Performance evaluation of laterite soil embankment stabilized with bottom ash,coir fiber,and lime

In tropical regions,heavy rainfall induces erosion and shallow landslides on road embankments.Cement-based stabilization methods,common in these regions,contribute to climate change due to their high carbon footprint.This study explored the potential application of coir fiber-reinforced laterite soil-bottom ash mixtures as embankment materials in the tropics.The objective is to enhance engineered embankment slopes'erosion resistance and stability while offering reuse options for industrial byproducts.This study examined various mix designs for unconfined compressive strength(UCS)and permeability,utilizing 30%bottom ash(BA)and 1%coir fiber(CF)with varying sizes ranging from 10 to 40 mm,6%lime,and laterite soil(LS),followed by microstructural analyses.The results demonstrate that the compressive strength increases as the CF length increases to 25 mm.In contrast,permeability increases continuously with increasing CF length.Lime-treated mixtures exhibit superior short-and long-term strength and reduce permeability owing to the formation of cementitious materials,as confirmed by microstructural analyses.A lab-scale slope box was constructed to evaluate the surface erosion of the stabilized laterite soil embankment.Based on the rainfall simulation results,the LS-BA-CF mixtures show better resistance to erosion and deformation compared to untreated LS,especially when lime is added to the top layer.This study provides insights into a sustainable and cost-effective approach for slope stabilization using BA and CF,offering a promising solution for tropical regions susceptible to surface erosion and landslides.

Effect of cement types, mineral admixtures, and bottom ash on the curing sensitivity of concrete

The curing sensitivity of concrete with cement Types 1, 3, and 5 as well as multiple powders consisting of cement, fly ash, and limestone powder was studied. Bottom ash was also used in the study as an internal curing agent and a partial substi- tution of fine aggregate. The curing sensitivity index was calculated by considering the performances of compressive strength and carbonation depth. Specimens were subjected to two curing conditions： continuously water-cured and continuously air-cured. The results show that cement Type 3 has a lower curing sensitivity, while cement Type 5 increases the curing sensitivity. For the mixes without bottom ash, the use of fly ash increases the curing sensitivity, while limestone powder reduces the curing sen- sRivity of concrete. The use of bottom ash in concrete reduces the curing sensitivity, especially at a lower mass ratio of water to binder. Concrete with limestone powder, together with bottom ash, is least sensitive to curing. The curing sensitivity calculated from carbonation depth also has a similar tendency as that derived by considering compressive strength. From the test results of compressive strength and curing sensitivity, bottom ash has been proven to be an effective internal curing agent.

Automated extraction of expressway road surface from mobile laser scanning data

This paper presents a voxel-based region growing method for automatic road surface extraction from mobile laser scanning point clouds in an expressway environment.The proposed method has three major steps:constructing a voxel model;extracting the road surface points by employing the voxel-based segmentation algorithm;refining the road boundary using the curb-based segmentation algorithm.To evaluate the accuracy of the proposed method,the two-point cloud datasets of two typical test sites in an expressway environment consisting of flat and bumpy surfaces with a high slope were used.The proposed algorithm extracted the road surface successfully with high accuracy.There was an average recall of 99.5%,the precision was 96.3%,and the F1 score was 97.9%.From the extracted road surface,a framework for the estimation of road roughness was proposed.Good agreement was achieved when comparing the results of the road roughness map with the visual image,indicating the feasibility and effectiveness of the proposed framework.

Bayesian network revealing pathways to workplace innovation and career satisfaction in the public service

This paper examined the innovation process in the Australian Public Service(APS)using a Bayesian network(BN)founded on an empirically derived structural equation model.The focus of the BN was to examine the impact of leadership style and organisational culture on workplace innovation and career satisfaction in the APS.Using scenario analysis,the best combination of managerial actions for enhancing APS career satisfaction was determined.The results emphasise the benefit of encouraging management to adopt a transformational leadership style and instilling innovative culture in their organisation.In addition,innovative culture was a key driver of workplace innovation,which served to improve the career satisfaction of APS employees.Implications are discussed to propose practical strategies for organisations wish to encourage innovation among employees.