Search results

Examines a research program into the use of automation and robotics in masonry building. The four main objectives of the project were to establish the requirements for the application of robotics in masonry construction; construct a prototype robot; develop the operation software system; and evaluate suitable blocks for construction using robots. Describes the construction of the prototype robot cell and the research methods used and concludes that combining the research findings with work elsewhere it should be possible to achieve a commercially viable robotic solution for masonry and similar tasks on the construction site.

Examines a prototype robot which is being developed to carry out non‐destructive inspection maintenance and repair [IMR] of buildings and structures. Describes some of the areas where IMR is crucial i.e. bridges, tall buildings and petro‐chemical storage tanks and the variety of inspection tasks a robot would be required to carry out. Looks at the essential technologies to be considered for a successful inspection robot and outlines the areas of access and mobility, robot construction, control strategy and the external sensing systems it would need. Concludes that this prototype is capable of delivering single NDT packages and that in the future it is intended that the robot will be able to exchange its own NDT packages. Expresses some reservation about whether a free climbing version would be viable.

The purpose of this paper is to investigate the performance of new and innovative crystallising materials, so-called moisture blockers, in protecting masonry structures from water ingress.

Two masonry wells were constructed: one with lime mortar and the other with cement-based mortar in order to hold water inside, and then a moisture blocking product was applied at dry and wet conditions to the negative hydrostatic pressure side. The moisture levels of both, the surfaces and the substrate, were then observed for 14 days.

Results demonstrated that moisture blocking materials are effective methods in reducing the levels of surface moisture for bricks, mortar-brick interface and mortar.

Moisture blockers use the available water in the masonry to block the passage of water to the surface of the masonry, filling pores, cracks and spaces at the interface between mortar and bricks. This approach will deliver a wider understanding of how water-based moisture blockers work and the scenarios in which they are best applied. The pursuit of possible environmentally friendly and sustainable materials for use in the construction industry is the key driver of this research.

Concrete decay has become a major ongoing problem for the developed world, affecting all manner of structures. The purpose of the reported research is thus to advance the prospects for the realisation of high capacity, robotic repair systems through sensor technology. Here, the particular target is the removal of defective concrete by the hydro‐erosion method. The main advantages of the method are that it is kind to the structure while having the potential to produce high definition excavations. Sensing has been investigated for both prediction of the hydro‐erosion task and real‐time process feedback. The latter is complicated by the extremely destructive hydro‐erosion environment, which precludes the use of conventional sensing probes. For this, vibration and process noise have been investigated to determine if diagnostic characteristics are detectable. To support the task, a predictive basis has been developed using non‐destructive testing (NDT) sensors within a data fusion model. Covermeter, rebound hammer, impact echo and surface dampness NDT data are fed into this. Progress is reported on this part of the ongoing research.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.

Photography is used in research because of its appeal for communicating, expressing feelings, sharing experiences, raising new awareness of participants and potential audiences, clarifying social issues, and framing plans for action. Taking and sharing photos has become easier particularly because of ready access to devices with cameras. Yet, using photographs in research can undermine anonymity and confidentiality (Noland, 2006), and unanticipated unauthorised dissemination of digital images raises ethical concerns for researchers using photography in their research methods (Brigham, Baillie Abidi, & Calatayud, 2018). In this chapter, the authors discuss the participatory photography method and provide practical suggestions for carrying out ethical research using participatory photography. The authors highlight the cultural, social, and contextual situatedness of ethics by drawing on our own research project with youth with refugee experience.

Forecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present study, the authors assess the forecast problem for the weekly wholesale price index of yellow corn in China during January 1, 2010–January 10, 2020 period.

The authors employ the nonlinear auto-regressive neural network as the forecast tool and evaluate forecast performance of different model settings over algorithms, delays, hidden neurons and data splitting ratios in arriving at the final model.

The final model is relatively simple and leads to accurate and stable results. Particularly, it generates relative root mean square errors of 1.05%, 1.08% and 1.03% for training, validation and testing, respectively.

Through the analysis, the study shows usefulness of the neural network technique for commodity price forecasts. The results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.

For a wide range of market actors, including policymakers, forecasting changes in commodity prices is crucial. As one of essential edible oil, peanut oil’s price swings are certainly important to predict. In this paper, the weekly wholesale price index for the period of January 1, 2010 to January 10, 2020 is used to address this specific forecasting challenge for the Chinese market.

The nonlinear auto-regressive neural network (NAR-NN) model is the forecasting method used. Forecasting performance based on various settings, such as training techniques, delay counts, hidden neuron counts and data segmentation ratios, are assessed to build the final specification.

With training, validation and testing root mean square errors of 5.89, 4.96 and 5.57, respectively, the final model produces reliable and accurate forecasts. Here, this paper demonstrates the applicability of the NAR-NN approach for commodity price predictions.

On the one hand, the findings may be used as independent technical price movement predictions. Conversely, they may be included in forecast combinations with forecasts derived from other models to form viewpoints of commodity price patterns for policy research.