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This paper focuses on the benchmark control problems for seismically excited nonlinear buildings. The benchmark study focuses on three typical steel structures, 3-, 9-, and 20-story buildings designed for the SAC project for the Los Angeles region. This paper reports the application of the active control scheme on the 3- and 20-story benchmark buildings, where the control action is achieved by a fuzzy logic controller. The main advantage of the fuzzy controller is its inherent robustness and ability to handle any nonlinear behavior of the structure. The performance of the controller is validated through the computer simulation on MATLAB. The control scheme satisfies the control constraints and is presented according to the evaluation criteria provided with the benchmark problems for comparison with other schemes. Four different earthquakes (El Centro 1940, Hachinohe 1968, Northridge 1994, and Kobe 1995) will be used in the simulation. [ABSTRACT FROM AUTHOR]

Homeland security challenges civil engineers to develop solutions to protect critical infrastructure systems from intentional attacks. The water infrastructure system is one of eight critical infrastructures defined in the report by the President’s Commission on Critical Infrastructure Protection (PDD 1998). This paper offers a model for an operational response strategy to mitigate consequences of intentional physical attacks on the water infrastructure system. An intentional physical attack occurs when a water infrastructure facility is nonfunctional because of physical destruction of the facility by individuals or groups with malicious intentions. Three indices are presented in this paper to measure the consequences of intentional physical attacks: (a) the degree of the disruption of critical infrastructure services, (b) economic loss, and (c) the number of people affected. Then, a multiobjective genetic algorithm is used to find solutions to affect minimum consequences among these indicators. Evaluation of the model using two water infrastructure networks indicates that the genetic algorithms can successfully identify Pareto-optimal solutions to reduce the negative consequences of different attack scenarios. These solutions represent a pool of strategies to be incorporated in emergency plans. In addition, the modified nondominated sorting genetic algorithms (NSGA-II) developed in this study can significantly improve the quality of the solution early in the evolution process. This can be very critical when the time available to obtain mitigation plans is very limited. [ABSTRACT FROM AUTHOR]

This paper presents the development of a parallel multiobjective genetic algorithm framework to enable an efficient and effective optimization of resource utilization in large-scale construction projects. The framework incorporates a multiobjective optimization module, a global parallel genetic algorithm module, a coarse-grained parallel genetic algorithm module, and a performance evaluation module. The framework is implemented on a cluster of 50 parallel processors and its performance was evaluated using 183 experiments that tested various combinations of construction project sizes, numbers of parallel processors and genetic algorithm setups. The results of these experiments illustrate the new and unique capabilities of the developed parallel genetic algorithm framework in: (1) Enabling an efficient and effective optimization of large-scale construction projects; (2) achieving significant computational time savings by distributing the genetic algorithm computations over a cluster of parallel processors; and (3) requiring a limited and feasible number of parallel processors/computers that can be readily available in construction engineering and management offices. [ABSTRACT FROM AUTHOR]

Bridge engineers need to group bridges to determine adequate let projects based on their proximities, types of work, costs, and cost constraints for each clustered let project after bridges to be treated yearly are identified. This process is time-consuming and typically performed manually. First, this paper presents the formulation of a bridge clustering problem for determining let projects by considering the bridge proximity and type of work with preference membership functions to simulate the actual decision-making process. Second, a constrained fuzzy c-mean (FCM) clustering algorithm is presented to resolves this problem. A case study using the subset of bridges in the state of Georgia with the hypothetical treatments and costs was used to test the developed algorithm and to demonstrate its capability. The results show that the developed constrained FCM clustering algorithm can, in seconds, effectively determine adequate let projects by clustering bridges while meeting cost constraints. The presented formulation also allows incorporation of additional factors such as preference of clustering bridges with the same route number or same route type that are important to other state Departments of Transportation. Finally, conclusions about the benefits and characteristics of the developed algorithm are summarized, and recommendations for future research are discussed. [ABSTRACT FROM AUTHOR]

Following the events of September 11, 2001, in the United States, world public awareness for possible terrorist attacks on water supply systems has increased dramatically. Among the different threats for a water distribution system, the most difficult to address is a deliberate chemical or biological contaminant injection, due to both the uncertainty of the type of injected contaminant and its consequences, and the uncertainty of the time and location of the injection. An online contaminant monitoring system is considered as a major opportunity to protect against the impacts of a deliberate contaminant intrusion. However, although optimization models and solution algorithms have been developed for locating sensors, little is known about how these design algorithms compare to the efforts of human designers, and thus, the advantages they propose for practical design of sensor networks. To explore these issues, the Battle of the Water Sensor Networks (BWSN) was undertaken as part of the 8th Annual Water Distribution Systems Analysis Symposium, Cincinnati, Ohio, August 27–29, 2006. This paper summarizes the outcome of the BWSN effort and suggests future directions for water sensor networks research and implementation. [ABSTRACT FROM AUTHOR]

