Carbon dioxide from earthworks: A bottom-up approach

Concerns over climate change mean engineers need to understand the greenhouse gas emissions associated with infrastructure projects. Standard coefficients are increasingly used to calculate the embodied emissions of construction materials, but these are not generally appropriate to inherently variable earthworks. This paper describes a new tool that takes a bottom-up approach to calculating carbon dioxide emissions from earthworks operations. In the case of bulk earthworks this is predominantly from the fuel used by machinery moving materials already on site. Typical earthworks solutions are explored along with the impact of using manufactured materials such as lime.

Carbon dioxide from earthworks: a bottom-up approach.

Concerns over climate change mean engineers need to understand the greenhouse gas emissions associated with infrastructure projects. Standard coefficients are increasingly used to calculate the embodied emissions of construction materials, but these are not generally appropriate to inherently variable earthworks. This paper describes a new tool that takes a bottom-up approach to calculating carbon dioxide emissions from earthworks operations. In the case of bulk earthworks this is predominantly from the fuel used by machinery moving materials already on site. Typical earthworks solutions are explored along with the impact of using manufactured materials such as lime.

Automated Detection of Exposed Reinforcement in Post-Earthquake Safety and Structural Evaluations

The world is at the threshold of emerging technologies, where new systems in construction, materials, and civil and architectural design are poised to make the world better from a structural and construction perspective. Exciting developments, that are too many to name individually, take place yearly, affecting design considerations and construction practices. This edited book brings together modern methods and advances in structural engineering and construction, fulfilling the mission of ISEC Conferences, which is to enhance communication and understanding between structural and construction engineers for successful design and construction of engineering projects. The articles in this book are those accepted for publication and presentation at the 6th International Structural Engineering and Construction Conference in Zurich. The 6th ISEC Conference in Zurich, Switzerland, follows the overwhelming reception and success of previous ISEC conference in Las Vegas, USA in 2009; Melbourne, Australia in 2007; Shunan, Japan in 2005; Rome, Italy in 2003; and Honolulu, USA in 2001. Many topics are covered in this book, ranging from legal affairs and contracting, to innovations and risk analysis in infrastructure projects, analysis and design of structural systems, materials, architecture, and construction. The articles here are a lasting testimony to the excellent research being undertaken around the world. These articles provide a platform for the exchange of ideas, research efforts and networking in the structural engineering and construction communities. We congratulate and thank the authors for these articles that were selected after intensive peer-review, and our gratitude extends to all reviewers and members of the International Technical Committee. It is their combined contributions that have made this book a reality.

Visual pattern recognition models for remote sensing of civil infrastructure

As-built models have been proven useful in many project-related applications, such as progress monitoring and quality control. However, they are not widely produced in most projects because a lot of effort is still necessary to manually convert remote sensing data from photogrammetry or laser scanning to an as-built model. In order to automate the generation of as-built models, the first and fundamental step is to automatically recognize infrastructure-related elements from the remote sensing data. This paper outlines a framework for creating visual pattern recognition models that can automate the recognition of infrastructure-related elements based on their visual features. The framework starts with identifying the visual characteristics of infrastructure element types and numerically representing them using image analysis tools. The derived representations, along with their relative topology, are then used to form element visual pattern recognition (VPR) models. So far, the VPR models of four infrastructure-related elements have been created using the framework. The high recognition performance of these models validates the effectiveness of the framework in recognizing infrastructure-related elements.

Visual Pattern Recognition Models for Remote Sensing of Civil Infrastructure

As-built models have been proven useful in many project-related applications, such as progress monitoring and quality control. However, they are not widely produced in most projects because a lot of effort is still necessary to manually convert remote sensing data from photogrammetry or laser scanning to an as-built model. In order to automate the generation of as-built models, the first and fundamental step is to automatically recognize infrastructure-related elements from the remote sensing data. This paper outlines a framework for creating visual pattern recognition models that can automate the recognition of infrastructure-related elements based on their visual features. The framework starts with identifying the visual characteristics of infrastructure element types and numerically representing them using image analysis tools. The derived representations, along with their relative topology, are then used to form element visual pattern recognition (VPR) models. So far, the VPR models of four infrastructure-related elements have been created using the framework. The high recognition performance of these models validates the effectiveness of the framework in recognizing infrastructure-related elements.

Testing of Depth-Encoded Hough Voting for Infrastructure Object Detection

The lack of viable methods to map and label existing infrastructure is one of the engineering grand challenges for the 21st century. For instance, over two thirds of the effort needed to geometrically model even simple infrastructure is spent on manually converting a cloud of points to a 3D model. The result is that few facilities today have a complete record of as-built information and that as-built models are not produced for the vast majority of new construction and retrofit projects. This leads to rework and design changes that can cost up to 10% of the installed costs. Automatically detecting building components could address this challenge. However, existing methods for detecting building components are not view and scale-invariant, or have only been validated in restricted scenarios that require a priori knowledge without considering occlusions. This leads to their constrained applicability in complex civil infrastructure scenes. In this paper, we test a pose-invariant method of labeling existing infrastructure. This method simultaneously detects objects and estimates their poses. It takes advantage of a recent novel formulation for object detection and customizes it to generic civil infrastructure scenes. Our preliminary experiments demonstrate that this method achieves convincing recognition results.