Search results

The purpose of this paper was to study the possibility of using smartphone roughness measurements for developing pavement roughness regression models as a function of pavement age, traffic loading and traffic volume variables. Also, the effects of patching and pavement distresses on pavement roughness were investigated. The work focused on establishing pavement roughness prediction models and applying these models to pavement management systems (PMS) to help decision-makers choose the best maintenance and rehabilitation (M&R) options by using cost-effective methods.

Signal processing techniques including filtering and processing techniques were used to obtain the International Roughness Index (IRI) from raw acceleration data collected from smartphone accelerometer sensors. The obtained IRI values were inputted as a dependent variable in analytical regression models as well as several independent variables with proper transformations.

According to the study results, several regression models were developed with a big variation in the coefficients of determination (R²). However, the best models included pavement age, accumulated traffic volume (∑TV) and construction quality factor (CQF) with R² equal to 0.63. It was also found that the effects of pavement distresses and patching was significant at a-level < 0.05. The patching effect on pavement roughness was found higher than the effect of other pavement distresses.

The presented results and methods in this paper could be used in the future predictions of pavement roughness and help the decision-makers to estimate M&R needs. The work focused on establishing IRI prediction models and applying these models to the PMS to help decision-makers choose the best M & R options.

To develop sound pavement roughness models, it is essential to collect roughness data using automated procedures. However, applying these procedures in developing countries faces several difficulties such as the high price and operation costs of roughness equipment and lack of technical experience. The advantage of using IRI values taken from smartphones is that the roughness evaluation survey may be expanded to cover the full road network at a cheaper cost than with automated instruments. Therefore, if the roughness survey covers more roads, the prediction model’s accuracy will be improved.

While the demand for weather‐based agricultural insurance in developed regions is limited, there exists significant potential for the use of weather indexes in developing areas. The purpose of this paper is to address the issue of historical data availability in designing actuarially sound weather‐based instruments.

A Bayesian rainfall model utilizing spatial kriging and Markov chain Monte Carlo techniques is proposed to estimate rainfall histories from observed historical data. An example drought insurance policy is presented where the fair rates are calculated using Monte Carlo methods and a historical analysis is carried out to assess potential policy performance.

The applicability of the estimation method is validated using a rich data set from Iowa. Results from the historical analysis indicate that the systemic nature of weather risk can vary greatly over time, even in the relatively homogenous region of Iowa.

The paper shows that while the kriging method may be more complex than competing models, it also provides a richer set of results. Furthermore, while the application is specific to forage production in Iowa, the rainfall model could be generalized to other regions by incorporating additional climatic factors.

This study investigates the impact and economic viability of energy-efficient building envelope and orientation for contributing net zero energy building (NZEB) and suggests optimum thermal insulation thickness, optimum wall thickness, appropriate orientation and glazing types of window in the contexts of unique Bangladeshi subtropical monsoon climate.

The whole study was conducted through energy simulation perspective of an existing office building using building information modeling (BIM) and building energy modeling (BEM) tools which are Autodesk Revit 2017, Autodesk Green Building Studio (GBS) and eQUEST. Numerous simulation patterns were created for energy simulation considering building envelope parameters and orientations. A comprehensive data analysis of simulation results was conducted to sort out efficient passive design strategies.

The optimum thermal mass and thermal insulation thickness are 6.5 and 0.5 inches, respectively, considering energy performance and economic viability. This study highly recommends that a building should be designed with a small window-to-wall ratio in the south and west face. The window should be constructed with double glazing Low-E materials to reduce solar heat gain. The studied building saves 9.14% annual energy consumption by incorporating the suggested passive design strategies of this study.

The output of this work can add some new energy-efficient design strategies to Bangladesh National Building Code (BNBC) because BNBC has not suggested any codes or regulations regarding energy-efficient passive design strategies. It will also be useful to designers of Bangladesh and other countries with similar subtropical climatic contexts which are located in Southeast Asia and Northern Hemisphere of Earth.

This paper not only draws conclusions from the available literature but also offers some new factors as well, which are not included in the existing literature. To be more precise, the purpose of this paper is to ascertain factors behind the clustering of the motorcycle industry, a low-tech and low investment industry. This paper weighs the government’s policies, role of factors of production, infrastructure, geography and other drivers for the subject industry and associated industries in the geographic location of Hyderabad.

For collection of data, a questionnaire was designed to survey the cluster (n=250) after reviewing the literature and conducting interviews of experts of the motorcycle manufacturing industry, i.e. owners, managers, auditors, suppliers, etc.; a component matrix was developed to reduce the dimension of factors and measure the correlation, which helped to weigh the influence of factors. A confirmatory factor analysis proposed four factors as the best fit.

The study conjectured a new viable factor for industrial clustering: “ethnic community,” as it acts as a catalyst to diffuse knowledge, experience and skills within the industrial cluster.

