conditions using vibration, angular motion, and acoustic telemetry.

Study Design: A benchtop repeated-measures experiment was conducted to test a healthy baseline and six controlled mechanical conditions across four rotational speeds.

Methodology: A 12-V geared DC motor was controlled by an Arduino Mega 2560 and monitored with an Arduino Nano 33 BLE Sense Rev2, logging telemetry at 40, 80, 100, and 130 RPM. Six fault states were evaluated: four rotor mass-distribution configurations, a 15-degree shaft misalignment, and loosened motor-fixing screws, with three replicates per fault-speed combination. Trial-level features included AC resultant acceleration RMS, Dynamic-G variability, gyroscope motion, relative acoustic RMS, and packet-loss rate.

Results: Vibration magnitude depended on mass distribution rather than total added mass. Two adjacent eccentric masses produced the strongest vibration, three masses approached the one-mass response, and four symmetrically distributed masses reduced vibration despite the greatest attached mass. Configuration effects were strongest at 100 RPM (one-way ANOVA, F = 66.90, P < .001) and 130 RPM (F = 32.69, P < .001). The 15-degree misalignment produced a directional, non-monotonic vibration response, while loosened motor mounting produced a comparatively flat vibration response consistent with altered structural coupling. Acoustic RMS correlated with speed rather than fault type; packet loss remained at 13-14% regardless of mechanical state.

Conclusion: An embedded multisensor platform reliably identifies motor fault signatures when speed-specific and multidimensional features are considered. The results also show that a mechanical fault should not be interpreted simply as an increase in overall vibration amplitude.

Experimental Investigation of the Compressive Behavior of Hybrid Fiber-Reinforced Concrete and AI-Agent-Assisted Modeling

Hanbo Zhang — 2026-10-10

Hybrid fibre reinforcement is increasingly used to improve the mechanical performance and failure behaviour of concrete by combining the complementary characteristics of different fibre types. However, the effects of different hybrid fibre combinations on compressive behaviour and mesoscale failure mechanisms, particularly when integrated with AI-assisted discrete element modelling, remain insufficiently understood. This study investigated the compressive behaviour and mesoscale failure mechanisms of concrete reinforced with steel, macro-synthetic, and polypropylene fibres, individually and in hybrid combinations, and evaluated an AI-agent-assisted PFC3D modelling workflow. Thirteen mixture groups comprising 39 cubic specimens were subjected to uniaxial compression testing. Steel-fibre concrete achieved a compressive strength of 39.7 MPa compared with 35.0 MPa for plain concrete, whereas macro-synthetic fibre concrete reached 35.7 MPa and polypropylene fibre concrete decreased to 25.0 MPa. Fibre incorporation generally changed failure from localised crack coalescence to more distributed cracking and improved post-peak specimen integrity. Three representative mixtures were modelled using PFC3D with an AI agent supporting task generation, script execution, feedback-based correction, and result recording. Simulated peak strengths of 34.2, 37.3, and 26.5 MPa for the N0, SM, and MP models, respectively, were within 5% of the corresponding experimental values. AI-agent-assisted modelling reduced the reported total modelling time from 142 to 34 min, corresponding to an efficiency improvement of 76.1%. The first-run task success rate was 61.1%, while all staged tasks were completed after correction. Repeated simulations produced low coefficients of variation for peak stress and crack number. The findings indicate that steel-macro-synthetic hybridisation provided comparatively favourable compressive performance, while the AI-assisted workflow supported efficient and reproducible discrete element modelling.