Preliminary Analysis Framework for State Sustainable Transportation system

Sustainable practices have become the cornerstone of the transportation sector, and widely adopted by many states' transportation agencies. The nerve center of the economic development today circles around resource utilization and energy use. The transportation sector is the bloodline of the U.S economy and sustainability of this sector affects the growth of the economy. Even though sustainable practices have now become the edifice of transportation sectors, the adoption of such practices cannot be quick enough to overcome the ever-increasing demand of resources from the global population. Benchmarking sustainability is the most appropriate method to determine the sustainability of transportation practices. There are numerous rating and benchmarking systems, and most of them follow similar approach and format that outline the sustainability factors (namely, energy, water, land use, air quality, pollution etc.). Such approaches and formats can be found on many sustainable standards and tools such as the Leadership for Energy and Environmental Design (LEED). The purpose of this research is to develop a framework that includes an alternative approach to benchmark the sustainable performances of state transportation systems. The framework focuses on measuring the actual sustainability rather than to develop standard compliance approach similar to LEED rating system. It also focuses on utilizing modified/adjusted quantitative data to determine the sustainability of transportation practices. Such an approach would allow transportation agencies and states to compare and compete with one another

Wildfire Predictions: Determining Reliable Models using Fused Dataset

Wildfires are a major environmental hazard that causes fatalities greater than structural fire and other disasters Computerized models have increased the possibilities of predictions that enhanced the firefighting capabilities in U S While predictive models are faster and accurate it is still important to identify the right model for the data type analyzed The paper aims at understanding the reliability of three predictive methods using fused dataset Performances of these methods Support Vector Machine K-Nearest Neighbors and decision tree models are evaluated using binary and multiclass classifications that predict wildfire occurrence and its severity Data extracted from meteorological database and U S fire database are utilized to understand the accuracy of these models that enhances the discussion on using right model for dataset based on their size The findings of the paper include SVM as the best optimum models for binary and multiclass classifications on the selected fused datase

Learning Energy Consumption and Demand Models through Data Mining for Reverse Engineering

The estimation of energy demand (by power plants) has traditionally relied on historical energy use data for the region(s) that a plant produces for. Regression analysis, artificial neural network and Bayesian theory are the most common approaches for analysing these data. Such data and techniques do not generate reliable results. Consequently, excess energy has to be generated to prevent blackout; causes for energy surge are not easily determined; and potential energy use reduction from energy efficiency solutions is usually not translated into actual energy use reduction. The paper highlights the weaknesses of traditional techniques, and lays out a framework to improve the prediction of energy demand by combining energy use models of equipment, physical systems and buildings, with the proposed data mining algorithms for reverse engineering. The research team first analyses data samples from large complex energy data, and then, presents a set of computationally efficient data mining algorithms for reverse engineering. In order to develop a structural system model for reverse engineering, two focus groups are developed that has direct relation with cause and effect variables. The research findings of this paper includes testing out different sets of reverse engineering algorithms, understand their output patterns and modify algorithms to elevate accuracy of the outputs

Understanding Infrastructure Resiliency in Chennai, India Using Twitter’s Geotags and Texts: A Preliminary Study

Geotagging is the process of labeling data and information with geographical identification metadata, and text mining refers to the process of deriving information from text through data analytics. Geotagging and text mining are used to mine rich sources of social media data, such as video, website, text, and Quick Response (QR) code. They have been frequently used to model consumer behaviors and market trends. This study uses both techniques to understand the resilience of infrastructure in Chennai, India using data mined from the 2015 flood. This paper presents a conceptual study on the potential use of social media (Twitter in this case) to better understand infrastructure resiliency. Using feature-extraction techniques, the research team extracted Twitter data from tweets generated by the Chennai population during the flood. First, this study shows that these techniques are useful in identifying locations, defects, and failure intensities of infrastructure using the location metadata from geotags, words containing the locations, and the frequencies of tweets from each location. However, more efforts are needed to better utilize the texts generated from the tweets, including a better understanding of the cultural contexts of the words used in the tweets, the contexts of the words used to describe the incidents, and the least frequently used words. Keywords: Social media, Flooding, Engineering desig

