RESUMEN
Current research into the production of sustainable construction materials for retrofitting and strengthening historic structures has been rising, with geopolymer technology being seen as an advantageous alternative to traditional concrete. Fiber reinforcement using this novel cementitious material involves a low embodied carbon footprint while ensuring cohesiveness with local materials. This study aims to develop fly ash-based geopolymers reinforced with six different types of fibers: polyvinyl alcohol, polypropylene, chopped basalt, carbon fiber, and copper-coated stainless steel. The samples are produced by mixing the geopolymer mortar in random distribution and content. Twenty-eight geopolymer mixes are evaluated through compressive strength, split-tensile strength, and modulus of elasticity to determine the fiber mix with the best performance compared with pure geopolymer mortar as a control. Polyvinyl alcohol and copper-coated stainless-steel fiber samples had considerably high mechanical properties and fracture toughness under applied tensile loads. However, the polypropylene fiber source did not perform well and had lower mechanical properties. One-way ANOVA verifies these results. Based on these findings, polyvinyl alcohol and stainless-steel fibers are viable options for fiber reinforcement in historical structures, and further optimization and testing are recommended before application as a reinforcement material in historic structures.
RESUMEN
P-graph causality maps were recently proposed as a methodology for systematic analysis of intertwined causal chains forming network-like structures. This approach uses the bipartite representation of P-graph to distinguish system components ("objects" represented by O-type nodes) from the functions they perform ("mechanisms" represented by M-type nodes). The P-graph causality map methodology was originally applied for determining structurally feasible causal networks to enable a desirable outcome to be achieved. In this work, the P-graph causality map methodology is extended to the analysis of vicious networks (i.e., causal networks with adverse outcomes). The maximal structure generation algorithm is first used to assemble the problem elements into a complete causal network; the solution structure generation algorithm is then used to enumerate all structurally feasible causal networks. Such comprehensive analysis gives insights on how to deactivate vicious networks through the removal of keystone objects and mechanisms. The extended methodology is illustrated with an ex post analysis of the 1984 Bhopal industrial disaster. Prospects for other applications to sustainability issues are also discussed.
RESUMEN
The use of natural fibers in reinforced composites to produce eco-friendly materials is gaining more attention due to their attractive features such as low cost, low density and good mechanical properties, among others. This work thus investigates the potential of waste abaca (Manila hemp) fiber as reinforcing agent in an inorganic aluminosilicate material known as geopolymer. In this study, the waste fibers were subjected to different chemical treatments to modify the surface characteristics and to improve the adhesion with the fly ash-based geopolymer matrix. Definitive screening design of experiment was used to investigate the effect of successive chemical treatment of the fiber on its tensile strength considering the following factors: (1) NaOH pretreatment; (2) soaking time in aluminum salt solution; and (3) final pH of the slurry. The results show that the abaca fiber without alkali pretreatment, soaked for 12 h in Al2(SO4)3 solution and adjusted to pH 6 exhibited the highest tensile strength among the treated fibers. Test results confirmed that the chemical treatment removes the lignin, pectin and hemicellulose, as well as makes the surface rougher with the deposition of aluminum compounds. This improves the interfacial bonding between geopolymer matrix and the abaca fiber, while the geopolymer protects the treated fiber from thermal degradation.
RESUMEN
Pore structure, tortuosity and permeability are considered key properties of porous materials such as cement pastes to understand their long-term durability performance. Three-dimensional image analysis techniques were used in this study to quantify pore size, effective porosity, tortuosity, and permeability from the X-ray computed tomography (CT) images of deteriorated pastes that were subjected to accelerated leaching test. X-ray microtomography is a noninvasive three-dimensional (3D) imaging technique which has been recently gaining attention for material characterization. Coupled with 3D image analysis, the digitized pore can be extracted and computational simulation can be applied to the pore network to measure relevant microstructure and transport properties. At a spatial resolution of 0.50 µm, the effective porosity (ψe) was found to be in the range of 0.04 to 0.33. The characteristic pore size (d) using a local thickness algorithm was found to be in the range of 3 to 7 µm. The geometric tortuosity (τg) based on a 3D random walk simulation in the percolating pore space was found to be in the range of 2.00 to 7.45. The water permeability values (K) using US NIST Permeability Stokes Solver range from an order of magnitudes of 10-14 to 10-17 m². Indications suggest that as effective porosity increases, the geometric tortuosity increases and the permeability decreases. Correlation among these microstructure and transport parameters is also presented in this study.
RESUMEN
The Analytic Network Process (ANP) has been proposed to incorporate interdependence and feedback effect in the prioritization of remedial countermeasures using a hierarchical network decision model, but this approach seems to be incapable of capturing the vagueness and fuzziness during value judgment elicitation. The aim of this paper is to present an evaluation method using a fuzzy ANP (FANP) approach to address this shortcoming. Triangular fuzzy numbers (TFN) and their degree of fuzziness are used in the semantic scale as human judgment expressed in natural language is most often vague and fuzzy. The method employs the alpha-cuts, interval arithmetic and optimism index to transform the fuzzy comparative judgment matrix into set of crisp matrices, and then calculates the desired priorities using the eigenvector method. A numerical example, which was drawn from a real-life case study of an uncontrolled landfill in Japan, is presented to demonstrate the process. Results from the sensitivity analysis describe how the fuzziness in judgment could affect the solution robustness of the prioritization method. The proposed FANP approach therefore could effectively deal with the uncertain judgment inherent in the decision making process and derive the meaningful priorities explicitly from a complex decision structure in the evaluation of contaminated site remedial countermeasures.
