This paper presents a fuzzy subtractive modelling technique to predict the weight of telecommunication towers which is used to estimate their respective costs. This is implemented through the utilization of data from previously installed telecommunication towers considering four input parameters: a) tower height; b) allowed tilt or deflection; c) antenna subjected area loading; and d) wind load. Telecommunication towers are classified according to designated code (TIA-222-F and TIA-222-G standards) and structures type (Self-Supporting Tower (SST) and Roof Top (RT)). As such, four fuzzy

Traditional modeling frameworks used for infrastructure asset management have suffered from two main shortcomings. Most approaches have focused their modeling efforts on the infrastructure asset itself, thereby ignoring the multitude of interactions that occur between other entities. In addition, an a-priori behavior of all elements in the modeling environment has always been assumed. This paper argues that these shortcomings have significantly limited the decision-making capabilities of infrastructure asset management systems by limiting their ability to simulate emergent behavior that is

Knowledge has been identified to be a significant organizational resource, which if used effectively can provide competitive advantage. Construction contract administration is a complex, knowledge-intensive process that if properly managed can mitigate the contractor's risk exposure. Challenges in proper knowledge management of contract administration are due to: 1) Large amount of fragmented information that is required to manage a construction contract, 2) Information located in heterogeneous sources (Request For Information (RFIs), site notices, schedules, contracts, etc...), 3) Information

Earthmoving operations represent a sizable work in heavy civil engineering projects. Selecting optimum fleet configuration for an earthmoving operation is a very difficult process, especially when dealing with a multi loader type and multi truck type configurations. This paper presents a framework that can be used for the selection of optimum fleet for earthmoving operations. It enables the user to input the available loading and hauling equipment, then, it calculates the cost and total project time of each possible fleet combination, and finally it provides a list of the top-ten best fleet

Condition assessment is an integral component in any infrastructure asset management system. Without condition information, asset managers lack the ability to make appropriate decisions regarding needed maintenance, rehabilitation, and replacement of infrastructure. Existing and emerging technologies for assessing the condition of water and sewer pipes provide a better picture of the state of these buried assets. Unfortunately, many of these technologies are costly and provide results that are not always highly reliable. This paper presents a methodology to assist asset managers in balancing

The use of water main break history as a proxy for condition has become common practice because of the high costs associated with direct assessments. Statistical deterioration models predict future water main breaks on the basis of historical patterns. Many municipalities are beginning to understand the value of utilizing water pipe break histories to manage their noncritical distribution networks via deterioration models. This paper presents a generic IDEF0 process model for developing water main deterioration models. Two common statistical deterioration models for water pipes are compared

The rapid urbanization and industrialization is causing worldwide water pollution, calling for advanced cleaning methods. For instance, pollutant adsorption on magnetic oxides is efficient and very practical due to the easy separation from solutions by an magnetic field. Here we review the synthesis and performance of magnetic oxides such as iron oxides, spinel ferrites, and perovskite oxides for water remediation. We present structural, optical, and magnetic properties. Magnetic oxides are also promising photocatalysts for the degradation of organic pollutants. Antimicrobial activities and

Detecting trending topics or events from Twitter is an active research area. The first step in detecting such topics focuses on efficiently capturing textual features that exhibit an unusual high rate of appearance during a specific timeframe. Previous work in this area has resulted in coining the term "detecting bursty features" to refer to this step. In this paper, TFIDF, entropy, and stream chunking are adapted to investigate a new technique for detecting bursty features from an Arabic Twitter stream. Experimental results comparing bursty features extracted from Twitter streams, to Twitter

One of the key challenges in applying deep learning to solve real-life problems is the lack of large annotated datasets. Furthermore, for a deep learning model to perform well on the test set, all samples in the training and test sets should be independent and identically distributed (i.i.d.), which means that test samples should be similar to the samples that were used to train the model. In many cases, however, the underlying training and test set distributions are different. In such cases, it is common to adapt the test samples by transforming them to their equivalent counterparts in the

Pipelines conveying gas under pressure exhibit turbulence-induced vibrations. The current work is concerned with extracting useful power from pipelines operating well within their stability region. At such regions, the pipe vibrations exist in small magnitudes and are unlikely to cause structural failure, yet can be exploited to provide useful energy for low-power electronic devices. Accordingly, emphasis in the present work is placed on the development of an energy harvesting technique employing the omnipresent and inevitable flow-induced vibrations in gas pipelines. © 2010 SPIE.