CivilEng, Volume 1, Issue 2 (September 2020) – 6 articles

The valorization and reusing of mining waste has been widely studied in recent years. Research has demonstrated that there is great potential for reusing mining waste for construction applications. This work experimentally investigated the strength development, pore structure, and microstructure of a binary alkali-activated binder. This is based on tungsten mining waste mud (TMWM) and electric-arc-furnace slag (EAF-Slag) using different proportions of TMWM (10, 20, 30, 40, and 50 vt.%). The precursors were activated using sodium silicate (Na₂SiO₃) and potassium hydroxide (KOH 8M) as alkaline activator solution with solid:liquid weight ratio = 3. Pastes were used to assess the compressive strength of the blended binder and their microstructure. The reaction products were characterized by X-ray diffraction (XRD), scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDS), and Fourier transform infra-red (FT-IR) spectroscopy, while the porosity and the pores size properties were examined by mercury intrusion porosimetry (MIP). The results show that the partial replacement of TMWM with EAF-Slag exhibited better mechanical properties than the 100TM-AAB. A maximum strength value of 20.1 MPa was obtained in the binary-AAB sample prepared with 50 vt.% TMWM and EAF-Slag. The pastes that contained a higher dosage of EAF-Slag became more compact with lower porosity and finer pore-size distribution. In addition, the results obtained by SEM-EDS confirmed the formation of different types of reaction products in the 100TM-AAB, 100FS-AAB, and the binary-AABs mixtures such as N-A-S-H, C-A-S-H and (N, C)-A-S-H gels frameworks in the system as the major elements detected are Si, Al, Ca, and Na. Full article

For an effective risk management of complex buildings it is required to dynamically estimate the risk on the service and take proper responsive measures to contrast it. This implies being able to estimate the evolving probabilities of failures over time and the way their occurrence is trust in affecting the service. This is now possible thanks to the advent of new sensing technologies and data-driven models to estimate failure probabilities, as well as solid risk management methodologies to estimate their effect on the service. However, it needs to be considered that the implementation of a dynamic risk management in standard building operation has to consider the reconfiguration of some processes to include the use of enabling technologies. In this paper a new dynamic risk management methodology is proposed to consistently (i) model the service, estimate the risk, first (ii) statically, using fault tree analysis, and then (iii) dynamically, using sensing technologies for data gathering and data-driven models for dynamic probability estimate, and finally (iv) implement the required intervention measures to minimize the risk. Then an application of the methodology is presented, for the risk management of an air handling unit, using a convolutional neural network, and its outcomes discussed. Conclusions are also drawn on the implications of integrating such a methodology in the current whole building risk management process and several outlooks are proposed. Full article

Railway managers identify and prioritize assets for risk-reducing interventions. This requires the estimation of risks due to failures, as well as the estimation of costs and effects due to interventions. This, in turn, requires the estimation of values of numerous input variables. As there is uncertainty related to the initial input estimates, there is uncertainty in the output, i.e., assets to be prioritized for risk-reducing interventions. Consequently, managers are confronted with two questions: Do the uncertainties in inputs cause significant uncertainty in the output? If so, where should efforts be concentrated to quantify them? This paper discusses the identification of input uncertainties that are likely to affect railway asset prioritization for risk-reducing interventions. Once the track sections, switches and bridges of a part of the Irish railway network were prioritized using best estimates of inputs, they were again prioritized using: (1) reasonably low and high estimates, and (2) Monte Carlo sampling from skewed normal distributions, where the low and high estimates encompass the 95% confidence interval. The results show that only uncertainty in a few inputs influences the prioritization of the assets for risk-reducing interventions. Reliable prioritization of assets can be achieved by quantifying the uncertainties in these particular inputs. Full article

Asset integrity and predictive maintenance models require field data for an accurate assessment of an asset’s condition. Historically these data collected periodically in the field by technicians using portable units. The significant investment in inexpensive microelectromechanical (MEMS) sensors mounted on untethered (energy-harvesting or battery-powered) microprocessors communicating wirelessly to the cloud is expected to change the way we collect asset health data. Permanently installed MEMS-based sensing units will enable near-real time data collection and reduce the safety exposure of technicians by eliminating the need to manually collect field data. With hundreds of MEMS-based sensing units expected to be installed at a single site it is vital to assure the data they produce and maintain them cost effectively. An asset management framework for validation of MEMS-based sensing units for condition monitoring and structural integrity (CM&SI) applications is proposed. An integral part of this framework is the proposed use of soft sensor models to replace technician inspections in the field. Soft sensor models are used in the process industry to stabilize product quality and process operations but there are few examples in asset management applications. The contributions of this paper are twofold. Firstly, we use an interdisciplinary approach drawing on electronics, process control, statistics, machine learning, and asset management fields to describe the emerging field of permanently installed MEMS-based sensing units for CM&SI. Secondly, we development a framework for assuring validation of the data these sensing units generate. Full article

Transmission of the prestressing force to concrete by prestressing tendons is a topic of discussion within the fib Task Group 2.5: Bond and Material Models. Particularly, the extensive use of pretensioned prestressed-concrete (PC) requires adequate knowledge of bond development at the steel–concrete interface after prestress release. The transmission length, representing the distance from the free-end of the beam necessary to transmit the fully effective prestressing-force to the surrounding concrete, is a design parameter of paramount importance for PC members detailing. This contribution presents the analytical modelling of the transmission length based on the thick-walled cylinders (TWC) theory, considering anisotropic behaviour of the concrete. To derive the optimal friction coefficient between steel and concrete, the theoretical model has been calibrated according to an experimental database of transmission lengths collected from the literature, encompassing 130 data points from 7 different campaigns. Additionally, local behaviour has been analysed by assessing radial cracking and bond stress development along the transmission length. Full article

The design and evaluation of structures subjected to blast loads has increased steadily since the 11 September 2001 terrorist attacks. While shock tube testing has filled some of the data gap by replicating blast waves in a controlled fashion, there is only scant field explosion data that is easily accessible for the structural engineering community for hypothesis testing or model validation. This paper summarizes experimental design, pre-test sensor verification, and data collection from 10 reinforced concrete slabs subjected to field explosions using a modest budget. The experimental record contains pressure, displacement, and acceleration measurements of each slab except in a few cases where the sensors have failed. The data is archived at George Mason Dataverse. Following detailed description of the experimental record for each slab, an example is provided in which the data can be utilized for finite element model verification. Full article