Effects of asphalt binder and aggregate gradation on dynamic modulus, resilient modulus and moisture resistance of asphalt concretes

In this paper, the effects of asphalt binder and aggregate gradation on dynamic modulus (|E*|), resilient modulus (Mr) and moisture resistance of asphalt concretes (AC) have been studied. Six different asphalt concretes are designed with two aggregate gradations (nominal maximum aggregate size of 12.5 and 19 mm) and three types of asphalt binders (penetration grades of 40/50, 60/70 and a polymer-modified bitumen PMB3). Dynamic modulus, resilient modulus and Tensile Strength Ratio (TSR) test have been performed on the studied AC. The |E*| values obtained from dynamic modulus test at various frequencies and temperatures are simulated using a linear viscoelastic model 2S2P1D. Experimental results indicate the clear effect of asphalt binder and aggregate gradation on mechanical properties of tested AC. The 2S2P1D model successfully simulates the |E*| master curve with high precision (R² values from 0.84 to 0.97). The roles of 40/50 and PMB3 asphalt binder in enhancing the performance of asphalt concretes are also clearly demonstrated under each specific temperature condition and mechanical property type

Investigating the Applicability of GNSS Technology for Monitoring Horizontal Displacement of Wharf Structures

Wharves, vital components of maritime infrastructure, are intricate structures typically anchored by robust pile foundations. These essential facilities face substantial environmental and operational challenges, particularly in deep-water settings where forces are amplified. To maintain structural integrity and ensure safety, precise monitoring of horizontal displacement is of paramount importance. This comprehensive study delves into the application of cutting-edge Global Navigation Satellite System (GNSS) technology for tracking wharf movement. Researchers employed both static and kinematic GNSS techniques to capture detailed displacement data across various time scales, from short-term fluctuations to long-term trends. By meticulously analyzing the collected information, experts can identify subtle displacement patterns, evaluate structural behavior under different conditions, and make informed decisions regarding maintenance and repairs. The findings of this investigation clearly demonstrate the effectiveness of GNSS as a highly reliable and accurate tool for wharf monitoring. This advanced technology offers port authorities and engineers a powerful means to safeguard these critical maritime structures, ensuring their longevity and optimal performance in the face of challenging environmental and operational demands

Performance comparison of hyperbolic paraboloidal shell footing with its flat counterpart

While traditional flat footings are commonly used in construction, hyperbolic paraboloidal shell footings present potential benefits in load distribution and settlement reduction. The study encompasses two primary aspects: the design of both footing types in accordance with Indian standard practices, followed by a comparative analysis of their performance utilizing finite element methodology. Performance comparison is carried out concentrating on vertical settlement, stress distribution, and the amount of concrete required under centric gravity loads. The hyperbolic paraboloidal shell footing was modeled with curved surfaces, and the underlying soil was treated as nonlinear using the Mohr-Coulomb yield criteria. The amount of concrete required for a hyperbolic paraboloidal shell footing is significantly less, calculated to be 0.61 times that of a flat footing.

Influence of Aggregate Type and Size on Residual Mechanical Properties of Post-Heated Geopolymer Concrete: Experimental Study and Applications of Artificial Neural Networks

To mitigate environmental impacts from Portland cement (PC) production, the researcher’s efforts is introducing eco-friendly alternatives such as Geopolymer concrete (GPC). While GPC shows promise, further research is required to understand how fire or elevated temperatures affect GPC’s mechanical properties. This research investigates the effects of elevated temperatures (200℃, 400℃, 600℃, and 800℃) on the residual mechanical properties (compressive, flexural, splitting-tensile strengths, and modulus of elasticity) of ambient-cured fly-ash (FA)-based GPC compared to PC mixtures. The study examined various concrete types (GPC and PC), three coarse aggregate types (basalt, gravel, and crushed dolomite), and three crushed dolomite sizes (40 mm, 20 mm, and 14 mm). Additionally, Artificial Neural Network (ANN) models were developed to predict the residual compressive strength of both ambient-cured and heat-cured GPC after exposure to elevated temperatures. Results showed that basalt aggregate significantly enhanced the residual mechanical properties at 800 ℃, outperforming crushed dolomite and gravel in compressive, flexural, splitting-tensile strengths, and modulus of elasticity, with increases of (20%, 80%), (26%, 244%), (10%, 100%), and (14%, 140%), respectively. Moreover, the residual mechanical properties were found to be inversely proportion with max size of coarse aggregate. In addition, using ANN models proved its efficient in predicting the compressive strength for both ambient and heat-cured GPC with R² values of 0.94 and 0.887, respectively

