Abstract

Bed joint reinforced repointing is a strengthening method often used in the Netherlands to counteract settlement damage. This strengthening technique consists of cutting a slot in the mortar joint and installing twisted steel bars embedded in a high-strength repair mortar. Due to the increase in seismic activities, triggered by gas extraction in the region of Groningen (northern part of the Netherlands), it is of interest to investigate whether this strengthening technique is efficient against seismic load. In order to characterize the performance of the bed joint reinforced repointing using twisted steel bars, an experimental campaign was conducted at Delft University of Technology. A quasi-static cyclic in-plane test on a full-scale wall was performed; similar tests on unstrengthened specimens were available from a previous experimental campaign [1][2] and were used for comparison. Moreover, small scale pull-out tests were performed to study the interaction between the steel bars and the repair mortar. By comparing the response of unstrengthened and strengthened masonry specimens, it is observed that the use of bed joint reinforced repointing can provide an increase in terms of ductility and displacement capacity, but not in terms of force capacity. Regarding the serviceability limit state, a reduction in crack width and an increase of load at onset of cracking were observed. The preliminary information obtained for the presented case study provides the ground for futher research as well as benchmark for numerical modelling.

Abstract

Obtaining soil parameters through laboratory tests and solving the governing equations that describe soil settlement can be time-consuming, making immediate on-site predictions of soil settlement challenging. In-situ testing provides a more efficient approach to obtain soil parameters than laboratory tests. Data from the Piezocone penetration test (CPTu) can be used for on-the-spot interpretation of soil mechanical parameters, which can then be incorporated into the governing equations for soil settlement calculation. Physics Informed Neural Networks (PINNs) algorithm uses automatic differentiation method to directly embed partial differential equations (PDEs) into a deep learning neural network and provides solution for these PDEs in a cost-effective manner compared to traditional numerical methods. In this paper, a framework integrating data from CPTu and PINNs to predict soil settlement is proposed and evaluated through comparison with numerical simulations from Finite Element Methods (FEMs). Results show that the framework gave a reasonably good agreement with the FEMs benchmark while substantially reduced the computation time. This method allows for immediate on-site prediction of soil settlement during site investigations, thus better guiding surveying and construction activities.

Abstract

Future static settlement of a former aggregate mining quarry site in Irwindale, California is predicted based on the monitoring data and using numerical analyses that consider the total settlement of the deepest portion of the rockfill. The portion of quarry analyzed was backfilled with approximately 61 to 67 meters, has a volume of 2.6 million m3, and covers an area of 114,900 m2. The backfill material ranges from silty sand (SM) to silty gravel (GM) and was compacted to achieve an average relative compaction (RC) of 96%. The deepest fill area is currently being monitored with survey data to predict future 50-year settlement due to secondary compression. Settlement monument data from three different locations has been collected and made available starting from May 2009 up to now. A 3-dimensional model of the rockfill was used to determine an appropriate upper and lower bound of predicted future 50-year settlement from today in 13 stages and scenarios. The modified secondary compression index was estimated along with the modified compression index that was back calculated to match the field monitoring data. We could find good agreement between the field monitoring data and our numerical model when considering total settlement of the rockfill.