Comparison of stabilised/solidified mixes of model drill cuttings based on the North Sea and Red Sea Areas

Following the global stringent legislations regulating the wastes generated from the drilling process of oil exploration and production activities, the management of hazardous drill cuttings has become one of the pressing needs confronting the petroleum industry. Most of the prevalent treatment techniques adopted by oil companies are extremely expensive and/or the treated product has to be landfilled without any potential end-use; thereby rendering these solutions unsustainable. The technique of stabilisation/solidification is being investigated in this research to treat drill cuttings prior to landfilling or for potential re-use in construction products. Two case studies were explored namely North Sea and Red Sea. Given the known difficulties with stabilising/solidifying oils and chlorides, this research made use of model drill cutting mixes based on typical drill cutting from the two case studies, which contained 4.2% and 10.95% average concentrations of hydrocarbons; and 2.03% and 2.13% of chlorides, by weight respectively. A number of different binders, including a range of conventional viz. Portland cement (PC) as well as less-conventional viz. zeolite, or waste binders viz. cement kiln dust (CKD), fly ash and compost were tested to assess their ability to treat the North Sea and Red Sea model drill cuttings. The dry binder content by weight was 10%, 20% and 30%. In addition, raw drill cuttings from one of the North Sea offshore rigs were stabilised/solidified using 30% PC. The characteristics of the final stabilised/solidified product were finally compared to those of thermally treated cuttings. The effectiveness of the treatment using the different binder systems was compared in the light of the aforementioned two contaminants only. A set of physical tests (unconfined compressive strength (UCS)), chemical tests (NRA leachability) and micro-structural examinations (using scanning electron microscopy (SEM), and X-ray diffraction (XRD)) were used to evaluate the relative performance of the different binder mixes in treating the drill cuttings. The results showed that the observed UCS covered a wide range of values indicating various feasible end-use scenarios for the treated cuttings within the construction industry. The teachability results showed the reduction of the model drill cuttings to a stable non-reactive hazardous waste, compliant with the UK acceptance criteria for non-hazardous landfills: (a) by most of the 30% and 20% binders for chloride concentrations, and (b) by the 20% and 30% of compost-PC and CKD-PC binders for the Red Sea cuttings. The 20% and 30% compost-PC and CKD-PC binders successfully reduced the leached oil concentration of the North Sea cuttings to inert levels. Copyright 2007, Society of Petroleum Engineers.

Embodied energy as an environmental impact indicator for basement wall construction

Attempts were made to quantify the environmental impacts of the basement walls of two commercial buildings in London. Four different retaining wall options were designed based on steel and concrete systems for each of the sites. It was considered that excavation would take place with the aid of a one or two anchors system. Evaluation of embodied energy (EE) and CO2 emissions for each of the wall designs and anchoring systems were compared. Results show that there are notable differences in EE between different wall designs. Using the averaged set of Embodied Energy Intensity (EEI) values, the use of recycled steel over virgin steel would reduce the EE of the wall significantly. The difference in anchor designs is relatively insignificant, and therefore the practicality of the design for the specific site should be the deciding factor for anchor types. Generally, the scale of environmental impacts due to constructions is large compared to other aspects in life as demonstrated with the comparisons to car emissions and household energy consumption. Copyright ASCE 2008.

Plasmonic band gaps and waveguide effects in carbon nanotube arrays based metamaterials.

Highly dense periodic arrays of multiwalled carbon nanotubes behave like low-density plasma of very heavy charged particles, acting as metamaterials. These arrays with nanoscale lattice constants can be designed to display extended plasmonic band gaps within the optical regime, encompassing the crucial optical windows (850 and 1550 nm) simultaneously. We demonstrate an interesting metamaterial waveguide effect displayed by these nanotube arrays containing line defects. The nanotube arrays with lattice constants of 400 nm and radius of 50 nm were studied. Reflection experiments conducted on the nanoscale structures were in agreement with numerical calculations.

Liquid crystal multi-mode lenses and axicons based on electronic phase shift control

We report on the principle of operation, construction and testing of a liquid crystal lens which is controlled by distributing voltages across the control electrodes, which are in turn controlled by adjusting the phase of the applied voltages. As well as (positive and negative) defocus, then lenses can be used to control tip/tilt, astigmatism, and to create variable axicons. © 2007 Optical Society of America.