Forecasts of future events are required in many activities associated with planning and operation of the components of a water resources system. For the hydrologic component, there is a need for both short term and long term forecasts of streamflow events in order to optimize the system or to plan for future expansion or reduction. This paper presents a comparison of different artificial neural networks (ANNs) algorithms for short term daily streamflow forecasting. Four different ANN algorithms, namely, backpropagation, conjugate gradient, cascade correlation, and Levenberg–Marquardt are applied to continuous streamflow data of the North Platte River in the United States. The models are verified with untrained data. The results from the different algorithms are compared with each other. The correlation analysis was used in the study and found to be useful for determining appropriate input vectors to the ANNs. [ABSTRACT FROM AUTHOR]

TransGuide is San Antonio’s multifunctional citywide center that manages traffic through surveillance, incident detection/management, and dissemination of traffic information. This paper reviews automatic incident detection technologies deployed in San Antonio freeways and managed by TransGuide. Traffic and incident data collected from the San Antonio network are used to compare the performance of inductive loop detectors (ILDs) and automatic vehicle identification (AVI) for automated incident detection. California No. 8 and the Texas algorithms were calibrated and tested using the ILD data collected for incident detection. The upper confidence limit algorithm and the Texas algorithm were calibrated and tested using the AVI data collected. When traffic and incident data from the San Antonio network are processed by the four different algorithms, the California No. 8 algorithms applied to ILD data performed best in terms of detection rate and false alarm rate. Automated incident detection (AID) is not currently worth implementing in the AVI system studied, but AID based on AVI data is generally feasible with denser tag penetration and sensor installation. [ABSTRACT FROM AUTHOR]

Nonpoint source management using wet detention ponds to reduce pollutant loading into receiving water bodies is a common practice. Design of these ponds is typically carried out individually to meet a target total suspended solids (TSS) removal level. An improvement to this approach is to generate cost-effective pond configurations that meet system-wide targets for removal of pollutant loadings, including TSS, total nitrogen, and total phosphorus, corresponding to a specific build-out land use plan. The amount of pollutant loading can be managed better through appropriate land use allocation planning conducted simultaneously with the design of detention pond configurations. This paper presents a modeling approach to address this problem and its solution via a genetic algorithm-based search procedure. Application of this approach is demonstrated using an illustrative case study involving the City Lake watershed in North Carolina. The results show cost improvements when considering land management simultaneously with the decisions for pond locations and sizes. Through iterative application of this method, the tradeoff between cost and removal level of each pollutant is also generated. [ABSTRACT FROM AUTHOR]

The article presents the authors' reply to a critique of their research on algorithms applied in multiple calendars used in managing construction schedules in the U.S. Key issues discussed include the key applications of the algorithms in conjunction with commercially available software to analyze unexpected results such as broken critical paths, as well as the current calculations and idiosyncrasies that involve multiple calendars.

Historical and in-service pavement layer data along with pavement maintenance, rehabilitation, and reconstruction (MR&R) history is critical information for developing a reliable and accurate pavement performance model that is indispensably necessary for the support of a pavement management system. The conventional homogeneous segmentation method for pavement layer data repository has the problem of excessive segmentation, which may result in inefficient data storage and incomplete data. To address this issue, a spatiotemporal block model is proposed to eliminate the need of homogeneous segmentation, and thus avoid the excessive segmentation and the creation of tiny pavement segments. Three time factors are defined for each pavement layer block to maintain the temporal information of a pavement layer structure. A depth-first-search (DFS)-based cluster searching algorithm is used for reconstruction and visualization of pavement structures as well as for data manipulation. A case study is presented to demonstrate the application developed using the proposed model and algorithms. The computing time and merits of the proposed methodology are also discussed. [ABSTRACT FROM AUTHOR]

Many thousands of impaired water segments in the United States will be the subject of total maximum daily load (TMDL) determinations in the next decade. Many of these load allocations will be established with access to only minimal local data. Long-term and detailed datasets from other locations can facilitate this process by offering general insights into the processes that interact to produce the chemistry observed in a particular waterbody over time. These insights can lead to more enlightened interpretation of sparse but locally relevant water quality data. They can also inform the design of implementation monitoring to evaluate success of TMDLs. Finally, study of such datasets reveals biases that may result from inappropriate sampling design or data interpretation algorithms, and may lead to erroneous conclusions about the success or failure of a TMDL program in a specific watershed. [ABSTRACT FROM AUTHOR]

Any vehicle whose gross weight exceeds 356 kN (80,000 lb) and/or does not conform to the restriction imposed by the Tennessee Department of Transportation (TDOT) requires a permit issued by TDOT in order to use the Tennessee roads. The current practice in the TDOT Permit Section is that a detailed structural analysis is required for all vehicles with a gross weight over 667.5 kN (150,000 lb). Due to the volume of overload permit applications received, this policy resulted in a large demand in man-hours to perform the structural analysis. Furthermore, the policy does not give consistently safe or conservative assessment. A method has been developed empirically to efficiently extract any suspicious overweight vehicle requesting a permit, regardless of gross weight, for further detailed analysis. The technique utilizes the combination of gross weight, axle loads, and axle spacings. The algorithm can be easily implemented to a computer program for users with limited technical training, since the only input information is the axle load and spacing configurations. The method is intended to be conservative. A study, based on actual permit applications and detailed bridge analyses, has shown that this approach would reduce the number of structural analyses required by approximately 50% when compared to the current policy. [ABSTRACT FROM AUTHOR]