Indigenous construction of fishing boats at Versova village in Maharashtra State

Versova, one of the 23 fishing villages in the district of Greater Bombay, is a major fishing centre. During the last three decades mechanisation of fishing boats received a tremendous boost in the state specially in and around Bombay resulting in higher income and gainful employment to fishermen. Indigenous construction of fishing boats at Versova contributes to the marine fisheries development. Inspite of certain constraints in construction activities, training of artisans in boat construction would ultimately help in the economic growth of the village.

Engineering development of the Lancaster flexible bag.

Describes progress in the last 12 months which has established bag designs and fabrication techniques, giving greater confidence in the life and cost of these components. A quarter scale bag is under construction. Extensive tank testing has also established life time bending moment and mooring load envelopes, enabling hull and mooring design to proceed. A computer simulation programme has been used to check tank model results and to establish turbine and generator operating conditions. This has allowed generation and transmission component design to proceed, and suggests a high operating efficiency can be maintained with a simple control regime. Simple solutions in minor areas such as valve design and damage stability control add to the picture of steady progress in establishing the Lancaster Flexible Bag 's feasibility.

Widening engineering horizons: Addressing the complexity of sustainable development

The complex, fragmented and diverse aspects of a sustainable development perspective are translated into an eight-point framework that defines a problem boundary larger than that traditionally adopted by civil engineers. This leads to practical questions intended to inform engineers who ask 'am I being sustainable?' during project implementation. The value of the questions is tested against a case history of a wastewater treatment project. This demonstrates the relevance of the questions to successive project delivery phases of defining the problem, choosing a solution and implementing that solution through design, construction and operation. The case history highlights that answers to several of the additional questions raised by considering this wider problem space are currently buried within government and clients' policies, regulations and standard practice; these answers may not be accessible to the professional engineer.

Geotechnical observations of construction OF A large shallow SCL tunnel in soft ground

The Fort Canning Tunnel is the first road tunnel in Singapore to be built using the sprayed concrete lining (SCL) method. The major technical challenge of this was to construct a 15m wide tunnel by mining in soft ground under a shallow overburden of 3m to 9m. This paper describes the geotechnical investigations and monitoring controls for the safe and progressive execution of the works, such as soil investigations, trial forepoling works, surface settlement monitoring, tunnel settlement monitoring, face movement monitoring, and the observational approach to construction. The monitored field data showed the volume loss to range from 0.4% to 2.1%, and the observed surface settlement trough was found to agree well with the theoretical Gaussian trough. Other observations made include substantial surface settlements induced by the stress relief at and ahead of the tunnel face in spite of the forepoling umbrella, and the higher volume losses associated with higher overburden. Tunnel face movements were observed during installation of forepoling. These observations are of interest to engineers planning future SCL tunnels in similar conditions.

Automated Computation of the Fundamental Matrix for Vision-Based Construction Site Applications

Estimating the fundamental matrix (F), to determine the epipolar geometry between a pair of images or video frames, is a basic step for a wide variety of vision-based functions used in construction operations, such as camera-pair calibration, automatic progress monitoring, and 3D reconstruction. Currently, robust methods (e.g., SIFT + normalized eight-point algorithm + RANSAC) are widely used in the construction community for this purpose. Although they can provide acceptable accuracy, the significant amount of required computational time impedes their adoption in real-time applications, especially video data analysis with many frames per second. Aiming to overcome this limitation, this paper presents and evaluates the accuracy of a solution to find F by combining the use of two speedy and consistent methods: SURF for the selection of a robust set of point correspondences and the normalized eight-point algorithm. This solution is tested extensively on construction site image pairs including changes in viewpoint, scale, illumination, rotation, and moving objects. The results demonstrate that this method can be used for real-time applications (5 image pairs per second with the resolution of 640 × 480) involving scenes of the built environment.

Construction site image retrieval based on material cluster recognition

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 construction site image retrieval method is presented. This method is based on image retrieval techniques, and specifically those related with material and object identification and matches known material samples with material clusters within the image content. The results demonstrate the suitability of this method for construction site image retrieval purposes and reveal the capability of existing image processing technologies to accurately identify a wealth of materials from construction site images.

Identification of Materials from Construction Site Images Using Content Based Image Retrieval Techniques

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. Examples include surface cracks detection, assessment of fire-damaged mortar, fatigue evaluation of asphalt mixes, aggregate shape measurements, velocimentry, vehicles detection, pore size distribution in geotextiles, damage detection and others. This capability is a product of the technological breakthroughs in the area of Image and Video Processing that has allowed for the development of a large number of digital imaging applications in all industries ranging from the well established medical diagnostic tools (magnetic resonance imaging, spectroscopy and nuclear medical imaging) to image searching mechanisms (image matching, content based image retrieval). Content based image retrieval techniques can also assist in the automated recognition of materials in construction site images and thus enable the development of reliable methods for image classification and retrieval. The amount of original imaging information produced yearly in the construction industry during the last decade has experienced a tremendous growth. Digital cameras and image databases are gradually replacing traditional photography while owners demand complete site photograph logs and engineers store thousands of images for each project to use in a number of construction management tasks. However, construction companies tend to store images without following any standardized indexing protocols, thus making the manual searching and retrieval a tedious and time-consuming effort. Alternatively, material and object identification techniques can be used for the development of automated, content based, construction site image retrieval methodology. These methods can utilize automatic material or object based indexing to remove the user from the time-consuming and tedious manual classification process. In this paper, a novel material identification methodology is presented. This method utilizes content based image retrieval concepts to match known material samples with material clusters within the image content. The results demonstrate the suitability of this methodology for construction site image retrieval purposes and reveal the capability of existing image processing technologies to accurately identify a wealth of materials from construction site images.

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.

Remote Wireless Communications for Construction Management: A Case Study

The recent advances in urban wireless communications and protocols that spurred the development of city-wide wireless infrastructure motivated this research, since in many cases, construction sites are not conveniently located for wired connectivity. Large scale transportation projects for example, such as new highways, railroad tracks and the networks of utilities (power-lines, phone lines, mobile towers, etc) that usually follow them are constructed in areas where wired infrastructure for data exchange is often expensive and time-consuming to deploy. The communication difficulties that can be encountered in such construction sites can be addressed with a wireless communications link between the construction site and the decision-making office. This paper presents a case study on long-range, wireless communications suitable for data exchange between construction sites and engineering headquarters. The purpose of this study was to define the requirements for a reliable wireless communications model where common types of electronic construction data will be exchanged in a fast and efficient manner, and construction site personnel will be able to interact and share knowledge, information and electronic resources with the office staff.

Civil and Environmental Engineering Challenges for Data Sensing and Analysis

The objective of this study was to identify challenges in civil and environmental engineering that can potentially be solved using data sensing and analysis research. The challenges were recognized through extensive literature review in all disciplines of civil and environmental engineering. The literature review included journal articles, reports, expert interviews, and magazine articles. The challenges were ranked by comparing their impact on cost, time, quality, environment and safety. The result of this literature review includes challenges such as improving construction safety and productivity, improving roof safety, reducing building energy consumption, solving traffic congestion, managing groundwater, mapping and monitoring the underground, estimating sea conditions, and solving soil erosion problems. These challenges suggest areas where researchers can apply data sensing and analysis research.