This article presents an application of an active all-optical photoacoustic sensing system with four elements for steel rebar corrosion monitoring. The sensor utilized a photoacoustic mechanism of gold nanocomposites to generate 8 MHz broadband ultrasound pulses in 0.4 mm compact space. A nanosecond 532 nm pulsed laser and 400 μm multimode fiber were employed to incite an ultrasound reaction. The fiber Bragg gratings were used as distributed ultrasound detectors. Accelerated corrosion testing was applied to four sections of a single steel rebar with four different corrosion degrees. Our results demonstrated that the mass loss of steel rebar displayed an exponential growth with ultrasound frequency shifts. The sensitivity of the sensing system was such that 0.175 MHz central frequency reduction corresponded to 0.02 g mass loss of steel rebar corrosion. It was proved that the all-optical photoacoustic sensing system can actively evaluate the corrosion of steel rebar via ultrasound spectrum. This multipoint all-optical photoacoustic method is promising for embedment into a concrete structure for distributed corrosion monitoring.

Alternative construction materials derived from agricultural waste, such as rice husk ash (RHA) and groundnut shell ash, have enhanced the sustainability and performance of concrete structures. The use of these ash materials, with their inherent pozzolanic and mineral admixture properties, has received much attention in the construction industry due to their potential benefits. In this experimental investigation, we assessed the attributes of cement paste incorporating RHA as a pozzolanic substitute. Various replacement ratios, ranging from 0% to 25% by weight of the binder, were examined at different water-to-binder ratios (0.40, 0.45, 0.50, and 0.55). Cementitious specimens were cast in 50mm x 50mm x 50mm cube molds, cured for seven days, and subjected to compressive strength testing. The findings highlight the impact of water-to-binder ratios and cement replacement levels on the compressive strength of cement paste. Higher water-to-binder ratios were associated with reduced compressive strength in the hardened paste. The optimal cement replacement levels were observed at 15% for the hardened blended paste specimens for all water-to-binder ratios. Two empirical regression models (polynomial and power) were employed to analyze the relationships between the paste’s replacement ratios, water-to-binder ratios, and compressive strength. The models revealed consistent findings, with the power model demonstrating an inverse relationship between compressive strength and water-to-binder ratios, while the polynomial model’s coefficients indicated a positive correlation between replacement ratios and compressive strength

The most basic problem facing man is shelter, apart from food. This problem is acute in Ghana and other developing countries due mainly to the high growth rate of the population, inflation, unemployment and poor economic resources. Today in Ghana both middle and low income earners can hardly afford their own houses. From one point of view one can see that some of the materials used in building are of good quality and can last. The study aimed to partially replace ordinary Portland cement (oPc) with burnt sawdust ash (BSDA) from different timber species (Wawa, Mansonia, Teak, Odum, Ceiba, Essah and Mahogany) in making interlocking laterite blocks by replacing 0–30 wt %. Mix proportion was 1:8 (cement + BSDA: laterite) with a 0.60 water-to-cement ratio. 528 specimens of size 185 mm × 220 mm × 120 mm were produced and cured at normal temperature and humidity under shady and sunny conditions for 7, 14, 21 and 28 days. Compressive and tensile strengths were subsequently evaluated. However, an initial strength increase was observed with curing time for specific wood species (Wawa, Mansonia, Teak, Odum, Essah, Ceiba, and Mahogany). Notably,Wawa, Mansonia and Odum ash exhibited superior strength performance at 10–20% replacement levels, potentially satisfying relevant standards for load-bearing wall construction. This research suggests that up to 10% replacement with Wawa, Mansonia or Odum BSDA presents a promising eco-friendly approach for partially substituting oPc in LIB production.

Shape memory alloys (SMAs), such as Ni–Ti, are valuable in structural and aerospace applications for their shape memory and superelastic properties. However, their performance can be degraded by corrosive and high-temperature environments. Although surface coatings such as thermal barrier coatings (TBCs) and anti-corrosion coatings (ACCs) are used for protection, their effects on the phase transformations of SMAs are not well understood. This study evaluates the impact of commercial polymer-based TBC and ACC coatings on the behavior of Ni–Ti SMAs through thermal, mechanical, and microstructural analysis. Results indicate that both coatings maintain the SMA’s phase transformation temperatures. However, the transformation enthalpy decreased for ACC-coated samples and increased for TBC-coated samples compared to uncoated ones. In thermal recovery tests, uncoated samples showed the highest shape recovery, followed by ACC-coated and TBC-coated samples, suggesting a compromise between protection and transformation efficiency. After 100 cycles, ACC coatings deteriorated significantly, while TBC coatings showed only minor cracking. Electrochemical tests confirmed the anti-corrosion effectiveness of the ACC, and thermal analysis verified the insulating property of the TBC. These findings demonstrate the trade-offs between coating functionality and SMA performance, offering practical insights for selecting coatings in SMA systems under thermal cycling conditions.

This paper presents a failure ratio machine learning estimation model for shear-critical reinforced concrete (RC) beams and slabs using multifractal features of their characteristic crack patterns to automate and provide well-informed decisions for RC damage assessment. Multifractal analysis was conducted on a database of 508 images, of which critical features were extracted from the singularity and generalized dimension spectra. These features are used as predictors for the failure ratio estimation model. The extreme gradient boosting algorithm yielded the best performance among the four machine learning models considered. The mean of the predicted-to-tested ratio for the developed model was 1.04 with a coefficient of variation of 0.27. Upon applying Shapley additive explanations, the fractal dimension, information dimension, correlation dimension and the area under the left branch of the singularity spectrum were the critical features influencing failure ratio estimation. The proposed model can be useful to RC building inspectors.