The transportation infrastructure, including low-volume roads in some regions, needs to be constructed on weak ground, implying the necessity of soil stabilization. Untreated and cement-treated lateritic soil for low-volume road suitability were studied based on Malaysian standards. A series of unconfined compressive strength (UCS) tests was performed for four cement doses (3%, 6%, 9%, 12%) for different curing times. According to Malaysian standards, the study suggested 6% cement and 7 days curing time as the optimum cement dosage and curing time, respectively, based on their 0.8 MPa UCS values. The durability test indicated that the specimens treated with 3% cement collapsed directly upon soaking in water. Although the UCS of 6% cement-treated specimens decreased against wetting–drying (WD) cycles, the minimum threshold based on Malaysian standards was still maintained against 15 WD cycles. On the contrary, the durability of specimens treated with 9% and 12% cement represented a UCS increase against WD cycles. FESEM results indicated the formation of calcium aluminate hydrate (CAH), calcium silicate hydrate (CSH), and calcium aluminosilicate hydrate (CASH) as well as shrinking of pore size when untreated soil was mixed with cement. The formation of gels (CAH, CSH, CASH) and decreasing pore size could be clarified by EDX results in which the increase in cement content increased calcium.

In this research, the novel metaheuristic algorithm Harris hawks optimization (HHO) is applied to landslide susceptibility analysis in Western Iran. To this end, the HHO is synthesized with an artificial neural network (ANN) to optimize its performance. A spatial database comprising 208 historical landslides, as well as 14 landslide conditioning factors—elevation, slope aspect, plan curvature, profile curvature, soil type, lithology, distance to the river, distance to the road, distance to the fault, land cover, slope degree, stream power index (SPI), topographic wetness index (TWI), and rainfall—is prepared to develop the ANN and HHO–ANN predictive tools. Mean square error and mean absolute error criteria are defined to measure the performance error of the models, and area under the receiving operating characteristic curve (AUROC) is used to evaluate the accuracy of the generated susceptibility maps. The findings showed that the HHO algorithm effectively improved the performance of ANN in both recognizing (AUROC_ANN = 0.731 and AUROC_HHO–ANN = 0.777) and predicting (AUROC_ANN = 0.720 and AUROC_HHO–ANN = 0.773) the landslide pattern.

Research on the utilization of recycled concrete aggregate (RCA) in civil engineering applications is gaining popularity worldwide due to the increased efforts to promote preservation of the environment and sustainable development. Recycled concrete aggregate concrete is, however, presently still limited to nonstructural applications. Recycled concrete aggregate concrete can still be considered as a rigid pavement material when its flexural strength is improved adequately to sustain future traffic loads. In this study, polyvinyl alcohol (PVA), a water-soluble polymer, was used to improve the flexural strength of RCA concrete. The influence of PVA-to-cement ($p/c$ ) and water-to-cement ($w/c$ ) ratios on the compressive and flexural strengths of RCA-PVA concrete was investigated via scanning electron microscopy (SEM) and X-ray diffraction (XRD). Polyvinyl alcohol films were found to retard the hydration process, resulting in the delay in initial and final setting times of cement-PVA paste, therefore reducing the compressive strength of RCA-PVA concrete. For all the $w/c$ ratios tested, the flexural strength increased with increasing $p/c$ ratios up to an optimum $p/c$ ratio that provided the highest flexural strength, followed by a subsequent decrease beyond this peak value. The optimum $p/c$ ratio tended to increase with an increase in $w/c$ ratio, being 0.5, 0.5, 1.0, and 1.5 for $w/c$ ratios of 0.3, 0.4, 0.5, and 0.6, respectively. Based on the requirements of the Department of Highways, Thailand, the $p/c$ $\le 1$ at $w/c$ $\le 0.5$ was found to be suitable for developing RCA-PVA concrete for rigid pavements. The outcome of this research confirms the viability of using PVA to improve the flexural strength of RCA concrete for usage as a sustainable rigid pavement material.

This article reports on a series of small-scale, plane strain, 1 g physical model tests designed to investigate the bearing capacity and failure mechanics of end-bearing soil-cement columns formed via Deep Mixing (DM). Pre-formed soil-cement columns, 24 mm in diameter and 200 mm in length, were installed in a soft clay bed using a replacement method; the columns represented improvement area ratios, a_p, of 17%, 26%, and 35% beneath a rigid foundation of width 100 mm. Particle Image Velocimetry (PIV) was implemented in conjunction with close-range photogrammetry in order to track soil displacement during loading, from which the failure mechanisms were derived. Bearing capacity performance was verified using Ultimate Limit State numerical analysis, with the results comparing favorably to the analytical static and kinematic solutions proposed by previous researchers. A new equation for bearing capacity was derived from this numerical analysis based on the improvement area ratio and cohesion ratio of the soil column and ground model.