Study on Mechanical and Tribological Characterization of Titanium Diboride (TiB2) Reinforced Al7075 Composites by Taguchi Technique

Three distinct titanium diboride (TiB2) weight percentages 3%, 6%, and 9% were incorporated into Al7075-TiB2 composites by stir casting. Al7075-TiB2 composites' wear and mechanical characteristics were evaluated. The microstructural investigation titanium diboride verified the stable interfacial bond between the matrix and reinforcing material as well as the uniform distribution of TiB2 particles. Superior mechanical properties were noted when comparing the 9% TiB2 composites to the 3 and 6% TiB2 composites. The tensile strength was increased by 11.94% for 9% TiB2 reinforced Al composites. Dry sliding wear was measured with pin-on-disc device. Measurements of the samples' wear loss as well as coefficient of friction (COF) showed that the cumulative wear loss for each composite varied linearly with load. As seen by the optical microscope, all specimens within the prescribed tension and sliding distance appear to have oxidative wear on the worn-out surfaces of the wear mechanism. Wear rate is most affected by weight percentage TiB2 (48.43%), followed by load (19.08%), according to the ANOVA. The wt. % of TiB2, at 37.68%, has the biggest effect on the COF, followed by sliding distance (20.54%). Taguchi technique validates the beneficial impact of weight percentage TiB2 on wear loss and COF. The composite reinforced with 9% TiB2 has the finest tribological and mechanical properties

Investigation of permanent deformation characterisation of different asphalt mixtures

Permanent deformation has emerged as a primary distress factor in asphalt pavements, largely driven by the recent rise in tyre pressures and axle loads. This deformation significantly impacts pavement performance, safety, and driving comfort, particularly when rutting depth exceeds a critical threshold. To address this issue, polymer-modified asphalt (PMA) mixtures have been widely adopted as an effective solution to resist permanent deformation (or rutting) under high temperatures. In this study, three different asphalt mixtures—one unmodified and two PMA mixtures—were selected for permanent deformation testing, which included the wheel tracking rutting test and the repeated load axial test under various conditions. The results were then compared, analysed, and discussed. The findings demonstrated a significant improvement in permanent deformation resistance in the PMA mixtures compared to conventional mixtures at both 45°C and 60°C. Furthermore, the results indicated that the use of PMA markedly enhanced both dynamic stability and strain rate, with the improvements becoming more pronounced at higher temperatures

Stochastic Stability Analysis of Columns with Randomly Elastic Joint Ends and Two-Dimensional Random Material Properties Using Monte Carlo Simulation

This paper develops a Monte Carlo simulation (MCs) to analyze the stochastic stability of a pin-ended column with elastic rotational springs at both ends. The randomness of input factors is considered, including the rotational stiffness of elastic couplings at both ends of the column as random variables following a normal distribution and the elastic modulus of the column as a two-dimensional (2D), stationary, homogeneous Gaussian random field. The spectral representation method is applied to represent the 2D random field and generate realizations of the elastic modulus. The influence of random factors such as the standard deviation of the 2D random field, the standard deviation of normal random variables, and correlation distance in each direction on the coefficient of variation (COV) of the critical load is analyzed in detail in this study. The results indicate a strong correlation between the COV of the critical load and both the standard deviation of the 2D random field and the correlation distance. The COV increases significantly with increasing standard deviation, particularly for larger correlation lengths. However, the influence of the variability of rotational stiffness is relatively minor, especially at larger correlation distances

Prediction of Compressive Strength by Considering Practical Consideration Non-destructive Test by Artificial Neural Network

