Material and Construction Optimization for Prevention of Premature Pavement Distress in PCC Pavements, September 2004

The chemistry of today’s concrete mixture designs is complicated by many variables, including multiple sources of aggregate and cements and a plethora of sometimes incompatible mineral and chemical admixtures. Concrete paving has undergone significant changes in recent years as new materials have been introduced into concrete mixtures. Supplementary cementitious materials such as fly ash and ground granulated blast furnace slag are now regularly used. In addition, many new admixtures that were not even available a few years ago now have widespread usage. Adding to the complexity are construction variables such as weather, mix delivery times, finishing practices, and pavement opening schedules. Mixture materials, mix design, and pavement construction are not isolated steps in the concrete paving process. Each affects and is affected by the other in ways that determine overall pavement quality and long-term performance. Equipment and procedures commonly used to test concrete materials and concrete pavements have not changed in decades, leaving serious gaps in our ability to understand and control the factors that determine concrete durability. The concrete paving community needs tests that will adequately characterize the materials, predict interactions, and monitor the properties of the concrete.

Material and Construction Optimization for Prevention of Premature Pavement Distress in PCC Pavements, Phase III Final Report, 2008

Mixture materials, mix design, and pavement construction are not isolated steps in the concrete paving process. Each affects the other in ways that determine overall pavement quality and long-term performance. However, equipment and procedures commonly used to test concrete materials and concrete pavements have not changed in decades, leaving gaps in our ability to understand and control the factors that determine concrete durability. The concrete paving community needs tests that will adequately characterize the materials, predict interactions, and monitor the properties of the concrete. The overall objectives of this study are (1) to evaluate conventional and new methods for testing concrete and concrete materials to prevent material and construction problems that could lead to premature concrete pavement distress and (2) to examine and refine a suite of tests that can accurately evaluate concrete pavement properties. The project included three phases. In Phase I, the research team contacted each of 16 participating states to gather information about concrete and concrete material tests. A preliminary suite of tests to ensure long-term pavement performance was developed. The tests were selected to provide useful and easy-to-interpret results that can be performed reasonably and routinely in terms of time, expertise, training, and cost. The tests examine concrete pavement properties in five focal areas critical to the long life and durability of concrete pavements: (1) workability, (2) strength development, (3) air system, (4) permeability, and (5) shrinkage. The tests were relevant at three stages in the concrete paving process: mix design, preconstruction verification, and construction quality control. In Phase II, the research team conducted field testing in each participating state to evaluate the preliminary suite of tests and demonstrate the testing technologies and procedures using local materials. A Mobile Concrete Research Lab was designed and equipped to facilitate the demonstrations. This report documents the results of the 16 state projects. Phase III refined and finalized lab and field tests based on state project test data. The results of the overall project are detailed herein. The final suite of tests is detailed in the accompanying testing guide.

A Comprehensive Portfolio Construction Under Stochastic Environment

Prior research has established that idiosyncratic volatility of the securities prices exhibits a positive trend. This trend and other factors have made the merits of investment diversification and portfolio construction more compelling. A new optimization technique, a greedy algorithm, is proposed to optimize the weights of assets in a portfolio. The main benefits of using this algorithm are to: a) increase the efficiency of the portfolio optimization process, b) implement large-scale optimizations, and c) improve the resulting optimal weights. In addition, the technique utilizes a novel approach in the construction of a time-varying covariance matrix. This involves the application of a modified integrated dynamic conditional correlation GARCH (IDCC - GARCH) model to account for the dynamics of the conditional covariance matrices that are employed. The stochastic aspects of the expected return of the securities are integrated into the technique through Monte Carlo simulations. Instead of representing the expected returns as deterministic values, they are assigned simulated values based on their historical measures. The time-series of the securities are fitted into a probability distribution that matches the time-series characteristics using the Anderson-Darling goodness-of-fit criterion. Simulated and actual data sets are used to further generalize the results. Employing the S&P500 securities as the base, 2000 simulated data sets are created using Monte Carlo simulation. In addition, the Russell 1000 securities are used to generate 50 sample data sets. The results indicate an increase in risk-return performance. Choosing the Value-at-Risk (VaR) as the criterion and the Crystal Ball portfolio optimizer, a commercial product currently available on the market, as the comparison for benchmarking, the new greedy technique clearly outperforms others using a sample of the S&P500 and the Russell 1000 securities. The resulting improvements in performance are consistent among five securities selection methods (maximum, minimum, random, absolute minimum, and absolute maximum) and three covariance structures (unconditional, orthogonal GARCH, and integrated dynamic conditional GARCH).

