Machine Learning in Tribology

Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology

Determining Additional Modulus of Subgarde Reaction Based on Tolerable Settlement for the Nailed-slab System Resting on Soft Clay.

Abstract—Nailed-slab System is a proposed alternative solution for rigid pavement problem on soft soils. Equivalent modulus of subgrade reaction (k’) can be used in designing of nailed-slab system. This modular is the cumulative of modulus of subgrade reaction from plate load test (k) and additional modulus of subgrade reaction due to pile installing (∆∆∆∆k). A recent method has used reduction of pile resistance approach in determining ∆∆∆∆k. The relative displacement between pile and soils, and reduction of pile resistance has been identified. In fact, determining of reduction of pile resistance is difficult. This paper proposes an approach by considering tolerable settlement of rigid pavement. Validation is carried out with respect to a loading test of nailed-slab models. The models are presented as strip section of rigid pavement. The theory of beams on elastic foundation is used to calculate the slab deflection by using k’. Proposed approach can results in deflection prediction close to observed one. In practice, the Nailed-slab System would be constructed by multiple-row piles. Designing this system based on one-pile row analysis will give more safety design and will consume less time

Advanced Underground Space Technology

The recent development of underground space technology makes underground space a potential and feasible solution to climate change, energy shortages, the growing population, and the demands on urban space. Advances in material science, information technology, and computer science incorporating traditional geotechnical engineering have been extensively applied to sustainable and resilient underground space applications. The aim of this Special Issue, entitled “Advanced Underground Space Technology”, is to gather original fundamental and applied research related to the design, construction, and maintenance of underground space

Framework of damage detection in vehicle-bridge coupled system and application to bridge scour monitoring

Most vibration-based damage identification methods make use of measurements directly from bridge structures with attached sensors. However, the vehicle moving on the bridge can serve as both an active actuator and a response receiver. This dissertation aimed to develop new methodologies to eventually detect bridge damages such as scour using the dynamic response of the vehicle. To reach the final objective, a framework of damage identification was developed first, which gave a guideline on the three crucial steps for damage detection. An optimization method was proposed that combines the Genetic Algorithm (GA) and the First Order (FO) method. It has the advantages of the global and local algorithms and converges faster than the traditional method using any initial values. Secondly, a new methodology using the transmissibility of vehicle and bridge responses was developed to detect bridge damages. The transmissibility of a simplified vehicle-bridge coupled (VBC) system was analyzed theoretically and numerically to study the feasibility of this method. To obtain the transmissibility, two methods were proposed using two “static” vehicles on the bridge. Then, a tractor-trailer test system was designed to obtain reliable responses and extract bridge modal properties from the dynamic response of moving vehicles. The test vehicle consists of a tractor and two following trailers. The residual responses of the two trailers were used, which successfully eliminated the roughness and vehicle driving effect and extracted the bridge modal properties. This methodology was applied on a field bridge and revealed a good performance. Most previous studies of bridge scour focus on the scour causes instead of its consequences. Finally, in this dissertation the developed methodologies were applied to detect scour damage from the response of bridge and/or vehicles. The scour effect on a single pile was studied and methods of scour damage detections were proposed. A monitoring system using fiber optic sensors was designed and tested in the laboratory and is being applied to a field bridge. Finally, the scour effect on the response of the entire bridge and the traveling vehicle was also investigated under the bridge-vehicle-wave interaction, which in turn was used to detect the bridge scour

Machine Learning Prediction of Mechanical and Durability Properties of Recycled Aggregates Concrete

Whilst recycled aggregate (RA) can alleviate the environmental footprint of concrete production and the landfilling of colossal amounts of demolition waste, there need for robust predictive tools for its effects on mechanical and durability properties. In this thesis, state-of-the-art machine learning (ML) models were deployed to predict properties of recycled aggregate concrete (RAC). A systematic review was performed to analyze pertinent ML techniques previously applied in the concrete technology field. Accordingly, three different ML methods were selected to determine the compressive strength of RAC and perform mixture proportioning optimization. Furthermore, a gradient boosting regression tree was used to study the effects of RA and several types of binders on the carbonation depth of RAC. The ML models developed in this study demonstrated robust performance to predict diverse properties of RAC

