A. Gomes Correia

Development of Hybrid Braided Composite Rods for Reinforcement and Health Monitoring of Structures

In the present study, core-reinforced braided composite rods (BCRs) were developed and characterized for strain sensing capability. A mixture of carbon and glass fibre was used in the core, which was surrounded by a braided cover of polyester fibres. Three compositions of core with different carbon fibre/glass fibre weight ratios (23/77, 47/53, and 100/0) were studied to find out the optimum composition for both strain sensitivity and mechanical performance. The influence of carbon fibre positioning in BCR cross-section on the strain sensing behaviour was also investigated. Strain sensing property of BCRs was characterized by measuring the change in electrical resistance with flexural strain. It was observed that BCRs exhibited increase (positive response) or decrease (negative response) in electrical resistance depending on carbon fibre positioning. The BCR with lowest amount of carbon fibre was found to give the best strain sensitivity as well as the highest tensile strength and breaking extension. The developed BCRs showed reversible strain sensing behaviour under cyclic flexural loading with a maximum gauge factor of 23.4 at very low strain level (0.55%). Concrete beams reinforced with the optimum BCR (23/77) also exhibited strain sensing under cyclic flexural strain, although the piezoresistive behaviour in this case was irreversible.

An evolutionary multi-objective optimization system for earthworks

A novel system for dynamic optimization of earthworks is proposed.The system is based on an evolutionary multi-objective (cost-duration) approach.Results from several experiments using real-world data are presented.Results show the system is very competitive when compared to conventional design. Earthworks involve the leveling or shaping of a target area through the moving or processing of the ground surface. Most construction projects require earthworks, which are heavily dependent on mechanical equipment (e.g., excavators, trucks and compactors). Often, earthworks are the most costly and time-consuming component of infrastructure constructions (e.g., road, railway and airports) and current pressure for higher productivity and safety highlights the need to optimize earthworks, which is a nontrivial task. Most previous attempts at tackling this problem focus on single-objective optimization of partial processes or aspects of earthworks, overlooking the advantages of a multi-objective and global optimization. This work describes a novel optimization system based on an evolutionary multi-objective approach, capable of globally optimizing several objectives simultaneously and dynamically. The proposed system views an earthwork construction as a production line, where the goal is to optimize resources under two crucial criteria (costs and duration) and focus the evolutionary search (non-dominated sorting genetic algorithm-II) on compaction allocation, using linear programming to distribute the remaining equipment (e.g., excavators). Several experiments were held using real-world data from a Portuguese construction site, showing that the proposed system is quite competitive when compared with current manual earthwork equipment allocation.

Artificial intelligence applications in transportation geotechnics

This paper presents a brief overview of artificial intelligence applications in transportation geotechnics, highlighting new approaches and current research directions, including issues related to data mining interpretability and prediction capacities. Several practical applications to earthworks, including the compaction management and quality control aspects of embankments, as well as pavement evaluation, design and management, and the mechanical behaviour of jet grouting material, are presented to illustrate the advantages of using data mining, including artificial neural networks, support vector machines, and evolutionary computation techniques in this domain. This study also propose a novel simplified compaction table for reusing geomaterials and compaction management in embankments and applied one- and two-dimensional advanced sensitivity analyses to better interpret the proposed data-driven models for the prediction of the deformability modulus of jet grouting field samples. These applications show the capabilities of data mining models to address complex problems in transportation geotechnics involving highly nonlinear relationships of data and optimisation needs.

Continuous Stiffness Monitoring of Cemented Sand through Resonant Frequency

Mixture formulation and in-situ quality control of the stabilized soils often represent difficult and challenging tasks. The present paper addresses the possibility of using a variant to a recently developed non-destructive technique for continuous monitoring of stiffness of hardening materials as a supporting means to the above-mentioned tasks. The material to be tested is placed inside a polycarbonate mold placed in simply supported conditions. The technique is based on the continuous monitoring of the first resonant frequency of this composite beam, which evolves as a consequence of the hardening of the material, and can be correlated with its E-modulus. The usefulness and potential of this experimental methodology for mixture formulation and quality control of stabilized soils is shown through a series of tests conducted on laboratory since the instant of mixing until 7 days. The conducted tests include complementary methodologies of characterization such as E-modulus measured on specimens with strain instrumentation, as well as monitoring with recourse to bender-extender elements.