This research does not find the weightage of the factors for industrial clustering, i.e. it does not calculate the influence of factors behind the industrial clustering.

The above findings aim to stimulate policy makers and researchers alike to further pursue the line of inquiry developed in this paper.

A first-time confirmatory factor analysis is used to find the reasons of industrial clustering. Root mean square error of approximation is used to test the model fit. Most importantly, it is the research about an emerging industrial cluster.

The aim of this paper is to study and evaluate the situation of different affordable housing projects with an eye toward developing a new affordable and sustainable housing model in the hot-arid climate of Jordan. There is a clear interest in providing affordable housing, yet sustainable and environmental issues are highly marginalized. To bridge this gap, and to meet Jordanian housing needs while tackling environmental problems, this research has analyzed the environmental issues of selected housing projects in Jordan, aiming to determine the existing problems. In addition, it proposes a solution through a sustainable and affordable housing model that was analyzed and compared to the previously studied housing projects.

A cross-sectional design strategy was adopted and a mixed design method was used. Information related to the physical and operation characteristics of buildings was collected through the review of “as built” drawings and other relevant documents. Further information was obtained from field surveys and personal interviews with architects and decision-makers in the housing sector. Energy consumption patterns of these housing projects were analyzed using the DesignBuilder simulation program. Water efficiency was assessed using the BRE Code Water Calculator. Based on the previous analysis, a new housing model was developed that was evaluated in terms of energy and water consumption.

The analysis shows a significant difference among different housing projects in terms of energy cooling and heating loads in different climatic regions in Jordan. Energy analysis proved that the proposed model is energy efficient in different locations and it can save up to 50.4 per cent of annual energy usage in comparison with existing projects. In addition, it can save around 43 per cent of water consumption by using a number of modifications for saving water.

Most of the housing initiatives focus on providing affordable housing, yet sustainable and environmental issues are highly marginalized. This research will bridge the gap by reducing the operation cost of affordable housing through adapting and implementing sustainable measures of design and construction.

Using building information modelling (BIM) technology, a conventional structure in this study was converted into a green building to measure its energy usage and CO2 emissions.

Digital images of the existing building conditions were captured using unmanned aerial vehicle (UAV), and were fed into Meshroom to generate the building’s geometry for 3D parametric model development. The model for the existing conventional building was created and converted to an energy model and exported to gbXML in Autodesk Revit for a whole building analysis which was carried out in the Green Building Studio (GBS). In the GBS, the conventional building was retrofitted into a green building to explore their energy consumption and CO2 emission.

By comparing the green building model to the conventional building model, the research found that the green building model saved 25% more energy while emitting 46.8% less CO2.

The study concluded that green building reduces energy consumption, thereby reducing the emission of CO2 into the environment. It is recommended that buildings should be simulated at the design stage to know their energy consumption and carbon emission performance before construction.

Occupant satisfaction, operation cost and environmental safety are essential for sustainable or green buildings. Green buildings increase the standard of living and enhance indoor air quality.

This investigation aided in a pool of information on how to use BIM methodology to retrofit existing conventional buildings into green buildings, showing how green buildings save the environment as compared to conventional buildings.

AT Schiphol Airport the first Dutch airliner to be built since the war is nearing completion. The Royal Netherlands Aircraft Company Fokker, which was responsible for a large number of successful airliner designs in the period between the wars, has designed this latest aircraft as a medium size ‘Dakota replacement’. Called the Fokker F.27 Friendship, it carries 28–36 passengers and a liberal amount of luggage on stage lengths up to 1,000 miles. It is powered by two Rolls‐Royce Dart propeller turbine engines installed in the high‐aspect‐ratio high wing. The advanced design incorporates a number of interesting structural features which contribute to efficient production and economy of operation.

To proactively draw efficient maintenance plans, road agencies should be able to forecast main road distress parameters, such as cracking, rutting, deflection and International Roughness Index (IRI). Nonetheless, the behavior of those parameters throughout pavement life cycles is associated with high uncertainty, resulting from various interrelated factors that fluctuate over time. This study aims to propose the use of dynamic Bayesian belief networks for the development of time-series prediction models to probabilistically forecast road distress parameters.

While Bayesian belief network (BBN) has the merit of capturing uncertainty associated with variables in a domain, dynamic BBNs, in particular, are deemed ideal for forecasting road distress over time due to its Markovian and invariant transition probability properties. Four dynamic BBN models are developed to represent rutting, deflection, cracking and IRI, using pavement data collected from 32 major road sections in the United Arab Emirates between 2013 and 2019. Those models are based on several factors affecting pavement deterioration, which are classified into three categories traffic factors, environmental factors and road-specific factors.

The four developed performance prediction models achieved an overall precision and reliability rate of over 80%.

The proposed approach provides flexibility to illustrate road conditions under various scenarios, which is beneficial for pavement maintainers in obtaining a realistic representation of expected future road conditions, where maintenance efforts could be prioritized and optimized.