Optimal seismic retrofitting of existing RC frames through soft-computing approaches

2016 - 2017Ph.D. Thesis proposes a Soft-Computing approach capable of supporting the engineer judgement in the selection and design of the cheapest solution for seismic retrofitting of existing RC framed structure. Chapter 1 points out the need for strengthening the existing buildings as one of the main way of decreasing economic and life losses as direct consequences of earthquake disasters. Moreover, it proposes a wide, but not-exhaustive, list of the most frequently observed deficiencies contributing to the vulnerability of concrete buildings. Chapter 2 collects the state of practice on seismic analysis methods for the assessment the safety of the existing buildings within the framework of a performancebased design. The most common approaches for modeling the material plasticity in the frame non-linear analysis are also reviewed. Chapter 3 presents a wide state of practice on the retrofitting strategies, intended as preventive measures aimed at mitigating the effect of a future earthquake by a) decreasing the seismic hazard demands; b) improving the dynamic characteristics supplied to the existing building. The chapter presents also a list of retrofitting systems, intended as technical interventions commonly classified into local intervention (also known “member-level” techniques) and global intervention (also called “structure-level” techniques) that might be used in synergistic combination to achieve the adopted strategy. In particular, the available approaches and the common criteria, respectively for selecting an optimum retrofit strategy and an optimal system are discussed. Chapter 4 highlights the usefulness of the Soft-Computing methods as efficient tools for providing “objective” answer in reasonable time for complex situation governed by approximation and imprecision. In particular, Chapter 4 collects the applications found in the scientific literature for Fuzzy Logic, Artificial Neural Network and Evolutionary Computing in the fields of structural and earthquake engineering with a taxonomic classification of the problems in modeling, simulation and optimization. Chapter 5 “translates” the search for the cheapest retrofitting system into a constrained optimization problem. To this end, the chapter includes a formulation of a novel procedure that assembles a numerical model for seismic assessment of framed structures within a Soft-Computing-driven optimization algorithm capable to minimize the objective function defined as the total initial cost of intervention. The main components required to assemble the procedure are described in the chapter: the optimization algorithm (Genetic Algorithm); the simulation framework (OpenSees); and the software environment (Matlab). Chapter 6 describes step-by-step the flow-chart of the proposed procedure and it focuses on the main implementation aspects and working details, ranging from a clever initialization of the population of candidate solutions up to a proposal of tuning procedure for the genetic parameters. Chapter 7 discusses numerical examples, where the Soft-Computing procedure is applied to the model of multi-storey RC frames obtained through simulated design. A total of fifteen “scenarios” are studied in order to assess its “robustness” to changes in input data. Finally, Chapter 8, on the base of the outcomes observed, summarizes the capabilities of the proposed procedure, yet highlighting its “limitations” at the current state of development. Some possible modifications are discussed to enhance its efficiency and completeness. [edited by author]XVI n.s

Advances in Plastic Forming of Metals

The forming of metals through plastic deformation comprises a family of methods that produce components through the re-shaping of input stock, oftentimes with little waste. Therefore, forming is one of the most efficient and economical manufacturing process families available. A myriad of forming processes exist in this family. In conjunction with their countless existing successful applications and their relatively low energy requirements, these processes are an indispensable part of our future. However, despite the vast accumulated know-how, research challenges remain, be they related to the forming of new materials (e.g., for light-weight transportation applications), pushing the boundaries of what is doable, reducing the intermediate steps and/or scrap, or further enhancing the environmental friendliness. The purpose of this book is to collect expert views and contributions on the current state-of-the-art of plastic forming, thus highlighting contemporary challenges and offering ideas and solutions

Trends and Prospects in Geotechnics

The Special Issue book presents some works considered innovative in the field of geotechnics and whose practical application may occur in the near future. This collection of twelve papers, in addition to their scientific merit, addresses some of the current and future challenges in geotechnics. The published papers cover a wide range of emerging topics with a specific focus on the research, design, construction, and performance of geotechnical works. These works are expected to inspire the development of geotechnics, contributing to the future construction of more resilient and sustainable geotechnical structures