Testing Guide for Implementing Concrete Paving Quality Control Procedures, March 2008

Construction of portland cement concrete pavements is a complex process. A small fraction of the concrete pavements constructed in the United States over the last few decades have either failed prematurely or exhibited moderate to severe distress. In an effort to prevent future premature failures, 17 state transportation agencies pooled their resources, and a pooled fund research project, Material and Construction Optimization for Prevention of Premature Pavement Distress in PCC Pavements, was undertaken in 2003. Its purpose was to evaluate existing quality control tests, and then select and advance the state-of-the-practice of those tests most useful for optimizing concrete pavements during mix design, mix verification, and construction. This testing guide is one product of that project. The guide provides three recommended testing schemes (Levels A, B, and C, depending on a pavement’s design life and traffic volumes, etc.) that balance the costs of testing with the risk of failure for various project types. The recommended tests are all part of a comprehensive suite of tests described in detail in this guide.

Modern optimization in earthwork construction

Earthworks tasks are often regarded in transportation projects as some of the most demanding processes. In fact, sequential tasks such as excavation, transportation, spreading and compaction are strongly based on heavy mechanical equipment and repetitive processes, thus becoming as economically demanding as they are time-consuming. Moreover, actual construction requirements originate higher demands for productivity and safety in earthwork constructions. Given the percentual weight of costs and duration of earthworks in infrastructure construction, the optimal usage of every resource in these tasks is paramount. Considering the characteristics of an earthwork construction, it can be looked at as a production line based on resources (mechanical equipment) and dependency relations between sequential tasks, hence being susceptible to optimization. Up to the present, the steady development of Information Technology areas, such as databases, artificial intelligence and operations research, has resulted in the emergence of several technologies with potential application bearing that purpose in mind. Among these, modern optimization methods (also known as metaheuristics), such as evolutionary computation, have the potential to find high quality optimal solutions with a reasonable use of computational resources. In this context, this work describes an optimization algorithm for earthworks equipment allocation based on a modern optimization approach, which takes advantage of the concept that an earthwork construction can be regarded as a production line.

Earthwork optimization system for sustainable highway construction

In highway construction, earthworks refer to the tasks of excavation, transportation, spreading and compaction of geomaterial (e.g. soil, rockfill and soil-rockfill mixture). Whereas relying heavily on machinery and repetitive processes, these tasks are highly susceptible to optimization. In this context Artificial Intelligent techniques, such as Data Mining and modern optimization can be applied for earthworks. A survey of these applications shows that they focus on the optimization of specific objectives and/or construction phases being possible to identify the capabilities and limitations of the analyzed techniques. Thus, according to the pinpointed drawbacks of these techniques, this paper describes a novel intelligent earthwork optimization system, capable of integrating DM, modern optimization and GIS technologies in order to optimize the earthwork processes throughout all phases of design and construction work. This integration system allows significant savings in time, cost and gas emissions contributing for a more sustainable construction.

Optimization and Management of Materials in Earthwork Construction, TR-501, 2010

As a result of forensic investigations of problems across Iowa, a research study was developed aimed at providing solutions to identified problems through better management and optimization of the available pavement geotechnical materials and through ground improvement, soil reinforcement, and other soil treatment techniques. The overall goal was worked out through simple laboratory experiments, such as particle size analysis, plasticity tests, compaction tests, permeability tests, and strength tests. A review of the problems suggested three areas of study: pavement cracking due to improper management of pavement geotechnical materials, permeability of mixed-subgrade soils, and settlement of soil above the pipe due to improper compaction of the backfill. This resulted in the following three areas of study: (1) The optimization and management of earthwork materials through general soil mixing of various select and unsuitable soils and a specific example of optimization of materials in earthwork construction by soil mixing; (2) An investigation of the saturated permeability of compacted glacial till in relation to validation and prediction with the Enhanced Integrated Climatic Model (EICM); and (3) A field investigation and numerical modeling of culvert settlement. For each area of study, a literature review was conducted, research data were collected and analyzed, and important findings and conclusions were drawn. It was found that optimum mixtures of select and unsuitable soils can be defined that allow the use of unsuitable materials in embankment and subgrade locations. An improved model of saturated hydraulic conductivity was proposed for use with glacial soils from Iowa. The use of proper trench backfill compaction or the use of flowable mortar will reduce the potential for developing a bump above culverts.

Sparse model construction using coordinate descent optimization

We propose a new sparse model construction method aimed at maximizing a model’s generalisation capability for a large class of linear-in-the-parameters models. The coordinate descent optimization algorithm is employed with a modified l1- penalized least squares cost function in order to estimate a single parameter and its regularization parameter simultaneously based on the leave one out mean square error (LOOMSE). Our original contribution is to derive a closed form of optimal LOOMSE regularization parameter for a single term model, for which we show that the LOOMSE can be analytically computed without actually splitting the data set leading to a very simple parameter estimation method. We then integrate the new results within the coordinate descent optimization algorithm to update model parameters one at the time for linear-in-the-parameters models. Consequently a fully automated procedure is achieved without resort to any other validation data set for iterative model evaluation. Illustrative examples are included to demonstrate the effectiveness of the new approaches.

Optimization of incubation time of protein Tc85 in the construction of biosensor: Is the EIS a good tool?