Jet grouting column diameter prediction based on a data-driven approach

This study takes advantage of the high learning capabilities of data mining (DM) techniques towards to the development of a novel approach for jet grouting (JG) column diameter prediction. The high number of variables involved in JG technology as well as the complex phenomena related with the injection process make JG column diameter (D) prediction a difficult task. Therefore, in order to overcome it, the flexible learning capabilities of DM techniques were applied as an alternative approach of the traditional tools. The achieved results show that both artificial neural network and support vector machine algorithms can be trained to accurately predict D built in different soil types of clayey nature and using different JG systems. In both cases a coefficient of correlation () very close to the unity was achieved. For models training, a set of eight input variables were considered. Among them, the rod withdrawal speed, flow rate of the grout slurry and the JG system were identified as the most relevant ones, although the grout pressure and the dynamic impact of the grout also revealed an important influence on D prediction. Moreover, additionally to the identification of the key model variables, it was also measured their effects on D prediction based on a data-based sensitivity analysis. These achievements represent a novel contribution for JG technology, mainly at the design level. Furthermore, the obtained results also underline the potential and contribution of DM to solving complex problem in geotechnical engineering.

Comparison between laboratory and field stiffness by wave measurements

The present work aims to establish a relationship between field and laboratory moduli obtained from mechanical wave measurements. Spectral analysis of surface waves was performed on a clayey sand trial layer during tests in a full-scale trial. Laboratory triaxial tests involving S-wave measurements with bender elements and accelerometers were conducted on specimens under similar conditions, and the shear modulus was determined. Both laboratory and field methodologies for the determination of the shear modulus were based on S-wave propagation through the tested material; thus they involved similar stress and strain levels. A reasonable relationship was observed between field and laboratory moduli, taking into account field conditions (moisture content and void ratio) and stress state.

Case Study to Promote the Use of Industrial Byproducts: The Relevance of Performance Tests

Currently, there is strong pressure to use industrial byproducts and recycled materials in the construction of transportation infrastructure and geotechnical works. The reuse of these materials positively affects the environment by reducing deposits and preserving raw materials. The related geotechnical, geoenvironmental, economical, and social issues should be addressed so that these materials can be used in construction to provide sustainable development. This paper presents a study of all of these aspects and focuses on Portuguese electrical arc furnace steel slag. A huge laboratory research program was carried out that addressed four elements of geotechnical and geoenvironmental behavior: ultimate strength under monotonic loading, resilient behavior (stiffness), susceptibility to permanent deformation due to repeated loading, and leachability. These test results were compared with those from the empirical tests used in the national specifications for embankments and structural layers of transportation infrastructures. It was concluded that performance-based laboratory test results show much better material performance than the results based on empirical tests (Los Angeles and micro-Deval). Furthermore, this material shows better mechanical performance than in the mechanical tests of natural unbound granular materials used in road construction. Additionally, leaching test results show that this byproduct is inert, which caused it to become known as “inert steel aggregates for construction” (ISAC). These laboratory conclusions were validated in a full-scale field trial by end performance testing (using devices that measure in situ stiffness through spot tests and continuous monitoring, as well as lysimeters to measure leaching values). This field trial involved raw materials and ISAC. A final remark is made about some socioeconomic aspects that should be taken into account in decision making regarding the use of ISAC in public works.

Jet grouting mechanicals properties prediction using data mining techniques

The authors wish to thank to Portuguese Foundation for Science and Technology (FCT) the support given through the doctoral grant SFRH/BD/45781/2008

Prediction of surface distress using neural networks

The Authors are grateful to the Lembaga Pengelola Dana Pendidikan (LPDP) for the financial support provided to this thesis through the grant financed by the Ministry of Finance Republic of Indonesia.

New optimization strategies of pavement maintenance: A case study for national road network in Indonesia using integrated road management system

A road network requires timely maintenance to keep the road surface in good condition onward better services to improve accessibility and mobility. Strategies and maintenance techniques must be chosen in order to maximize road service level through cost-effective interventions. This approach requires an updated database, which the road network in Indonesia is supported by a manual and visual survey, also using NAASRA profiler. Furthermore, in this paper, the deterministic model of deterioration was used. This optimization model uses life cycle cost analysis (LCCA), applied in an integrated manner, using IRI indicator, and allows determining the priority of treatment, type of treatment and its relation to the cost. The purpose of this paper was focussed on the aspects of road maintenance management, i.e., maintenance optimization models for different levels of traffic and various initial of road distress conditions on the national road network in Indonesia. The implementation of Integrated Road Management System (IRMS) can provide a solution to the problem of cost constraints in the maintenance of the national road network. The results from this study found that as the lowest as agency cost, it will affect the increasing of user cost. With the achievement of the target plan scenario Pl000 with initial value IRI 2, it was found that the routine management throughout the year and in early reconstruction and periodic maintenance with a 30 mm thick overlay, will simultaneously provide a higher net benefit value and has the lowest total cost of transportation.