Testing in harsh conditions: Tracking resources on construction sites with machine vision

When tracking resources in large-scale, congested, outdoor construction sites, the cost and time for purchasing, installing and maintaining the position sensors needed to track thousands of materials, and hundreds of equipment and personnel can be significant. To alleviate this problem a novel vision based tracking method that allows each sensor (camera) to monitor the position of multiple entities simultaneously has been proposed. This paper presents the full-scale validation experiments for this method. The validation included testing the method under harsh conditions at a variety of mega-project construction sites. The procedure for collecting data from the sites, the testing procedure, metrics, and results are reported. Full-scale validation demonstrates that the novel vision tracking provides a good solution to track different entities on a large, congested construction site.

Neuro-Fuzzy genetic system for selection of construction project managers

Choosing a project manager for a construction project—particularly, large projects—is a critical project decision. The selection process involves different criteria and should be in accordance with company policies and project specifications. Traditionally, potential candidates are interviewed and the most qualified are selected in compliance with company priorities and project conditions. Precise computing models that could take various candidates’ information into consideration and then pinpoint the most qualified person with a high degree of accuracy would be beneficial. On the basis of the opinions of experienced construction company managers, this paper, through presenting a fuzzy system, identifies the important criteria in selecting a project manager. The proposed fuzzy system is based on IF-THEN rules; a genetic algorithm improves the overall accuracy as well as the functions used by the fuzzy system to make initial estimates of the cluster centers for fuzzy c-means clustering. Moreover, a back-propagation neutral network method was used to train the system. The optimal measures of the inference parameters were identified by calculating the system’s output error and propagating this error within the system. After specifying the system parameters, the membership function parameters—which by means of clustering and projection were approximated—were tuned with the genetic algorithm. Results from this system in selecting project managers show its high capability in making high-quality personnel predictions

Machine vision based concrete surface quality assessment

Manually inspecting concrete surface defects (e.g., cracks and air pockets) is not always reliable. Also, it is labor-intensive. In order to overcome these limitations, automated inspection using image processing techniques was proposed. However, the current work can only detect defects in an image without the ability of evaluating them. This paper presents a novel approach for automatically assessing the impact of two common surface defects (i.e., air pockets and discoloration). These two defects are first located using the developed detection methods. Their attributes, such as the number of air pockets and the area of discoloration regions, are then retrieved to calculate defects’ visual impact ratios (VIRs). The appropriate threshold values for these VIRs are selected through a manual rating survey. This way, for a given concrete surface image, its quality in terms of air pockets and discoloration can be automatically measured by judging whether their VIRs are below the threshold values or not. The method presented in this paper was implemented in C++ and a database of concrete surface images was tested to validate its performance. Read More: http://ascelibrary.org/doi/abs/10.1061/%28ASCE%29CO.1943-7862.0000126?journalCode=jcemd4

Comparison of manual and user guided methodologies for the classification and retrieval of construction site images

The amount of original imaging information produced yearly during the last decade has experienced a tremendous growth in all industries due to the technological breakthroughs in digital imaging and electronic storage capabilities. This trend is affecting the construction industry as well, where digital cameras and image databases are gradually replacing traditional photography. Owners demand complete site photograph logs and engineers store thousands of images for each project to use in a number of construction management tasks like monitoring an activity's progress and keeping evidence of the "as built" in case any disputes arise. So far, retrieval methodologies are done manually with the user being responsible for imaging classification according to specific rules that serve a limited number of construction management tasks. New methods that, with the guidance of the user, can automatically classify and retrieve construction site images are being developed and promise to remove the heavy burden of manually indexing images. In this paper, both the existing methods and a novel image retrieval method developed by the authors for the classification and retrieval of construction site images are described and compared. Specifically a number of examples are deployed in order to present their advantages and limitations. The results from this comparison demonstrates that the content based image retrieval method developed by the authors can reduce the overall time spent for the classification and retrieval of construction images while providing the user with the flexibility to retrieve images according different classification schemes.

Shape Recognition of Linear Construction Entities from Construction Site Images

The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. This capability is a product of the technological breakthroughs in the area of Image Processing that has allowed for the development of a large number of digital imaging applications in all industries. In this paper, an automated and content based shape recognition model is presented. This model was devised to enhance the recognition capabilities of our existing material based image retrieval model. The shape recognition model is based on clustering techniques, and specifically those related with material and object segmentation. The model detects the borders of each previously detected material depicted in the image, examines its linearity (length/width ratio) and detects its orientation (horizontal/vertical). The results emonstrate the suitability of this model for construction site image retrieval purposes and reveal the capability of existing clustering technologies to accurately identify the shape of a wealth of materials from construction site images.