Electrochemical impedance spectroscopy (EIS) in pH 6.9 phosphate buffer solution was used to investigate each step of the procedure employed to modify a screen-printed electrode (SPE). The SPE was modified with self-assembled monolayers (SAMs) of cystamine (CYS, deposited from 20 mM solution), followed by glutaraldehyde (GA, 0.3 M solution). The Trypanosoma cruzi antigen was immobilized using different deposition times. The influence of incubation time (2-18 h) of protein was also investigated. The topography of modified electrode with this protein was investigated by atomic force microscopy (AFM). Interpretation of impedance data was based on physical and chemical adsorption, and degradation of the layer at high and meddle frequencies, and charge transfer reaction involving mainly the reduction of oxygen at low frequencies. EIS studies on modified electrodes with Tc85 protein immobilized for different incubation times indicated that the optimum incubation time was 6-8 h. It was demonstrated that EIS is a good technique to evaluate the different steps and the integrity of the surface modifications, and to optimize the incubation time of protein in the development of biosensors. (C) 2010 Elsevier B.V. All rights reserved.

Optimization of incubation time of protein Tc85 in the construction of biosensor: Is the EIS a good tool?

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Construction of pediatric homogeneous phantoms for optimization of chest and skull radiographs

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

The construction and optimization of an ion mobility spectrometer for the analysis of explosives and drugs

Today, over 15,000 Ion Mobility Spectrometry (IMS) analyzers are employed at worldwide security checkpoints to detect explosives and illicit drugs. Current portal IMS instruments and other electronic nose technologies detect explosives and drugs by analyzing samples containing the headspace air and loose particles residing on a surface. Canines can outperform these systems at sampling and detecting the low vapor pressure explosives and drugs, such as RDX, PETN, cocaine, and MDMA, because these biological detectors target the volatile signature compounds available in the headspace rather than the non-volatile parent compounds of explosives and drugs.^ In this dissertation research volatile signature compounds available in the headspace over explosive and drug samples were detected using SPME as a headspace sampling tool coupled to an IMS analyzer. A Genetic Algorithm (GA) technique was developed to optimize the operating conditions of a commercial IMS (GE Itemizer 2), leading to the successful detection of plastic explosives (Detasheet, Semtex H, and C-4) and illicit drugs (cocaine, MDMA, and marijuana). Short sampling times (between 10 sec to 5 min) were adequate to extract and preconcentrate sufficient analytes (> 20 ng) representing the volatile signatures in the headspace of a 15 mL glass vial or a quart-sized can containing ≤ 1 g of the bulk explosive or drug.^ Furthermore, a research grade IMS with flexibility for changing operating conditions and physical configurations was designed and fabricated to accommodate future research into different analytes or physical configurations. The design and construction of the FIU-IMS were facilitated by computer modeling and simulation of ion’s behavior within an IMS. The simulation method developed uses SIMION/SDS and was evaluated with experimental data collected using a commercial IMS (PCP Phemto Chem 110). The FIU-IMS instrument has comparable performance to the GE Itemizer 2 (average resolving power of 14, resolution of 3 between two drugs and two explosives, and LODs range from 0.7 to 9 ng). ^ The results from this dissertation further advance the concept of targeting volatile components to presumptively detect the presence of concealed bulk explosives and drugs by SPME-IMS, and the new FIU-IMS provides a flexible platform for future IMS research projects.^

The construction and optimization of a surface plasmon resonance sensor for the studies of bio-molecular interactions

Bio-molecular interactions exist ubiquitously in all biological systems. This dissertation project was to construct a powerful surface plasmon resonance (SPR) sensor. The SPR system is used to study bio-molecular interactions in real time and without labeling. Surface plasmon is the oscillation of free electrons in metals coupled with surface electromagnetic waves. These surface electromagnetic waves provide a sensitive probe to study bio-molecular interactions on metal surfaces. This project resulted in the successful construction and optimization of a homemade SPR sensor and the development of several new powerful protocols to study bio-molecular interactions. It was discovered through this project that the limitations of earlier SPR sensors are related not only to the instrumentation design and operating procedures, but also to the complex behaviors of bio-molecules on sensor surfaces that were very different from that in solution. Based on these discoveries the instrumentation design and operating procedures were fully optimized. A set of existing sensor surface treatment protocols were tested and evaluated and new protocols were developed in this project. The new protocols have demonstrated excellent performance to study biomolecular interactions. The optimized home-made SPR sensor was used to study protein-surface interactions. These protein-surface interactions are responsible for many complex organic cell activities. The co-existence of different driving forces and their correlation with the structure of the protein and the surface make the understanding of the fundamental mechanism of protein-surface interactions a very challenging task. Using the improved SPR sensor, the electrostatic interaction and hydrophobic interaction were studied separately. The results of this project directly confirmed the theoretical predictions for electrostatic force between the protein and surface. In addition, this project demonstrated that the strength of the protein-surface hydrophobic interaction does not solely depend on the hydrophobicity as reported earlier. Surface structure also plays a significant role.