Analyzing Arizona OSHA Injury Reports Using Unsupervised Machine Learning

As the construction continue to be a leading industry in the number of injuries and fatalities annually, several organizations and agencies are working avidly to ensure the number of injuries and fatalities is minimized. The Occupational Safety and Health Administration (OSHA) is one such effort to assure safe and healthful working conditions for working men and women by setting and enforcing standards and by providing training, outreach, education and assistance. Given the large databases of OSHA historical events and reports, a manual analysis of the fatality and catastrophe investigations content is a time consuming and expensive process. This paper aims to evaluate the strength of unsupervised machine learning and Natural Language Processing (NLP) in supporting safety inspections and reorganizing accidents database on a state level. After collecting construction accident reports from the OSHA Arizona office, the methodology consists of preprocessing the accident reports and weighting terms in order to apply a data-driven unsupervised K-Means-based clustering approach. The proposed method classifies the collected reports in four clusters, each reporting a type of accident. The results show the construction accidents in the state of Arizona to be caused by falls (42.9%), struck by objects (34.3%), electrocutions (12.5%), and trenches collapse (10.3%). The findings of this research empower state and local agencies with a customized presentation of the accidents fitting their regulations and weather conditions. What is applicable to one climate might not be suitable for another; therefore, such rearrangement of the accidents database on a state based level is a necessary prerequisite to enhance the local safety applications and standards

Impact of sustainability on business performance and strategy for commercial building contractors

Purpose: The purpose of this paper is to understand the corporate sustainability culture of contracting firms, particularly in the Midwest. Many of the local firms operate nationally. The local corporate culture influences their regional offices. Other than convenience, the firms are selected from Midwest since their headquarters are situated in Midwest region and comprises of more number of employees than their other regional offices. This helped the research to approach more contractors for better survey and interview responses. The paper documents the study on existing management and construction practices these contractors adopt on sustainability and examines how their sustainability efforts influence the firms\u27 performances. Design/methodology/approach: This research utilized surveys and interviews as the primary means of data collection. The data were collected from survey and interviews with selected companies operating in the Midwest region of the USA. One of the companies also operates offices across the country. Data from the interviews and surveys were analyzed using statistical analysis system software application. χ2 analysis, particularly the frequency procedures using the Cochran-Mantel-Haenszel (CMH) method was the primary analysis method used to study the relationships between different factors. The CMH method compares the association between and within two groups and permits adjustments of the control variables. Findings: The findings of this paper include the results from various Midwest commercial building contractors. The results on different aspects of sustainable practices and their success rates among the contractors are determined and discussed, and future scope of improvements are mentioned at the conclusion of this paper. Research limitations/implications: In summary, sustainable business practices are beneficial to society and favorable for the construction business. Embracing sustainable business practices has a positive impact on firm strategic performance for commercial building contractors through employee satisfaction, project opportunities, and market advantage. Sustainable business practices extend into the lives of individuals involved which exceedingly impacts society. The construction industry has advanced sustainability efforts, but there is a long way to go on the journey to being better stewards of the environment and resources. Originality/value: A rival theory became apparent during the investigation that a new building consolidating all local company employees could have an impact on firm strategic performance. Third, this paper is confounded by a great recession that made project opportunities and revenue considerations analysis problematic. Some information regarding these aspects were helpful; however, expanding this paper during a more stable and typical economic period could provide additional insight

Applications of Clustering and Isolation Forest Techniques in Real-Time Building Energy-Consumption Data: Application to LEED Certified Buildings

Buildings are the largest consumer of energy in the United States from various sectors that includes transportation, industry, commercial, and residential buildings. Leadership in Energy and Environmental Design (LEED) certification program, home energy rating system (HERS), and American Society of Heating, Refrigerating and Air-conditioning Engineers (ASHRAE) standards are developed to improve the energy efficiency of the commercial and residential buildings. However, these programs, codes, and standards are used before or during the design and construction phases. For this reason, it is challenging to track whether buildings still could be energy efficient post construction. The primary purpose of this study was to detect the anomalies from the energy consumption dataset of LEED institutional buildings. The anomalies are identified using two different data mining techniques, which are clustering, and isolation Forest (iForest). This paper demonstrates an integrated data mining approach that helps in evaluating LEED energy and atmosphere (EA) credits after construction

Analyzing the Impact of Outside Temperature on Energy Consumption and Production Patterns in High-Performance Research Buildings in Arizona

The intimate relationship between energy consumption and climate change demands attention. More energy will be needed to run cooling systems if the annual global temperature continues to rise. The urban heat island would also increase the demand for cooling. As global energy demand continues to grow, the utility sector would face a continuous increase in energy demand. Studies in several countries have shown mostly nonlinear relationships between outside ambient temperature and electricity consumption, whereas other studies have suggested the absence of such relationships among high-performance buildings. However, these studies were based on aggregate data from entire cities and/or countries (indirect relationships) and were not based on real-time data (direct relationships). This study uses continuous real-time data from four high-performance research buildings and presents the results from a set of correlations and regression analyses between several variables, i.e., outside temperature, heat index, electricity consumption, and the production of solar energy. The authors found no relationship between electricity use and outdoor temperature, and between electricity use and heat index. Conversely, the efficiency of the production of solar energy was affected negatively by higher outdoor temperatures