Correlating Multiple 2D Vision Trackers for 3D Object Tracking on Construction Sites

Tracking methods have the potential to retrieve the spatial location of project related entities such as personnel and equipment at construction sites, which can facilitate several construction management tasks. Existing tracking methods are mainly based on Radio Frequency (RF) technologies and thus require manual deployment of tags. On construction sites with numerous entities, tags installation, maintenance and decommissioning become an issue since it increases the cost and time needed to implement these tracking methods. To address these limitations, this paper proposes an alternate 3D tracking method based on vision. It operates by tracking the designated object in 2D video frames and correlating the tracking results from multiple pre-calibrated views using epipolar geometry. The methodology presented in this paper has been implemented and tested on videos taken in controlled experimental conditions. Results are compared with the actual 3D positions to validate its performance.

Innovative Stereo Vision-Based Approach to Generate Dense Depth Map of Transportation Infrastructure

Three-dimensional (3-D) spatial data of a transportation infrastructure contain useful information for civil engineering applications, including as-built documentation, on-site safety enhancements, and progress monitoring. Several techniques have been developed for acquiring 3-D point coordinates of infrastructure, such as laser scanning. Although the method yields accurate results, the high device costs and human effort required render the process infeasible for generic applications in the construction industry. A quick and reliable approach, which is based on the principles of stereo vision, is proposed for generating a depth map of an infrastructure. Initially, two images are captured by two similar stereo cameras at the scene of the infrastructure. A Harris feature detector is used to extract feature points from the first view, and an innovative adaptive window-matching technique is used to compute feature point correspondences in the second view. A robust algorithm computes the nonfeature point correspondences. Thus, the correspondences of all the points in the scene are obtained. After all correspondences have been obtained, the geometric principles of stereo vision are used to generate a dense depth map of the scene. The proposed algorithm has been tested on several data sets, and results illustrate its potential for stereo correspondence and depth map generation.

Initializing Vision Based Trackers Using Semantic Texton Forests

Vision based tracking can provide the spatial location of project related entities such as equipment, workers, and materials in a large-scale congested construction site. It tracks entities in a video stream by inferring their motion. To initiate the process, it is required to determine the pixel areas of the entities to be tracked in the following consecutive video frames. For the purpose of fully automating the process, this paper presents an automated way of initializing trackers using Semantic Texton Forests (STFs) method. STFs method performs simultaneously the segmentation of the image and the classification of the segments based on the low-level semantic information and the context information. In this paper, STFs method is tested in the case of wheel loaders recognition. In the experiments, wheel loaders are further divided into several parts such as wheels and body parts to help learn the context information. The results show 79% accuracy of recognizing the pixel areas of the wheel loader. These results signify that STFs method has the potential to automate the initialization process of vision based tracking.

Machine Vision-Based Infrastructure As-Built Documentation Using Edge Points

The existing machine vision-based 3D reconstruction software programs provide a promising low-cost and in some cases automatic solution for infrastructure as-built documentation. However in several steps of the reconstruction process, they only rely on detecting and matching corner-like features in multiple views of a scene. Therefore, in infrastructure scenes which include uniform materials and poorly textured surfaces, these programs fail with high probabilities due to lack of feature points. Moreover, except few programs that generate dense 3D models through significantly time-consuming algorithms, most of them only provide a sparse reconstruction which does not necessarily include required points such as corners or edges; hence these points have to be manually matched across different views that could make the process considerably laborious. To address these limitations, this paper presents a video-based as-built documentation method that automatically builds detailed 3D maps of a scene by aligning edge points between video frames. Compared to corner-like features, edge points are far more plentiful even in untextured scenes and often carry important semantic associations. The method has been tested for poorly textured infrastructure scenes and the results indicate that a combination of edge and corner-like features would allow dealing with a broader range of scenes.

Detection of Construction Workers in Video Frames for Automatic Initialization of Vision Trackers

Vision based tracking can provide the spatial location of construction entities such as equipment, workers, and materials in large scale, congested construction sites. It tracks entities in video streams by inferring their locations based on the entities’ visual features and motion histories. To initiate the process, it is necessary to determine the pixel areas corresponding to the construction entities to be tracked in the following consecutive video frames. In order to fully automate the process, an automated way of initialization is needed. This paper presents the method for construction worker detection which can automatically recognize and localize construction workers in video frames. The method first finds the foreground areas of moving objects using a background subtraction method. Within these foreground areas, construction workers are recognized based on the histogram of oriented gradients (HOG) and histogram of the HSV colors. HOG’s have proved to work effectively for detection of people, and the histogram of HSV colors helps differentiate between pedestrians and construction workers wearing safety vests. Preliminary experiments show that the proposed method has the potential to automate the initialization process of vision based tracking.