Accurate assessment of concrete compressive strength is critical for evaluating structural performance. While nondestructive testing (NDT) methods, such as Schmidt rebound hammer tests, offer rapid and NDT gives result with reasonable accurate based on environmental factors such as temperature, humidity etc of site and condition in which test is performed.  Destructive testing (DT) methods, like core cutting, provide direct and accurate results. This study aimed to bridge the gap between these approaches by developing predictive models that correlate DT and NDT results. Experimental work involved 126 laboratory-prepared samples (grades M10–M40) with curing age of 14 day and 28 day and 231 field samples from a 20-year-old structure, tested using both methods. Total 357 no. of data samples were created with different mix proportion of design, curing ages and on-site environmental exposed concrete structure without unknown grade. Most of the researches were done while preparation of samples in the laboratory. For these purposes of taking mixing both variations such as control (Laboratory) and uncontrolled(on-site) samples  were to prepare as a practical condition for prediction. For generation of predict model 70% data was used with methods such as regression analysis and Cascade forward back propagation neural network (CFBPNN) were used for investigation. To validate the prediction 30% data was used which was not used in model generation. The prediction results show that the coefficients of determination (R2) of the Regression analysis and the CFBPNN prediction models for the test set of concrete compressive strength are 95% and 99% respectively ANN model founded to be more accurate as compare to regression analysis. The validation by R2 of the Regression analysis and the CFBPNN prediction model for the compressive strength for above dataset was 89.0% and 98%. Statistical metrics (MSE, RMSE, MAPE) further confirmed the neural network’s superior accuracy

Study on the Application of Simultaneous Localization and Mapping Solution for Surveying Tasks: A Case Study of RS10 3D Handheld SLAM

The article assesses the suitability of handheld SLAM for surveying tasks, focusing on visual observation and elevation accuracy from point cloud data. The study uses an RS10 handheld SLAM device, an integrated solution of GNSS technology, IMU sensors and three cameras to scan about one hectare using either real-time kinematic or post-processing kinematic working modes at approximately 4 Km/h speed. The elevations extracted from point cloud data are compared to those of 108 checking points measured by GNSS-RTK single base with 4G working mode at a close distance. Results showed the RS10's feasibility for surveying due to its accuracy and visual capabilities. Using different thresholds for assessment shows that 50 points (46.3%) deviate ≤ 0.010 m. 87 points (80.56%) deviate ≤ 0.020 m. 98 points (90.74%) deviate ≤ 0.030 m. 106 points (98.15%) deviate ≤ 0.040 m. Only points 85 (0.045 m) and 91 (0.052 m) deviate significantly. The standard deviation of 108 checking points is only 0.016 m. These results, excellent even without control points to adjust elevation elements, highlight the RS10's ability to provide detailed, feature-rich point cloud data along the scanning route

Sustainable Concrete Innovation: Crumb Rubber as a Partial Fine Aggregate Replacement

The exponential growth of the automobile industry contributes a significant accumulation of waste tires, presenting a substantial environmental challenge due to their non-biodegradable nature. This study investigates the potential of using crumb rubber produced from waste tires as a partial replacement for natural fine aggregates of concrete, aiming to enhance the material's properties while mitigating environmental impact. The mechanical and durability characteristics of concrete introducing 5%, 10%, and 15% crumb rubber were determined through various tests, including compressive strength, stress-strain behavior, and impact resistance. Results indicate that while there is a slight decrease in compressive strength with increased rubber content, the post-cracking behavior and impact resistance improve significantly, with energy absorption capacities increasing by 75%, 125%, and 158.4% for 5%, 10%, and 15% replacements, respectively. Additionally, durability tests reveal that rubberized concrete shows reduced chloride ion penetration and improved resistance to acid attack, with 30% reduction in chloride penetration at 15% rubber content. The findings suggest that incorporating crumb rubber can enhance the ductility and durability of concrete, making it a viable and sustainable alternative in construction, particularly in applications such as road pavements where impact resistance is crucial. This study contributes to the ongoing research on sustainable construction materials and emphasizes the importance of recycling waste materials to reduce environmental impact