Automated Measurement of Heavy Equipment Greenhouse Gas Emission: The case of Road/Bridge Construction and Maintenance

Road/bridge construction and maintenance projects are major contributors to greenhouse gas (GHG) emissions such as carbon dioxide (CO2), mainly due to extensive use of heavy-duty diesel construction equipment and large-scale earthworks and earthmoving operations. Heavy equipment is a costly resource and its underutilization could result in significant budget overruns. A practical way to cut emissions is to reduce the time equipment spends doing non-value-added activities and/or idling. Recent research into the monitoring of automated equipment using sensors and Internet-of-Things (IoT) frameworks have leveraged machine learning algorithms to predict the behavior of tracked entities. In this project, end-to-end deep learning models were developed that can learn to accurately classify the activities of construction equipment based on vibration patterns picked up by accelerometers attached to the equipment. Data was collected from two types of real-world construction equipment, both used extensively in road/bridge construction and maintenance projects: excavators and vibratory rollers. The validation accuracies of the developed models were tested of three different deep learning models: a baseline convolutional neural network (CNN); a hybrid convolutional and recurrent long shortterm memory neural network (LSTM); and a temporal convolutional network (TCN). Results indicated that the TCN model had the best performance, the LSTM model had the second-best performance, and the CNN model had the worst performance. The TCN model had over 83% validation accuracy in recognizing activities. Using deep learning methodologies can significantly increase emission estimation accuracy for heavy equipment and help decision-makers to reliably evaluate the environmental impact of heavy civil and infrastructure projects. Reducing the carbon footprint and fuel use of heavy equipment in road/bridge projects have direct and indirect impacts on health and the economy. Public infrastructure projects can leverage the proposed system to reduce the environmental cost of infrastructure project

The impact of tool selection on back and wrist injury risk in tying steel reinforcement bars: a single case experiment

The paper explores the risk of work-related musculoskeletal injury in tying steel reinforcement bars. Three tools are compared to determine the extent to which ergonomic tools can reduce the risk of injury to the back and wrist in steel-tying. A whole body system of wearable sensors was used to measure biomechanical risk in tying. Three tools were assessed to determine their impact on the risk of work-related musculoskeletal injury when used at different heights. These were: a conventional pincer-cutting tool; a power-driven tying tool, and a long handled stapler tool. No tool was found to work best in all situations. The long handled stapler tool significantly reduced trunk inclination when used from ground to shoulder height, but produced higher trunk extension (backward bending) when used above shoulder height. The power tying tool did not reduce the need to bend when working at lower work heights. The power-tying tool produced significantly lower peak wrist flexion values compared to the conventional pincer-cutter tool at all work heights except overhead. The power tying tool involved significantly lower levels of wrist rotation than the conventional pincer-cutter tool at all work heights above knee level. Many assessments of ergonomic risk factors in construction rely on observational methods. The use of small, lightweight wearable sensors permits the objective measurement of biomechanical risk factors for work-related musculoskeletal injury, as well as providing objective performance data that can be used in the design and selection of task-specific tools. Our analysis of work by height also provides insight into the way in which risk factors and reduction opportunities afforded by different tools vary depending on the height at which work is to be performed

The impact of tool selection on back and wrist injury risk in tying steel reinforcement bars: a single case experiment

The paper explores the risk of work-related musculoskeletal injury in tying steel reinforcement bars. Three tools are compared to determine the extent to which ergonomic tools can reduce the risk of injury to the back and wrist in steel-tying. A whole body system of wearable sensors was used to measure biomechanical risk in tying. Three tools were assessed to determine their impact on the risk of work-related musculoskeletal injury when used at different heights. These were: a conventional pincer-cutting tool; a power-driven tying tool, and a long handled stapler tool. No tool was found to work best in all situations. The long handled stapler tool significantly reduced trunk inclination when used from ground to shoulder height, but produced higher trunk extension (backward bending) when used above shoulder height. The power tying tool did not reduce the need to bend when working at lower work heights. The power-tying tool produced significantly lower peak wrist flexion values compared to the conventional pincer-cutter tool at all work heights except overhead. The power tying tool involved significantly lower levels of wrist rotation than the conventional pincer-cutter tool at all work heights above knee level. Many assessments of ergonomic risk factors in construction rely on observational methods. The use of small, lightweight wearable sensors permits the objective measurement of biomechanical risk factors for work-related musculoskeletal injury, as well as providing objective performance data that can be used in the design and selection of task-specific tools. Our analysis of work by height also provides insight into the way in which risk factors and reduction opportunities afforded by different tools vary depending on the height at which work is to be performed

Structural health monitoring for wind turbine foundations

The construction of onshore wind turbines has rapidly been increasing as the UK attempts to meet its renewable energy targets. As the UK’s future energy depends more on wind farms, safety and security are critical to the success of this renewable energy source. Structural integrity of the tower and its components is a critical element of this security of supply. With the stochastic nature of the load regime a bespoke low cost structural health monitoring system is required to monitor integrity of the concrete foundation supporting the tower. This paper presents an assessment of ‘embedded can’ style foundation failure modes in large onshore wind turbines and proposes a novel condition based monitoring solution to aid in early warning of failure. The most common failure modes are discussed and a low-cost remote monitoring system is presented

Great East Japan Earthquake, JR East Mitigation Successes, and Lessons for California High-Speed Rail, MTI Report 12-37

California and Japan both experience frequent seismic activity, which is often damaging to infrastructure. Seismologists have developed systems for detecting and analyzing earthquakes in real-time. JR East has developed systems to mitigate the damage to their facilities and personnel, including an early earthquake detection system, retrofitting of existing facilities for seismic safety, development of more seismically resistant designs for new facilities, and earthquake response training and exercises for staff members. These systems demonstrated their value in the Great East Japan Earthquake of 2011 and have been further developed based on that experience. Researchers in California are developing an earthquake early warning system for the state, and the private sector has seismic sensors in place. These technologies could contribute to the safety of the California High-Speed Rail Authority’s developing system, which could emulate the best practices demonstrated in Japan in the construction of the Los Angeles-to-San Jose segment

Advancing Climate Change Research and Hydrocarbon Leak Detection : by Combining Dissolved Carbon Dioxide and Methane Measurements with ADCP Data

With the emergence of largescale, comprehensive environmental monitoring projects, there is an increased need to combine state-of-the art technologies to address complicated problems such as ocean acidifi cation and hydrocarbon leak detection

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Diverse perceptions of smart spaces

This is the era of smart technology and of ‘smart’ as a meme, so we have run three workshops to examine the ‘smart’ meme and the exploitation of smart environments. The literature relating to smart spaces focuses primarily on technologies and their capabilities. Our three workshops demonstrated that we require a stronger user focus if we are advantageously to exploit spaces ascribed as smart: we examined the concept of smartness from a variety of perspectives, in collaboration with a broad range of contributors. We have prepared this monograph mainly to report on the third workshop, held at Bournemouth University in April 2012, but do also consider the lessons learned from all three. We conclude with a roadmap for a fourth (and final) workshop, which is intended to emphasise the overarching importance of the humans using the spac