Realistic estimation of the uncertainty associated with the bearing capacity, which is often represented by the uncertainty of a model bias factor, is important to reliability-based design of pile foundations. Due to the existence of cross-site variability, the statistics of a model bias factor may vary from one site to another. Also, as the number of site-specific load test data is often very limited, it is difficult to obtain the site-specific statistics of the model bias factor. This paper aims to establish a Bayesian network based machine learning method to develop site-specific statistics of the model bias factor utilizing information from both the regional and site-specific load test data, through which the resistance factor for design of the pile foundation can be calibrated. The suggested method has been verified using a comprehensive load test database for design of driven piles in Shanghai, China. It is found that a few site-specific pile load test data can significantly reduce the uncertainty associated with the model bias factor and hence increase the cost-effectiveness of the pile design. The method suggested in this paper lays a sound foundation for site-specific reliability-based design of pile foundations, and provides useful insight into the planning of site-specific load tests for design of pile foundations.
This paper investigates the process of tail-grouting during tunnel excavation using the URUP (Ultra-rapid Underpass) method. A small-scale model test was conducted to investigate the grouting-induced influence on surrounding soils and the filling process of the grout mortar during URUP tunneling. Particular attention was paid to tunneling phases with negative and shallow overburdens. In addition, ground responses including surface settlement, grouting pressure and earth pressure around the tunnel were monitored in the experiment. Combined with the experimental observation, the grouting effect behind the tunnel linings under different overburdens was analyzed. According to the experiment results, the grouting pressure was not uniformly distributed within the tail-void, with the maximum magnitude monitored adjacent to the injection openings. The earth pressure was more sensitive to the grouting volume ratio than to the pause/restart of tail-grouting operation, and the surface settlement was positively correlated to the grouting-volume ratio (GVR) during tail-grouting. The influence zone of each opening was not uniform and symmetric within the tail-void. The experiment suggested a reasonable GVR range of 130–160% for the URUP tunneling with a shallow overburden. For negative overburden phases, a GVR smaller than 145% is suggested to avoid unexpected overflow to the ground surface during tail-grouting.
Today material is the driving force in architectural design processes run by Computational Design (CD). The architect may lead the design process and its outputs by analysing material type and properties, as well as constraints, at the beginning of the process. This article reviews the state of the art in Material-based Computational Design (MCD) and aims to analyse the role of materials in efficient and sustainable MCD processes. A set of critical projects developed over the past decade have been selected and grouped based on how material is incorporated into the process. In the process, three main categories are identified—namely, Material Performance, Informed Materials and Programming Materials. Based on predefined criteria on efficiency (E) and sustainability (S) in architectural design processes, the projects are analysed to calculate their E + S ratings. The analysis identifies two principal approaches implemented in MCD. One focuses on integrating material properties with other critical parameters—including form, performance and fabrication. The other concerns enhancing material properties by designing new materials. The analysis verifies that MCD generates both efficient and sustainable design solutions. By using CD in architectural design processes, existing materials can be re-interpreted and innovative materials can be produced to achieve new spatial experiences and meanings.
Microbially induced calcite precipitation (MICP) is a recently proposed method that is environmentally friendly and has considerable potential applications in artificial biotreated geomaterials. New artificial biotreated geomaterials are produced based on the MICP technology for different parent soils. The purpose of this study is to explore the strength-increase mechanism and microstructural characteristics of the biotreated geomaterial through a series of experiments. The results show that longer mineralization time results in higher-strength biotreated geomaterial. The strength growth rate rapidly increases in the beginning and remains stable afterwards. The calcium ion content significantly increases with the extended mineralization time. When standard sand was used as a parent soil, the calcium ion content increased to a factor of 39 after 7 days. The bacterial cells with attached calcium ions serve as the nucleus of crystallization and fill the pore space. When fine sand was used as a parent soil, the calcium ion content increased to only a factor of 7 after 7 days of mineralization. The nucleus of crystallization could not normally grow because of the limited pore space. The porosity and variation in porosity are clearly affected by the parent soil. Therefore, the strength of the biotreated geomaterial is affected by the parent soil properties, mineralization time, and granular material pore space. This paper provides a basis for theory and experiments for biotreated geomaterials in future engineering practice.
Ultra-low cycle fatigue (ULCF) damage is one of the main failure modes of steel structures when subjected to intense earthquake action, such as near-field action. However, existing ULCF evaluation methods are based on the plastic strain history of structures, which requires fine numerical simulation and causes high calculation cost. In order to improve and simplify the ULCF evaluation process for steel structures, a new damage index based on the structure deformation history was proposed in this paper, with the application of structure life curve and Miner’s rule. Two types of steel components, notched round steel bar and steel pier, were employed as the research objectives to verify the accuracy of proposed damage index. The predicted ULCF life was compared with the results of tests and finite element simulations, which showed that the application of damage index was of acceptable accuracy. Compared with the traditional plastic strain history-based ULCF evaluation methods, the advantage of proposed damage index is that ULCF life of a given steel structure can be determined quickly according to the loading condition once its life curve is realized, thus eliminating the cumbersome numerical simulation process.
In recent years, the composite materials have been very desirable by researchers for many engineering applications such as aviation and biomedical because of the tremendous characteristics of magnesium matrix metal composite. This current investigation aims to develop the AZ91/SiCp composites with various weight fractions (0, 2.5, 5 and 10 wt%) of silicon carbide particles via the stir casting method. The effect of SiC particles content on microstructure, mechanical and wear behaviour was investigated. The optical microscope, scanning electron microscopy and EDX analyses were utilized to detect the distribution of hard particles as well as the interface between the alloy and particles. Based on the findings, the homogeneous distribution of particles, refinement of grains in addition to good bonding between AZ91 alloy and particles have been achieved in produced composites. Therefore, the mechanical characteristics and wear performance are improved in composites compared with the unreinforced alloy. Moreover, these results suggest that for applications demanding high mechanical properties and wear resistance the AZ91/SiCp will be effective composites.